1. A distance fuzzy suppression method based on a particle swarm algorithm and a projection method is characterized by comprising the following steps:

obtaining a mask corresponding to each particle sample of the sample group in the iteration;

determining a receiving weight and a receiving direction graph of each corresponding particle sample by applying a projection method according to a preset radar signal parameter value and a mask corresponding to each particle sample in the iteration;

according to the receiving direction graph of each particle sample, determining the fitness of the corresponding particle sample;

sorting the particle samples according to the fitness of each particle sample, and determining the optimal particle sample of the iteration;

and under the condition that the fitness of the optimal particle sample of the iteration does not meet the specific condition, updating the sample group, carrying out a new iteration, and repeating the iteration until the fitness of the optimal particle sample meets the specific condition.

2. The method according to claim 1, wherein the determining the receiving weight and the receiving direction graph of the corresponding particle sample by applying a projection method according to the preset radar signal parameter value and the mask corresponding to each particle sample in the current iteration comprises:

according to a preset radar signal parameter value, receiving direction graphs of each particle sample in the previous iteration and a mask corresponding to each particle sample in the current iteration, determining a weight difference value of the corresponding particle sample in the current iteration;

determining a receiving weight value of a corresponding particle sample in the iteration according to the preset radar signal parameter value, the receiving weight value corresponding to each particle sample in the previous iteration and the weight value difference value of each particle sample in the iteration;

and determining a receiving directional diagram of the corresponding particle sample in the iteration according to the preset radar signal parameter value and the receiving weight of each particle sample in the iteration.

3. The method of claim 2, wherein determining the weight difference value of the corresponding particle sample in the current iteration according to a preset radar signal parameter value, a receiving direction graph of each particle sample in the previous iteration, and a mask corresponding to each particle sample in the current iteration comprises:

determining a directional diagram difference value of a corresponding particle sample in the iteration according to a receiving directional diagram of each particle sample in the previous iteration and a mask corresponding to each particle sample in the iteration;

and determining the weight difference value of the corresponding particle sample in the iteration according to a preset radar signal parameter value and the directional diagram difference value of each particle sample in the iteration.

4. The method according to claim 2, wherein the determining, according to the preset radar signal parameter value and the receiving weight of each particle sample in the current iteration, a receiving direction diagram of a corresponding particle sample in the current iteration includes:

determining a transfer relation matrix according to a preset radar signal parameter value;

and determining a receiving directional diagram corresponding to the particle sample in the iteration according to the transfer relation matrix and the receiving weight of each particle sample in the iteration.

5. The method of claim 4, wherein the preset radar signal parameter values comprise a down-angle sampling sequence, a wave number and coordinates of each array element of the radar in a reference coordinate system, and the determining the transfer relation matrix according to the preset radar signal parameter values comprises:

acquiring a lower view sampling sequence; the lower visual angle sampling sequence comprises coordinates of a plurality of lower visual angles in a far-field spherical coordinate system;

converting a far-field spherical coordinate system into a rectangular coordinate system according to the lower visual angle sampling sequence and the wave number, and determining the coordinate of each lower visual angle in the rectangular coordinate system;

determining a far field directional diagram of each array element in a plurality of array elements of the radar according to the coordinate of each lower visual angle in the rectangular coordinate system;

acquiring the coordinates of each array element in a reference coordinate system;

and determining a transfer relation matrix according to the far field directional diagram of each array element and the coordinates of the corresponding array element in the reference coordinate system.

6. The method of claim 3, wherein the mask comprises a first mask and a second mask, and the determining the pattern difference of the corresponding particle sample in the current iteration according to the receiving pattern of each particle sample in the previous iteration and the mask corresponding to each particle sample in the current iteration comprises:

determining the maximum value of the receiving directional diagram of the corresponding particle sample in the last iteration according to the receiving directional diagram of each particle sample in the last iteration;

normalizing the receiving directional diagram of each particle sample in the last iteration to obtain a normalized directional diagram of the receiving directional diagram of the corresponding particle sample in the last iteration;

determining a partial directional diagram of a receiving directional diagram of a corresponding particle sample in the previous iteration according to the first mask, the second mask in the current iteration and the normalized directional diagram of each particle sample in the previous iteration;

and determining the directional diagram difference value of the corresponding particle sample in the iteration according to the maximum value, the normalized directional diagram and the partial directional diagram of the receiving directional diagram of each particle sample in the last iteration.

7. The method according to claim 1, wherein the determining the fitness of the corresponding particle sample according to the receiving direction graph of each particle sample in the current iteration comprises:

determining the measurement index of the corresponding particle sample in the iteration according to the receiving direction graph of each particle sample in the iteration;

and determining the fitness of the corresponding particle sample according to the measurement index of each particle sample and a preset fitness function in the iteration.

8. The method of claim 7, wherein the measurement indicators include at least three of: range ambiguity, main lobe width and side lobe levels.

9. The method of claim 8, wherein the measurement indicator is range ambiguity, and the preset radar signal parameter values include a downward view sampling sequence, an earth radius, a speed of light, an orbital altitude of the radar, a pulse repetition frequency of the radar, a number of downward views in the downward view sampling sequence, an ambiguity region number of the radar, and an empirical parameter;

the determining the measurement index of the corresponding particle sample in the iteration according to the receiving direction graph of each particle sample in the iteration comprises the following steps:

acquiring a lower view sampling sequence; the lower visual angle sampling sequence comprises coordinates of a plurality of lower visual angles in a far-field spherical coordinate system;

determining a slope distance sequence according to the lower visual angle sampling sequence, the earth radius and the orbit height of the radar;

determining a fuzzy slope distance sequence according to the slope distance sequence, the pulse repetition frequency, the fuzzy area number and the light speed;

determining an incidence angle sequence according to the fuzzy slant distance sequence;

determining a reflectivity sequence according to the incidence angle sequence and the empirical parameters;

and determining the measurement index of the corresponding particle sample in the iteration according to the incident angle sequence, the reflectivity sequence, the fuzzy slope distance sequence and the receiving direction graph of each particle sample in the iteration.

10. A distance fuzzy suppression device based on a particle swarm algorithm and a projection method is characterized by comprising the following steps:

the acquisition module is used for acquiring a mask corresponding to each particle sample of the sample group in the iteration;

the first determining module is used for determining a receiving weight and a receiving direction graph of each corresponding particle sample by applying a projection method according to a preset radar signal parameter value and a mask corresponding to each particle sample in the iteration;

the second determining module is used for determining the fitness of the corresponding particle sample according to the receiving direction graph of each particle sample;

the third determining module is used for sequencing the particle samples according to the fitness of each particle sample and determining the optimal particle sample of the iteration;

and the updating module is used for updating the sample group and carrying out a new iteration under the condition that the fitness of the optimal particle sample of the iteration does not meet the specific condition, and repeating the iteration until the fitness of the optimal particle sample meets the specific condition.

