1. A method of signal reconstruction, the method comprising:

establishing a multi-channel SAR signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of a multi-channel synthetic aperture radar SAR, and obtaining azimuth fuzzy power based on the multi-channel SAR signal model;

establishing a constraint condition related to frequency change for the azimuth fuzzy power; establishing a constraint optimization model of which the objective function is the output noise power based on the constraint condition;

and solving the constraint optimization model to obtain a multi-channel signal reconstruction filter when the output noise power is minimum, so as to realize signal reconstruction based on the multi-channel signal reconstruction filter.

2. The method of claim 1, wherein the establishing a multi-channel Synthetic Aperture Radar (SAR) signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of the SAR comprises:

acquiring azimuth receiving signals corresponding to at least one azimuth receiving channel of the multi-channel SAR;

sampling the azimuth receiving signal to obtain a sampled azimuth receiving signal;

carrying out Fourier transform on the sampled azimuth receiving signal to obtain a frequency domain expression corresponding to the sampled azimuth receiving signal;

and establishing a multi-channel SAR signal model according to the frequency domain expression.

3. The method of claim 1 or 2, wherein the deriving an azimuth ambiguity power based on the multi-channel SAR signal model comprises:

reconstructing the multi-channel SAR signal model by using a multi-channel signal reconstruction filter to obtain a reconstructed multi-channel SAR signal model;

determining an orientation fuzzy vector and an output noise power based on the reconstructed multi-channel SAR signal model;

and obtaining the azimuth fuzzy power according to the azimuth fuzzy vector.

4. The method of claim 1, wherein establishing a frequency variation-dependent constraint on the azimuth ambiguity power comprises:

establishing a constraint condition related to frequency change for the azimuth ambiguity power, wherein the constraint condition is as follows:

wherein, P_l(f) For the azimuth blur power, ω_l(f) A filter is reconstructed for the multi-channel signal,for the conjugate transpose, R, of the multichannel signal reconstruction filter_l(f) To represent the covariance matrix of the azimuthal blur, epsilon²(f) Is a constraint related to the frequency variation.

5. The method of claim 4, wherein the constrained optimization model is:

in the constraint optimization model, a_l(f) Representing the channel vector.

6. The method of claim 1, wherein solving the constrained optimization model to obtain a multi-channel signal reconstruction filter with the minimum output noise power comprises:

constructing a Lagrange multiplier expression corresponding to the constraint optimization model;

solving the Lagrange multiplier expression to obtain a solution of the constraint optimization model when the value of the Lagrange multiplier expression is minimum;

determining the multi-channel signal reconstruction filter when the output noise power is minimum using a solution of the constrained optimization model.

7. A signal reconstruction device, characterized in that the device comprises:

the data processing unit is used for establishing a multi-channel Synthetic Aperture Radar (SAR) signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of the SAR and obtaining azimuth fuzzy power based on the multi-channel SAR signal model;

the data processing unit is also used for establishing a constraint condition related to frequency change for the azimuth fuzzy power; establishing a constraint optimization model of which the objective function is the output noise power based on the constraint condition;

and the signal reconstruction unit is used for solving the constraint optimization model to obtain a multi-channel signal reconstruction filter when the output noise power is minimum, so as to realize signal reconstruction based on the multi-channel signal reconstruction filter.

8. The apparatus of claim 7, further comprising:

the acquisition unit is used for acquiring azimuth receiving signals corresponding to at least one azimuth receiving channel of the multi-channel SAR;

the sampling unit is used for sampling the azimuth receiving signal to obtain a sampled azimuth receiving signal;

the data processing unit is further configured to perform fourier transform on the sampled azimuth receiving signal to obtain a frequency domain expression corresponding to the sampled azimuth receiving signal;

and the data processing unit is also used for establishing a multi-channel SAR signal model according to the frequency domain expression.

9. The apparatus according to claim 7 or 8, characterized in that it further comprises:

the signal reconstruction unit is also used for reconstructing the multi-channel SAR signal model by using a multi-channel signal reconstruction filter to obtain a reconstructed multi-channel SAR signal model;

the determining unit is used for determining an orientation fuzzy vector and output noise power based on the reconstructed multi-channel SAR signal model;

the data processing unit is further configured to obtain the azimuth fuzzy power according to the azimuth fuzzy vector.

10. A signal reconstruction device, characterized in that the device comprises: a processor, a memory, and a communication bus; the processor, when executing a running program stored in the memory, implements the signal reconstruction method according to any one of claims 1 to 6.

Background

For a multi-channel Synthetic Aperture Radar (SAR) system, spatial sampling cannot be directly replaced by temporal sampling, and a reconstruction method is required to convert the spatial sampling into the temporal sampling. The existing reconstruction method can restore the aliasing Doppler frequency spectrum in a low-ambiguity mode under an ideal condition, and further reduces the requirement of a multi-channel SAR system on pulse repetition frequency under the condition of not changing the azimuth resolution, so that high-resolution wide-range SAR imaging is realized. However, in the case of non-uniform spatial sampling, the conventional reconstruction method may cause an increase in output noise, which in turn causes a decrease in the quality of the SAR image.

Disclosure of Invention

The embodiment of the application provides a signal reconstruction method and device, which can reduce output noise after signal reconstruction and further improve the quality of an SAR image.