Background

Synthetic Aperture Radar (SAR) is an active microwave imaging device, has stronger penetrability compared with an optical Radar, can realize all-time and all-weather earth observation, and has wide application in the field of remote sensing. The SAR obtains a high-resolution image by processing a broadband pulse signal and an azimuth doppler signal, and since a pitch directional pattern (also called an antenna pattern) of an antenna inevitably has side lobes, an echo outside a mapping band is also received when the echo is received, so that the final image quality is affected, and the interference is called range ambiguity. In a full polarization mode represented by a hybrid circular polarization mode, a partial distance ambiguity component becomes extremely strong, the performance of the SAR is severely limited, and a distance ambiguity suppression method must be introduced to improve the performance.

Disclosure of Invention

In view of this, embodiments of the present application provide a distance ambiguity suppression method and apparatus based on a particle swarm algorithm and a projection method.

In a first aspect, an embodiment of the present application provides a distance ambiguity suppression method based on a particle swarm algorithm and a projection method, where the method includes: obtaining a mask, a receiving weight and a receiving direction graph corresponding to each particle sample of the sample group in the last iteration; determining a receiving direction graph corresponding to each particle sample in the iteration according to a preset radar signal parameter value and a mask, a receiving weight and the receiving direction graph corresponding to each particle sample in the previous iteration; according to a receiving direction graph corresponding to each particle sample in the iteration, determining the fitness of the corresponding particle sample; sorting the particle samples according to the fitness of each particle sample, and determining the optimal particle sample of the iteration; and under the condition that the fitness of the optimal particle sample of the iteration does not meet the specific condition, updating the sample group, carrying out a new iteration, and repeating the iteration until the fitness of the optimal particle sample meets the specific condition.

In a second aspect, an embodiment of the present application provides a distance ambiguity suppression apparatus based on a particle swarm algorithm and a projection method, including: the acquisition module is used for acquiring a mask, a receiving weight and a receiving direction graph corresponding to each particle sample of the sample group in the last iteration; the first determining module is used for determining a receiving direction graph corresponding to each particle sample in the iteration according to a preset radar signal parameter value and a mask, a receiving weight and the receiving direction graph corresponding to each particle sample in the previous iteration; the second determining module is used for determining the fitness of the corresponding particle sample according to the receiving direction graph corresponding to each particle sample in the iteration; the third determining module is used for sequencing the particle samples according to the fitness of each particle sample and determining the optimal particle sample of the iteration; and the updating module is used for updating the sample group and carrying out a new iteration under the condition that the fitness of the optimal particle sample of the iteration does not meet the specific condition, and repeating the iteration until the fitness of the optimal particle sample meets the specific condition.

In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, where the memory stores a computer program that is executable on the processor, and the processor implements, when executing the computer program, the steps in any distance ambiguity suppression method based on a particle swarm algorithm and a projection method in the embodiments of the present application.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in any one of the distance ambiguity suppression methods based on the particle swarm optimization and the projection method in the embodiments of the present application.

In the embodiment of the application, the receiving direction graph corresponding to the particle sample in the iteration is determined according to the mask, the receiving weight and the receiving direction graph of the particle sample in the previous iteration and the preset radar signal parameter value, and the fitness of the particle sample is determined according to the receiving direction graph of the particle sample in the iteration; therefore, the optimal particle sample of the iteration can be determined from the multiple particle samples according to the fitness of the particle sample, the sample group is updated according to the fitness of the optimal particle sample of the iteration, a new iteration is performed, the mask of the particle sample is updated, the fitness of the particle sample is updated, the optimal particle sample is determined from the updated particle sample according to the updated fitness of the particle sample until the fitness of the optimal particle sample meets a specific condition, and therefore the particle swarm algorithm can be adopted for iterative optimization.

Drawings

Fig. 1 is a schematic flowchart of a distance ambiguity suppression method based on a particle swarm optimization and a projection method according to an embodiment of the present application;

fig. 2 is an algorithm schematic diagram of a distance ambiguity suppression method based on a particle swarm algorithm and a projection method according to an embodiment of the present application;

fig. 3 is a schematic flowchart of a distance ambiguity suppression method for hybrid circular polarization based on a particle swarm algorithm and a projection method according to an embodiment of the present application;

FIG. 4 is a diagram illustrating receive patterns before and after optimization according to an embodiment of the present application;

FIG. 5 is a diagram illustrating reception graph weight magnitudes before and after optimization according to an embodiment of the present application;

FIG. 6 is a schematic illustration of distance blur levels before and after optimization according to an embodiment of the present application;

FIG. 7 is a schematic diagram of receive patterns before and after optimization according to another embodiment of the present application;

FIG. 8 is a diagram of receive graph weight magnitudes before and after optimization according to another embodiment of the present application;

FIG. 9 is a schematic illustration of distance blur levels before and after optimization according to another embodiment of the present application;

fig. 10 is a schematic diagram of a method for generating an antenna direction diagram according to an embodiment of the present disclosure;

fig. 11 is a schematic structural diagram of a distance ambiguity suppression apparatus based on a particle swarm algorithm and a projection method according to an embodiment of the present application;

fig. 12 is a hardware entity diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solution of the present application is further elaborated below with reference to the drawings and the embodiments.

Fig. 1 is a schematic flow chart of a distance ambiguity suppression method based on a particle swarm optimization and a projection method according to an embodiment of the present application, as shown in fig. 1, the method includes:

step 102: obtaining a mask corresponding to each particle sample of the sample group in the iteration;

the sample group can also be called a particle group, the particle group is composed of a plurality of particle samples, and each sample particle is a vector; in one embodiment, the population of particles may consist of N particle samples, the particle sample S_nCan be expressed by the following formula (1):

it should be noted that, according to a rule designed in advance, the following formula (2) may be mapped for each particle sample to obtain a mask M corresponding to each particle sample_U,M_L：

Wherein M is_UAnd M_LRespectively, an upper mask and a lower mask, which may also be referred to as a first mask and a second mask.

Step 104: determining a receiving weight and a receiving direction graph of each corresponding particle sample by applying a projection method according to a preset radar signal parameter value and a mask corresponding to each particle sample in the iteration;

the preset radar signal parameter value is a predetermined and stored radar signal parameter value, and the radar signal parameter value is irrelevant to the particle sample; the radar signal parameter values can be divided into target wave position signal parameters, SAR system parameters, user-defined parameters and the like.

The target wave position signal parameters may include a scanning range, a pulse width, a PRF (pulse repetition frequency), a transmission weight, a transmission directional diagram, a polarization mode, and the like of the selected wave position; the target wave position signal parameter is related to the SAR and varies with the wave position.

The SAR system parameters can comprise the central frequency, the elevation array element number, the array element spacing, the antenna installation angle, the unit directional diagram and the like of the SAR system; the SAR system parameters are related to SAR and do not change with wave position.

The user-defined parameters comprise the number of particle samples, values of l and m, maximum iteration times and the like; the self-defined parameters are not related to SAR and do not change along with wave position.

In the first iteration, the receiving weight of each particle sample can be 1, and in the subsequent iteration process, the receiving weight of each particle sample can be continuously updated; an antenna pattern, also called a radiation pattern or a far-field pattern, may refer to a pattern in which the relative field strength (normalized mode value) of a radiation field changes with direction at a certain distance from an antenna of a radar, and is usually represented by two mutually perpendicular plane patterns in the maximum radiation direction of the antenna; the antenna pattern includes a transmit pattern and a receive pattern.

Step 106: according to the receiving direction graph of each particle sample, determining the fitness of the corresponding particle sample;

it should be noted that the fitness includes, but is not limited to, a degree of suppression for characterizing the range ambiguity of the reception direction map; under the condition that the emission directional diagram of the radar is known, determining a two-way directional diagram of the radar according to the determined receiving directional diagram, and in one embodiment, determining the fitness of the corresponding particle sample according to the two-way directional diagram of each particle sample; in another embodiment, the fitness of the corresponding particle sample may also be determined according to a preset radar signal parameter value and the two-way directional diagram of each particle sample.

Step 108: sorting the particle samples according to the fitness of each particle sample, and determining the optimal particle sample of the iteration;

the particle samples can be sorted according to the difference between the fitness of the particle samples and the preset reference fitness, the particle sample corresponding to the fitness closest to the preset reference fitness is determined as the optimal particle sample of the current iteration, and the particle sample corresponding to the maximum or minimum fitness can also be determined as the optimal particle sample of the current iteration according to the size relationship between the fitness of the particle samples; the particle samples can be ranked according to the fitness of the particle samples and a preset scoring standard, and the particle sample with the highest score is determined as the optimal particle sample for the iteration; in addition, the scoring criteria can be flexibly adjusted to effectively screen the optimal result (the optimal particle sample of the current iteration).

Step 110: and under the condition that the fitness of the optimal particle sample of the iteration does not meet the specific condition, updating the sample group, carrying out a new iteration, and repeating the iteration until the fitness of the optimal particle sample meets the specific condition.

The specific condition may be that the fitness of the optimal particle sample in the current iteration is within a preset fitness range, the specific condition may also be that a difference between the fitness of the optimal particle sample determined in the current iteration and the fitness of the optimal particle sample determined in the previous iteration is within a preset difference range, and the specific condition may also be that the iteration number reaches a preset iteration number threshold (i.e., the maximum iteration number).

Fig. 2 is an algorithm schematic diagram of a distance ambiguity suppression method based on a particle swarm algorithm and a projection method provided in an embodiment of the present application, and referring to fig. 2, a sample group may be initialized and updated by using the particle swarm algorithm.

In the first iteration, a sample group may be initialized, and an initial particle sample s may be randomly generated according to a given number N of particle samples₁,…,s_N(i.e., particle 1 to particle N), each particle sample can then be mapped into a mask (mask 1 to mask N) according to a pre-designed rule, each mask including an upper mask M_UAnd a lower mask M_L(ii) a Determining a receiving direction diagram corresponding to each particle sample according to a preset radar signal parameter value and an initial mask and an initial receiving weight corresponding to each particle sample by using a projection method; determining the initial fitness of the corresponding particle sample by utilizing a fitness function according to the receiving direction graph corresponding to each particle sample; the particle samples may be ranked according to their initial fitness to determine an optimal particle sample in a first iteration from the N particle samples, where the optimal particle sample in the first iteration may also be referred to as an optimal particle sample in the current iteration.

When the fitness of the optimal particle sample of the current iteration does not meet the specific condition, the sample group may be updated by using the fitness of the optimal particle sample of the current iteration, and a second iteration may be performed, where it is to be noted that updating the sample group in the first iteration process may generate an updated particle sample s₁,…,s_NAt this time, each updated particle sample may still be mapped into a mask according to a preset design rule, at this time, since the particle sample is updated, the mask corresponding to the particle sample is also updated correspondingly, and the mask after the particle sample is updated is the mask corresponding to the particle sample in the second iteration.

In the second iteration process, determining a receiving direction diagram of the corresponding particle sample in the second iteration according to a preset radar signal parameter value, a receiving direction diagram and an initial receiving weight value which correspond to each particle sample in the first iteration and a mask which corresponds to the particle sample in the second iteration by using a projection algorithm; determining the fitness of the corresponding particle sample by using a fitness function; the particle samples can be ranked according to their fitness to determine the optimal particle sample from the N particle samples.

Under the condition that the fitness of the optimal particle sample does not meet a specific condition, iteration can be continued until the fitness of the optimal particle sample meets the specific condition, and then the iteration is stopped.

In the embodiment of the application, a projection method is applied to determine the receiving weight and the receiving direction graph of the corresponding particle sample according to the mask of the particle sample in the iteration and the preset radar signal parameter value, and the fitness of the particle sample is determined according to the receiving direction graph of the particle sample; therefore, the optimal particle sample of the iteration can be determined from the multiple particle samples according to the fitness of the particle sample, the sample group is updated according to the fitness of the optimal particle sample of the iteration, a new iteration is performed, the mask of the particle sample is updated, the fitness of the particle sample is updated, the optimal particle sample is determined from the updated particle sample according to the updated fitness of the particle sample until the fitness of the optimal particle sample meets a specific condition, and therefore the particle swarm algorithm can be adopted for iterative optimization.

The embodiment of the application further provides a distance fuzzy suppression method based on a particle swarm algorithm and a projection method, and the method comprises the following steps:

step S202: obtaining a mask corresponding to each particle sample of the sample group in the iteration;

wherein, assuming that the iteration is the (i +1) th iteration, the mask corresponding to the particle sample can be represented as M_U ⁽ⁱ⁺¹⁾And M_L ⁽ⁱ⁺¹⁾The receiving weight corresponding to the particle sample can be represented as A⁽ⁱ⁺¹⁾The receiving direction graph corresponding to the particle sample can be represented as F⁽ⁱ⁺¹⁾。

Step S204: determining a weight difference value of each corresponding particle sample in the iteration according to a preset radar signal parameter value and a mask corresponding to each particle sample in the iteration and a receiving direction graph of each particle sample in the last iteration;

the weight difference of the corresponding particle sample in the current iteration may be represented as Δ a (i +1), and the receiving directional diagram of the particle sample in the last iteration may be represented as F⁽ⁱ⁾。

Step S206: determining a receiving weight value of a corresponding particle sample in the iteration according to the preset radar signal parameter value, the weight difference value of each particle sample in the iteration, and the receiving weight value of each particle sample in the previous iteration;

in the last iteration, the receiving weight of each particle sample may be represented as a⁽ⁱ⁾The receiving weight of the corresponding particle sample in this iteration can be represented as A⁽ⁱ⁺¹⁾。

Step S208: determining a receiving direction diagram corresponding to the corresponding particle sample in the iteration according to the preset radar signal parameter value and the receiving weight of each particle sample in the iteration;

step S210: according to a receiving direction graph corresponding to each particle sample in the iteration, determining the fitness of the corresponding particle sample;

step S212: sorting the particle samples according to the fitness of each particle sample, and determining the optimal particle sample of the iteration;

step S214: and under the condition that the fitness of the optimal particle sample of the iteration does not meet the specific condition, updating the sample group, carrying out a new iteration, and repeating the iteration until the fitness of the optimal particle sample meets the specific condition.

In the embodiment of the application, the weight difference value of the particle sample is determined, the receiving weight value of the particle sample is updated according to the weight difference value to obtain the updated receiving weight value, and the receiving directional diagram of the particle sample is updated by using the updated receiving weight value, so that the fitness of the particle sample can be updated more accurately and continuously until the fitness of the optimal particle sample meets a specific condition.

The embodiment of the application further provides a distance fuzzy suppression method based on a particle swarm algorithm and a projection method, and the method comprises the following steps:

step S302: acquiring a receiving weight and a receiving direction graph of each particle sample of the sample group in the last iteration and a mask corresponding to each particle sample in the current iteration;

step S304: determining a directional diagram difference value of a corresponding particle sample in the iteration according to a receiving directional diagram of each particle sample in the previous iteration and a mask corresponding to each particle sample in the iteration;

the pattern difference of the corresponding particle sample in this iteration may be represented as Δ F (i + 1).

Step S306: determining a weight difference value of a corresponding particle sample in the previous iteration according to a preset radar signal parameter value and a directional diagram difference value of each particle sample in the current iteration;

step S308: determining a receiving weight value of a corresponding particle sample in the iteration according to the preset radar signal parameter value, the receiving weight value corresponding to each particle sample in the previous iteration and the weight value difference value of each particle sample in the iteration;

step S310: determining a transfer relation matrix according to a preset radar signal parameter value;

wherein the transfer relation matrix may be represented as T.

Step S312: determining a receiving direction graph corresponding to the particle sample in the iteration according to the transfer relation matrix and the receiving weight of each particle sample in the iteration;

step S314: according to the receiving direction graph of each particle sample in the iteration, determining the fitness of the corresponding particle sample;

step S316: sorting the particle samples according to the fitness of each particle sample, and determining the optimal particle sample of the iteration;

step S318: and under the condition that the fitness of the optimal particle sample of the iteration does not meet the specific condition, updating the sample group, carrying out a new iteration, and repeating the iteration until the fitness of the optimal particle sample meets the specific condition.

In the embodiment of the application, the weight difference value of the particle sample is calculated by firstly calculating the directional diagram difference value of the particle sample and then calculating the weight difference value of the particle sample according to the directional diagram difference value of the particle sample, so that the weight difference value of the particle sample can be calculated more accurately; in addition, a transfer relation matrix is determined according to preset radar signal parameter values, and then a receiving directional diagram of the particle sample is determined according to the transfer relation matrix and the receiving weight of the particle sample, so that the determined receiving directional diagram of the particle sample is more accurate.

The embodiment of the application further provides a distance fuzzy suppression method based on a particle swarm algorithm and a projection method, and the method comprises the following steps:

step S402: acquiring a receiving weight and a receiving direction graph of each particle sample of the sample group in the last iteration and a mask corresponding to each particle sample in the current iteration;

wherein the mask comprises a first mask and a second mask; the first mask may be denoted as M_UThe second mask may be denoted as M_LIn the last iteration, the receiving weight corresponding to the particle sample can be represented as a⁽ⁱ⁾The receiving direction graph corresponding to the particle sample can be represented as F⁽ⁱ⁾。

Step S404: determining the maximum value of a receiving directional diagram corresponding to each particle sample in the previous iteration according to the receiving directional diagram corresponding to each particle sample in the previous iteration;

wherein, the maximum value of the receiving directional diagram corresponding to the corresponding particle sample in the last iteration can be represented as maxF.

Step S406: normalizing the receiving direction graph corresponding to each particle sample in the last iteration to obtain a normalized direction graph of the receiving direction graph corresponding to the corresponding particle sample in the last iteration;

wherein, the normalized directional diagram of the receiving directional diagram corresponding to the corresponding particle sample in the last iteration can be represented as F_n。

Step S408: determining a part of directional diagrams of receiving directional diagrams corresponding to the particle samples in the previous iteration according to the first mask, the second mask in the current iteration and the normalized directional diagrams of each particle sample in the previous iteration;

wherein, the partial directional diagram of the receiving directional diagram corresponding to the corresponding particle sample in the last iteration can be represented as P_M{|F_nAnd | j, the partial directional diagram corresponding to the particle sample can be expressed by equation (3):

step S410: determining the directional diagram difference value of the corresponding particle sample in the iteration of the time according to the maximum value, the normalized directional diagram and the partial directional diagram of the receiving directional diagram corresponding to each particle sample in the iteration of the time;

wherein, assuming that the directional diagram difference of the corresponding particle sample in the current iteration is represented as Δ F, the directional diagram difference corresponding to the particle sample can be represented by formula (4):

ΔF＝max F(|F_n|-P_M{|F_n|})(F_n/|F_n|) (4)；

wherein, | F_nI denotes F_nAbsolute value of (a).

Step S412: acquiring a lower view sampling sequence; the lower visual angle sampling sequence comprises coordinates of a plurality of lower visual angles in a far-field spherical coordinate system;

wherein, in the reference coordinate system, can be obtained according toThe variation range of the downward angle of view of the radar is determined according to the obtained target wave position working instruction, and the variation range of the downward angle of view can be expressed as [ alpha ]_min,α_max]N-point uniform sampling can be carried out on the lower visual angle within the variation range of the lower visual angle to obtain a lower visual angle sampling sequence alpha₁₀,…,α_N0。

Step S414: converting a far-field spherical coordinate system into a rectangular coordinate system according to the lower visual angle sampling sequence and the wave number, and determining the coordinate of each lower visual angle in the rectangular coordinate system;

where the wavenumber is equal to the real frequency divided by the speed of light, i.e. the reciprocal of the wavelength (λ), and the wavenumber can be given by k₀Represents; the far-field spherical coordinate system may be converted as shown in the following equations (5) and (6):

u＝k₀sinθcosφ (5)；

v＝k₀sinθsinφ (6)；

wherein the coordinate (u) of the lower view in the rectangular coordinate system in the sequence of lower view samples can be determined according to the above equation (5) and equation (6)_ij,v_ij)。

Step S416: determining a far field directional diagram of each array element in a plurality of array elements of the radar according to the coordinate of each lower visual angle in the rectangular coordinate system;

wherein, E may be used_k(u_ij,v_ij) Showing the far field pattern of the kth array element.

Step S418: acquiring the coordinates of each array element in a reference coordinate system;

wherein (x) can be used_k,y_k) Indicating the coordinates of the kth array element in the reference coordinate system.

Step S420: determining a transfer relation matrix according to the far field directional diagram of each array element and the coordinates of the corresponding array element in a reference coordinate system;

wherein, assuming that the transfer relationship matrix is a T matrix, the transfer relationship matrix can be represented by the following formula (7):

further, assuming that the transfer relationship matrix is denoted as T (i, j), the transfer relationship matrix can be expressed by the following formula (8):

step S422: determining a weight difference value of the corresponding particle sample in the iteration according to the transfer relation matrix and the directional diagram difference value of each particle sample in the iteration;

the weight difference Δ a of each particle sample in the iteration may be represented by formula (9):

ΔA＝(T^HT)^-1T^HΔF (9)；

wherein, T^HA conjugate transpose matrix representing a T matrix, (T)^HT)^-1Represents (T)^HT).

Step S424: determining a receiving weight value of a corresponding particle sample in the iteration according to the step length, the weight value difference value of each particle sample in the iteration and the receiving weight value of each particle sample in the previous iteration;

wherein, assume step size C₁Then, the receiving weight A of the corresponding particle sample in the current iteration⁽ⁱ⁺¹⁾Can be expressed by equation (10) as:

A⁽ⁱ⁺¹⁾＝A⁽ⁱ⁾+C₁ΔA (10)；

step S426: determining a receiving direction graph corresponding to the particle sample in the iteration according to the transfer relation matrix and the receiving weight of each particle sample in the iteration;

in this iteration, the receiving direction graph corresponding to the corresponding particle sample may be represented by formula (11):

F⁽ⁱ⁺¹⁾＝TA⁽ⁱ⁺¹⁾ (11)；

similarly, in the last iteration, the receiver direction graph corresponding to the corresponding particle sample can be represented by equation (12):

F⁽ⁱ⁾＝TA⁽ⁱ⁾ (12)；

step S428: determining the measurement index of the corresponding particle sample in the iteration according to the receiving direction graph corresponding to each particle sample in the iteration;

wherein the measurement indicators include at least three of: range ambiguity, main lobe width and side lobe levels; since the emission pattern of the particle sample is known, the gain of the two-way pattern of the particle sample can be determined according to the gain of the emission pattern and the gain of the reception pattern of the particle sample, and further the measurement index corresponding to the particle sample can be determined according to the gain of the two-way pattern of the particle sample.

Step S430: according to the measurement index of each particle sample and a preset fitness function in the iteration, determining the fitness of the corresponding particle sample;

step S432: sorting the particle samples according to the fitness of each particle sample, and determining the optimal particle sample of the iteration;

step S434: and under the condition that the fitness of the optimal particle sample of the iteration does not meet the specific condition, updating the sample group, carrying out a new iteration, and repeating the iteration until the fitness of the optimal particle sample meets the specific condition.

In the embodiment of the application, when the transfer relation matrix is determined, the transfer relation matrix can be determined according to the far-field directional diagram of each array element and the coordinates of the corresponding array element in the reference coordinate system, so that the accuracy of the determined transfer relation matrix can be improved.

When determining the directional diagram difference, the directional diagram difference of the particle sample can be determined according to the maximum value of the receiving directional diagram, the normalized directional diagram, the first mask and the second mask, so that the directional diagram difference of the particle sample can be determined more accurately.

When the fitness of the particle sample is determined, the measurement index of the particle sample can be determined according to the receiving direction graph of the particle sample, and then the measurement index of the particle sample is input into a preset fitness function, so that the fitness data output corresponds to the fitness of the particle sample, and the fitness of the particle sample can be determined more accurately.

The embodiment of the application further provides a distance fuzzy suppression method based on a particle swarm algorithm and a projection method, and the method comprises the following steps:

step S502: acquiring a receiving weight and a receiving direction graph of each particle sample of the sample group in the last iteration and a mask corresponding to each particle sample in the current iteration;

wherein the mask includes a first mask and a second mask.

Step S504: determining the maximum value of a receiving directional diagram corresponding to each particle sample in the previous iteration according to the receiving directional diagram corresponding to each particle sample in the previous iteration;

step S506: normalizing the receiving direction graph corresponding to each particle sample in the last iteration to obtain a normalized direction graph of the receiving direction graph corresponding to the corresponding particle sample in the last iteration;

step S508: determining a part of directional diagrams of receiving directional diagrams corresponding to the particle samples in the previous iteration according to the first mask, the second mask in the current iteration and the normalized directional diagrams of each particle sample in the previous iteration;

step S510: determining the directional diagram difference value of the corresponding particle sample in the iteration of the time according to the maximum value, the normalized directional diagram and the partial directional diagram of the receiving directional diagram corresponding to each particle sample in the iteration of the time;

step S512: acquiring a lower view sampling sequence; the lower visual angle sampling sequence comprises coordinates of a plurality of lower visual angles in a far-field spherical coordinate system;

step S514: converting a far-field spherical coordinate system into a rectangular coordinate system according to the lower visual angle sampling sequence and the wave number, and determining the coordinate of each lower visual angle in the rectangular coordinate system;

step S516: determining a far field directional diagram of each array element in a plurality of array elements of the radar according to the coordinate of each lower visual angle in the rectangular coordinate system;

step S518: acquiring the coordinates of each array element in a reference coordinate system;

step S520: determining a transfer relation matrix according to the far field directional diagram of each array element and the coordinates of the corresponding array element in a reference coordinate system;

step S522: determining a weight difference value of a corresponding particle sample in the last iteration according to the transfer relation matrix and the directional diagram difference value of each particle sample in the last iteration;

step S524: determining a receiving weight value of a corresponding particle sample in the iteration according to the step length and the weight value difference value and the receiving weight value of each particle sample in the previous iteration;

step S526: determining a receiving direction graph corresponding to the particle sample in the iteration according to the transfer relation matrix and the receiving weight of each particle sample in the iteration;

step S528: determining the lobe width and the side lobe level of the corresponding particle sample in the iteration according to the receiving direction graph corresponding to each particle sample in the iteration;

step S530: acquiring a lower view sampling sequence; the lower visual angle sampling sequence comprises coordinates of a plurality of lower visual angles in a far-field spherical coordinate system;

step S532: determining a slope distance sequence according to the lower visual angle sampling sequence, the earth radius and the orbit height of the radar;

wherein the radius of the earth is assumed to be R_eAnd the track height of the radar is H, determining the pitch sequence according to the following formula (10):

wherein α represents a down-view in the down-view sequence, and R represents a slant range constituting the slant range sequence, and thus, mayDetermining corresponding slope distance according to each lower visual angle in the lower visual angle sequence, and forming a slope distance sequence R according to the determined slope distances₁₀,…,R_N0。

Step S534: determining a fuzzy slope distance sequence according to the slope distance sequence, the pulse repetition frequency, the fuzzy area number and the light speed;

wherein the fuzzy slant range sequence is a slant range R corresponding to the fuzzy area_a(ii) the sequence of (a); the pulse repetition frequency may be represented by PRF, the ambiguity region number may be represented by j, the speed of light may be represented by c, and the slope distance in the slope distance sequence may be represented by R_swIndicates, then the pitch R_aCan be expressed by the following equation (14):

according to equation (11), for the ith element R in the skew sequence_i0The slope in the corresponding fuzzy slope sequence can be recorded as R_ij(j-1, …, M) and a downward viewing angle α_ij(j＝1,…,M)。

Step S536: determining an incidence angle sequence according to the fuzzy slant distance sequence;

wherein a slope distance R from the fuzzy slope distance sequence can be determined_ijCorresponding angle of incidence η_ijAnd can be based on the angle of incidence η_ijConstituting a sequence of angles of incidence.

Step S538: determining a reflectivity sequence according to the incidence angle sequence and the empirical parameters;

wherein it is assumed that the empirical parameters include p₁，p₂，p₃，p₄，p₅And p₆Angle of incidence η_ijCorresponding reflectivity ofA reflectivity ofCan be expressed by the following formula (15):

wherein the obtained reflectivity isConstituting a reflectivity sequence.

Step S540: and determining the distance ambiguity of the corresponding particle sample in the iteration according to the incident angle sequence, the reflectivity sequence, the ambiguity slant distance sequence and the receiving direction graph corresponding to each particle sample in the iteration.

Wherein the two-way pattern of the particle sample is at alpha_ijThe gain in direction can be expressed asUnder the condition that the transmitting directional diagram of the radar is known, the gain of the two-way directional diagram of the radar can be determined according to the determined receiving directional diagram, and if the measurement index is range ambiguity and the range ambiguity is RASR, the RASR can be represented by the following formula (16):

wherein the content of the first and second substances,can be represented by the following formula (17), S_iCan be expressed by the following equation (18):

step S542: according to the measurement index of each particle sample and a preset fitness function in the iteration, determining the fitness of the corresponding particle sample;

the measurement index of each particle sample can be used as an input to call a predefined fitness function, and the fitness of the corresponding particle sample is calculated and output.

Step S544: sorting the particle samples according to the fitness of each particle sample, and determining the optimal particle sample of the iteration;

step S546: and under the condition that the fitness of the optimal particle sample of the iteration does not meet the specific condition, updating the sample group, carrying out a new iteration, and repeating the iteration until the fitness of the optimal particle sample meets the specific condition.

In the embodiment of the application, the measurement index of the corresponding particle sample is determined according to the incident angle sequence, the reflectivity sequence, the fuzzy slope distance sequence and the receiving direction graph of each particle sample, so that the accuracy of the measurement index of the particle sample can be improved.

The distance fuzzy suppression method based on antenna directional diagram synthesis searches an antenna directional diagram with nulls at corresponding positions according to distance fuzzy distribution, thereby reducing the received distance fuzzy energy level. The method does not improve the complexity of the system, and can effectively inhibit distance ambiguity under the condition of ensuring that the antenna gain, the beam width and the side lobe level meet the requirements. In addition, the application of the method does not influence the azimuth ambiguity, and the ambiguity characteristic of the SAR system can be optimized on the whole, so that the method is suitable for the ambiguity performance optimization of the full polarization mode SAR represented by a hybrid circular polarization mode.

The standard for judging the performance of the distance fuzzy suppression method is to calculate the resource occupation amount, the algorithm speed and the control capability of the direction diagram index, and simultaneously, the requirements are difficult to be considered. The existing algorithms only pay attention to one aspect, and in practical application, the existing algorithms must be chosen according to specific requirements.

The embodiment of the application designs a brand-new distance fuzzy suppression method for hybrid circular polarization based on a particle swarm algorithm and a projection method. The method can effectively optimize the distance ambiguity characteristic of the hybrid circular polarization mode SAR system, and is also suitable for other polarization modes. In addition, compared with a mask design method, the method has the advantages of wide application range and strong flexibility, and the corresponding result can be directly applied to the traditional phased array antenna, so that the method has strong engineering application value.

Fig. 3 is a distance ambiguity suppression method for hybrid circular polarization based on a particle swarm algorithm and a projection method according to an embodiment of the present application, where the method includes:

step 301: acquiring a target wave position working instruction;

step 302: determining a target wave position signal parameter according to the target wave position working instruction;

step 303: initializing measurement parameters;

wherein the measurement parameters comprise the target wave position signal parameters, SAR system parameters and self-defined parameters; the SAR system parameters can be antenna size, signal bandwidth, pulse repetition frequency, scanning wave bit number, wave bit position and the like.

Step 304: initializing or updating a sample group of the iteration of the current round according to the measurement parameters;

step 305: calculating the corresponding antenna directional pattern of each particle sample by using a projection algorithm according to the sample group and measuring indexes;

step 306: and judging to continue iteration or taking the optimal result of the iteration of the current round as the optimal weight according to the measurement index of the antenna directional diagram.

In the above embodiment, initializing or updating the sample group of the current iteration according to the measurement parameters in step 304 includes:

initializing a sample group when the first iteration starts, determining the value boundaries of the sample position and speed according to the given total sample number N, and randomly generating an initial sample vector s according to the boundary conditions₁,…,s_NAnd corresponding initial velocity directionQuantity v₁,…,v_NThe design of the sample vector must satisfy the condition that there is a rule of one-to-one correspondence of samples to masks (i.e., the key parameter values μ, ρ, σ).

In the second and subsequent iterations, the group of samples is updated. The sample update for the (n +1) th iteration can be represented by equation (19) and equation (20):

wherein, ω, c₁、c₂To control the parameters of the iterative performance, ω_nIs the value of ω at the nth iteration, p is the historical optimum position of the single particle sample,representing the optimal position of the particle sample i in the nth iteration, wherein g is the historical global optimal position of the sample group; omega is called as an inertia factor, the value of the inertia factor is non-negative, when the value is larger, the global optimizing capability is strong, and the local optimizing capability is strong; when the time is small, the global optimizing capability is weak, and the local optimizing capability is strong; by adjusting the magnitude of ω, the global optimization performance and the local optimization performance can be adjusted. c. C₁And c₂The former is the individual learning factor of each particle sample, and the latter is the social learning factor of each particle sample. r1 and r2 represent the interval [0, 1 ]]A random number of (c);representing the velocity of the particle sample i at the nth iteration;representing the velocity of the particle sample i at the n +1 th iteration;representing the position of the particle sample i at the nth iteration;the position of the particle sample i at the n +1 th iteration is indicated.

In the above embodiment, the calculating the antenna pattern corresponding to each particle sample by using the projection algorithm according to the sample group and measuring the index in step 305 includes:

constructing a transfer relation matrix [ T ] of the complex excitation coefficient [ A ] and a directional diagram matrix [ F ], wherein the relation between the directional diagram matrix [ F ] and the transfer relation matrix and the negative excitation coefficient can be expressed by the following formula (21):

wherein, the complex excitation coefficient can also be called as a receiving weight; referring to equation (7), the antenna pattern can further be represented by equation (22):

introducing a projection operator, iteratively searching a result meeting the requirement, acquiring a corresponding upper mask MU and a corresponding lower mask ML according to a sample, and calculating a histogram difference value by using a formula (4):

ΔF＝max F(|F_n|-P_M{|F_n|})(F_n/|F_n|) (4)；

wherein, the partial directional diagram of the receiving directional diagram corresponding to the particle sample can be represented as P_M{|F_nAnd | j, the partial directional diagram corresponding to the particle sample can be expressed by equation (3):

wherein, in the above iterative optimization formula (3), M_UFor top masking, M_LFor the lower mask, F_nIs a normalized directional pattern.

A weight update difference value may be calculated according to the directional diagram difference value, and may also be referred to as a weight difference value, where the weight difference value Δ a may be represented by formula (9):

ΔA＝(T^HT)^-1T^HΔF (9)；

wherein, the receiving weight can be updated by using the weight difference, assuming the step length is C₁The receiving weight A of the corresponding particle sample in this iteration⁽ⁱ⁾Then, the receiving weight A of the corresponding particle sample in the current iteration⁽ⁱ⁺¹⁾Can be expressed by equation (10) as:

A⁽ⁱ⁺¹⁾＝A⁽ⁱ⁾+C₁ΔA (10)；

in the above embodiment, the transfer relationship matrix T (i, j) and the receiving weight a of each particle sample may be used⁽ⁱ⁺¹⁾Determining a reception directional diagram of a corresponding particle sample of the radar antenna; and determining the measurement index of the corresponding particle sample according to the receiving direction graph of each particle sample, and judging whether to continue iteration or to take the optimal result of the iteration of the current round as the optimal weight according to the measurement index.

In the above embodiment, in step 306, the determining to continue the iteration or using the optimal result of the iteration in this round as the optimal weight according to the antenna pattern measurement indicator includes:

assuming that the measurement index is distance ambiguity, the distance ambiguity is RASR, and when the SAR system works, the slant range R corresponding to the distance ambiguity region exists_aCan be expressed by the following equation (14):

RASR can be expressed by the following formula (18):

wherein the content of the first and second substances,can be expressed by the following formula (19), S_iCan be expressed by the following equation (20):

the measurement indexes can also comprise main lobe width and side lobe level, the RASR, the main lobe width, the side lobe level and other measurement indexes can be synthesized to carry out grading sequencing on the particle samples according to requirements, iteration is finished when the optimal result meets the requirements, and otherwise, the iteration is continued to update the samples.

In one embodiment, the technical solution of the present application is further described and illustrated by the inhibition effect. The special case of the most serious distance ambiguity problem in the hybrid circular polarization mode is optimized, and the two polarization modes of the transmitting and receiving signals are approximately linear polarizations which are orthogonal to each other.

In this embodiment, the number of elevation array elements of the satellite antenna is set to 22, and the satellite antenna operates in the L-band. The PRF of the selected wave position is 3505Hz, the antenna installation angle is 30.5 degrees, the visual angles of the near end and the far end of the observation wave beam are respectively 21.26 degrees and 23.69 degrees, and the track height is 607 km. And grouping the sample results in each iteration according to whether the constraint conditions are met and sequencing according to the distance fuzzy characteristic.

Fig. 4 shows the receiver plots before and after the optimization of the present embodiment, and referring to fig. 4, the side lobe 403 in the fuzzy region is significantly reduced in the optimized receiver plot 401 compared with the non-optimized receiver plot 402. Fig. 5 shows the receiving directional diagram weight magnitudes before and after the optimization in the present embodiment, and referring to fig. 5, the weight magnitude 501 of the optimized receiving directional diagram is more engineering-feasible than the weight magnitude 502 of the non-optimized receiving directional diagram.

Fig. 6 shows the distance ambiguity levels before and after the optimization in this embodiment, and referring to fig. 6, the optimized distance ambiguity 601 is effectively suppressed compared to the unoptimized distance ambiguity 602, and the above result demonstrates that the present application can effectively optimize the distance ambiguity in the hybrid circular polarization mode. The further description by optimizing the range ambiguity of the spread beam in a single polarization mode illustrates that the solution of the invention is equally applicable to other polarization modes. And similarly, the elevation array element number of the satellite antenna is set to be 22, and the satellite antenna works in an L wave band. The selected wave position PRF is 1477Hz, the antenna installation angle is 30.5 degrees, the near-end and far-end visual angles of the observed wave beam are 14.33 degrees and 27.2 degrees respectively, the track height is 607km, and the parameter initialization and screening standards are the same as those of the above embodiment.

Fig. 7 shows the reception patterns before and after the optimization in the present embodiment, and referring to fig. 7, the side lobe 703 in the blur area is significantly reduced while the beam width satisfies the requirement in the optimized reception pattern 701 compared with the non-optimized reception pattern 702. Fig. 8 shows the receiver pattern weight magnitudes before and after optimization, and referring to fig. 8, the weight magnitude 801 of the optimized receiver pattern is engineering realizable compared to the weight magnitude 802 of the non-optimized receiver pattern. Fig. 9 shows the distance ambiguity levels before and after optimization, and referring to fig. 9, the distance ambiguity 901 of the optimized reception pattern is effectively suppressed compared to the distance ambiguity 902 of the non-optimized reception pattern. The above results demonstrate that the embodiments of the present application are equally applicable to other polarization modes while addressing the hybrid circular polarization mode.

Fig. 10 is a schematic diagram of a method for generating an antenna direction diagram according to an embodiment of the present disclosure; referring to fig. 10, a projection method may be applied to determine a receiving directional diagram 1003 of a corresponding particle sample according to preset radar signal parameter values and an upper mask 1001 and a lower mask 1002 corresponding to each particle sample in the iteration, where a main lobe width 1004 and a side lobe level 1005 are measurement indexes of the receiving directional diagram 1003, and it may be determined according to the measurement indexes of the antenna directional diagram, that the main lobe width 1004 or the side lobe level 1005, to continue the iteration or to use an optimal result of the iteration in this round as an optimal weight.

Based on the foregoing embodiments, the present application provides a distance ambiguity suppression apparatus based on a particle swarm algorithm and a projection method, where the apparatus includes modules that can be implemented by a processor in an electronic device; of course, the implementation can also be realized through a specific logic circuit; in the implementation process, the processor may be a Central Processing Unit (CPU), a Microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.

Fig. 11 is a schematic structural diagram of a distance ambiguity suppression apparatus based on a particle swarm algorithm and a projection method according to an embodiment of the present application, as shown in fig. 11, the apparatus 1100 includes an obtaining module 1101, a first determining module 1102, a second determining module 1103, a third determining module 1104, and an updating module 1105, where:

an obtaining module 1101, configured to obtain a mask corresponding to each particle sample of the sample group in the current iteration; a first determining module 1102, configured to determine, according to a preset radar signal parameter value and a mask corresponding to each particle sample in the iteration, a receiving weight and a receiving direction map of the corresponding particle sample by applying a projection method; a second determining module 1103, configured to determine, according to the receiving directional graph of each particle sample, a fitness of the corresponding particle sample; a third determining module 1104, configured to sort the particle samples according to the fitness of each particle sample, and determine an optimal particle sample for the current iteration; an updating module 1105, configured to update the sample group and perform a new iteration when the fitness of the optimal particle sample in the current iteration does not meet a specific condition, and repeat the iteration until the fitness of the optimal particle sample meets the specific condition.

In one embodiment, the first determining module 1102 includes: the first determining submodule is used for determining a weight difference value of a corresponding particle sample in the iteration according to a preset radar signal parameter value, a receiving direction graph of each particle sample in the iteration of the last time and a mask corresponding to each particle sample in the iteration of the time; the second determining submodule is used for determining the receiving weight value of the corresponding particle sample in the iteration according to the preset radar signal parameter value, the receiving weight value corresponding to each particle sample in the previous iteration and the weight value difference value of each particle sample in the iteration; and the third determining submodule is used for determining a receiving directional diagram of the corresponding particle sample in the iteration according to the preset radar signal parameter value and the receiving weight of each particle sample in the iteration.

In one embodiment, the first determining sub-module includes: the first determining unit is used for determining the directional diagram difference value of the corresponding particle sample in the iteration according to the receiving directional diagram of each particle sample in the previous iteration and the mask corresponding to each particle sample in the iteration; and the second determining unit is used for determining the weight difference value of the corresponding particle sample in the iteration according to the preset radar signal parameter value and the directional diagram difference value of each particle sample in the iteration.

In one embodiment, the third determining sub-module includes: the third determining unit is used for determining a transfer relation matrix according to a preset radar signal parameter value; and the fourth determining unit is used for determining the receiving directional diagram of the corresponding particle sample in the iteration according to the transfer relation matrix and the receiving weight of each particle sample in the iteration.

In one embodiment, the preset radar signal parameter values include a down-angle sampling sequence, a wave number and coordinates of each array element of the radar in a reference coordinate system, and the third determining unit includes: a first obtaining subunit, configured to obtain a lower-view sampling sequence; the lower visual angle sampling sequence comprises coordinates of a plurality of lower visual angles in a far-field spherical coordinate system; the conversion subunit is used for converting the far-field spherical coordinate system into a rectangular coordinate system according to the lower visual angle sampling sequence and the wave number, and determining the coordinate of each lower visual angle in the rectangular coordinate system; the first determining subunit is configured to determine a far-field directional diagram of each array element in multiple array elements of the radar according to coordinates of each downward viewing angle in the rectangular coordinate system; the second acquisition subunit is used for acquiring the coordinates of each array element in a reference coordinate system; and the second determining subunit is used for determining a transfer relation matrix according to the far-field directional diagram of each array element and the coordinates of the corresponding array element in the reference coordinate system.

In one embodiment, the mask includes a first mask and a second mask, and the first determining unit includes: the third determining subunit is configured to determine, according to the receiving directional diagram of each particle sample in the previous iteration, a maximum value of the receiving directional diagram of the corresponding particle sample in the previous iteration; the fourth determining subunit is configured to perform normalization processing on the receiving directional diagram of each particle sample in the previous iteration to obtain a normalized directional diagram of the receiving directional diagram of the corresponding particle sample in the previous iteration; a fifth determining subunit, configured to determine, according to the first mask and the second mask in the current iteration and the normalized directional diagram of each particle sample in the previous iteration, a partial directional diagram of a receiving directional diagram of a corresponding particle sample in the previous iteration; and the sixth determining subunit is configured to determine, according to the maximum value of the reception pattern of each particle sample in the previous iteration, the normalized pattern and a partial pattern, a pattern difference value of a corresponding particle sample in the current iteration.

In one embodiment, the second determining module 1103 includes: the fourth determining submodule is used for determining the measurement index of the corresponding particle sample in the iteration according to the receiving direction graph of each particle sample in the iteration; and the fifth determining submodule is used for determining the fitness of the corresponding particle sample according to the measurement index of each particle sample and a preset fitness function in the iteration.

In one embodiment, the measurement indicators include at least three of: range ambiguity, main lobe width and side lobe levels.

In one embodiment, the measurement index is range ambiguity, and the preset radar signal parameter values comprise a downward-looking angle sampling sequence, an earth radius, a light speed, a track height of the radar, a pulse repetition frequency of the radar, the number of downward-looking angles in the downward-looking angle sampling sequence, an ambiguity region number of the radar and empirical parameters;

the fourth determination submodule includes: an acquisition unit configured to acquire a lower view sampling sequence; the lower visual angle sampling sequence comprises coordinates of a plurality of lower visual angles in a far-field spherical coordinate system; a fifth determining unit, configured to determine a slant range sequence according to the downward view sampling sequence, the earth radius, and the orbit height of the radar; a sixth determining unit, configured to determine a fuzzy slope sequence according to the slope sequence, the pulse repetition frequency, the fuzzy region number, and the light speed; a seventh determining unit, configured to determine an incident angle sequence according to the fuzzy slant distance sequence; an eighth determining unit, configured to determine a reflectivity sequence according to the incidence angle sequence and the empirical parameter; and the ninth determining unit is used for determining the measurement index of the corresponding particle sample in the iteration according to the incidence angle sequence, the reflectivity sequence, the fuzzy slope distance sequence and the receiving direction graph of each particle sample in the iteration.

In the embodiment of the present application, if the distance ambiguity suppression method based on the particle swarm algorithm and the projection method is implemented in the form of a software functional module and sold or used as an independent product, the distance ambiguity suppression method may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or a part contributing to the related art may be embodied in the form of a software product stored in a storage medium, and including a plurality of instructions for enabling an electronic device (which may be a mobile phone, a tablet computer, a desktop computer, a personal digital assistant, a navigator, a digital phone, a video phone, a television, a sensing device, etc.) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.

The above description of the apparatus embodiments, similar to the above description of the method embodiments, has similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the apparatus of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.

Correspondingly, an embodiment of the present application provides an electronic device, and fig. 12 is a schematic diagram of a hardware entity of the electronic device according to the embodiment of the present application, and as shown in fig. 12, the hardware entity of the electronic device 1200 includes: the distance fuzzy suppression method based on the particle swarm optimization and the projection method of the embodiment is realized when the processor 1202 executes the computer program stored in the memory 1201 and executed by the processor 1202.

The Memory 1201 is configured to store instructions and applications executable by the processor 1202, and may also buffer data (e.g., image data, audio data, voice communication data, and video communication data) to be processed or already processed by the processor 1202 and modules in the electronic device 1200, and may be implemented by a FLASH Memory (FLASH) or a Random Access Memory (RAM).

Correspondingly, the present application provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps in the distance blur suppression method based on the particle swarm optimization and the projection method provided in the foregoing embodiments.

Here, it should be noted that: the above description of the storage medium and device embodiments, similar to the above description of the method embodiments, has similar advantageous effects as the device embodiments. For technical details not disclosed in the embodiments of the storage medium and method of the present application, reference is made to the description of the embodiments of the apparatus of the present application for understanding.

It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment. In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or a part contributing to the related art may be embodied in the form of a software product stored in a storage medium, and including a plurality of instructions for enabling a computer device (which may be a mobile phone, a tablet computer, a desktop computer, a personal digital assistant, a navigator, a digital phone, a video phone, a television, a sensing device, etc.) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.

The methods disclosed in the several method embodiments provided in the present application may be combined arbitrarily without conflict to obtain new method embodiments. Features disclosed in several of the product embodiments provided in the present application may be combined in any combination to yield new product embodiments without conflict. The features disclosed in the several method or apparatus embodiments provided in the present application may be combined arbitrarily, without conflict, to arrive at new method embodiments or apparatus embodiments.

The above description is only for the embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.