The technical scheme of the application is realized as follows:

in a first aspect, an embodiment of the present application provides a signal reconstruction method, where the method includes:

establishing a multi-channel SAR signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of a multi-channel synthetic aperture radar SAR, and obtaining azimuth fuzzy power based on the multi-channel SAR signal model;

establishing a constraint condition related to frequency change for the azimuth fuzzy power; establishing a constraint optimization model of which the objective function is the output noise power based on the constraint condition;

and solving the constraint optimization model to obtain a multi-channel signal reconstruction filter when the output noise power is minimum, so as to realize signal reconstruction based on the multi-channel signal reconstruction filter.

In the above signal reconstruction method, the establishing a multi-channel Synthetic Aperture Radar (SAR) signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of the SAR comprises:

acquiring azimuth receiving signals corresponding to at least one azimuth receiving channel of the multi-channel SAR;

sampling the azimuth receiving signal to obtain a sampled azimuth receiving signal;

carrying out Fourier transform on the sampled azimuth receiving signal to obtain a frequency domain expression corresponding to the sampled azimuth receiving signal;

and establishing a multi-channel SAR signal model according to the frequency domain expression.

In the above signal reconstruction method, the obtaining of the azimuth ambiguity power based on the multi-channel SAR signal model includes:

reconstructing the multi-channel SAR signal model by using a multi-channel signal reconstruction filter to obtain a reconstructed multi-channel SAR signal model;

determining an orientation fuzzy vector and an output noise power based on the reconstructed multi-channel SAR signal model;

and obtaining the azimuth fuzzy power according to the azimuth fuzzy vector.

In the above signal reconstruction method, the establishing a constraint condition related to frequency variation for the azimuth ambiguity power includes:

establishing a constraint condition related to frequency change for the azimuth ambiguity power, wherein the constraint condition is as follows:

wherein, P_l(f) For the azimuth blur power, ω_l(f) Reconstructing a filter, ω, for said multi-channel signal_l ^H(f) For the conjugate transpose, R, of the multichannel signal reconstruction filter_l(f) To represent the covariance matrix of the azimuthal blur, epsilon²(f) Is a constraint related to the frequency variation.

In the above signal reconstruction method, the constrained optimization model is:

in the constraint optimization model, a_l(f) Representing the channel vector.

In the above signal reconstruction method, the solving the constrained optimization model to obtain a multi-channel signal reconstruction filter when the output noise power is minimum includes:

constructing a Lagrange multiplier expression corresponding to the constraint optimization model;

solving the Lagrange multiplier expression to obtain a solution of the constraint optimization model when the value of the Lagrange multiplier expression is minimum;

determining the multi-channel signal reconstruction filter when the output noise power is minimum using a solution of the constrained optimization model.

In a second aspect, an embodiment of the present application provides a signal reconstruction apparatus, including:

the data processing unit is used for establishing a multi-channel Synthetic Aperture Radar (SAR) signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of the SAR and obtaining azimuth fuzzy power based on the multi-channel SAR signal model;

the data processing unit is also used for establishing a constraint condition related to frequency change for the azimuth fuzzy power; establishing a constraint optimization model of which the objective function is the output noise power based on the constraint condition;

and the signal reconstruction unit is used for solving the constraint optimization model to obtain a multi-channel signal reconstruction filter when the output noise power is minimum, so as to realize signal reconstruction based on the multi-channel signal reconstruction filter.

In the above signal reconstruction device, the device further includes:

the acquisition unit is used for acquiring azimuth receiving signals corresponding to at least one azimuth receiving channel of the multi-channel SAR;

the sampling unit is used for sampling the azimuth receiving signal to obtain a sampled azimuth receiving signal;

the data processing unit is further configured to perform fourier transform on the sampled azimuth receiving signal to obtain a frequency domain expression corresponding to the sampled azimuth receiving signal;

and the data processing unit is also used for establishing a multi-channel SAR signal model according to the frequency domain expression.

In the above signal reconstruction device, the device further includes:

the signal reconstruction unit is also used for reconstructing the multi-channel SAR signal model by using a multi-channel signal reconstruction filter to obtain a reconstructed multi-channel SAR signal model;

the determining unit is used for determining an orientation fuzzy vector and output noise power based on the reconstructed multi-channel SAR signal model;

the data processing unit is further configured to obtain the azimuth fuzzy power according to the azimuth fuzzy vector.

In a third aspect, an embodiment of the present application provides a signal reconstruction apparatus, where the apparatus includes: a processor, a memory, and a communication bus; the processor, when executing the running program stored in the memory, implements the signal reconstruction method as described in any one of the above.

The embodiment of the application provides a signal reconstruction method and equipment, wherein the method comprises the following steps: establishing a multi-channel SAR signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of the multi-channel SAR, and obtaining azimuth fuzzy power based on the multi-channel SAR signal model; establishing a constraint condition related to frequency change for the azimuth fuzzy power; establishing a constraint optimization model of which the objective function is the output noise power based on the constraint condition; solving the constraint optimization model to obtain a multi-channel signal reconstruction filter when the output noise power is minimum, so as to realize signal reconstruction based on the multi-channel signal reconstruction filter; by adopting the implementation scheme, the multi-channel SAR reconstruction filter with the minimum output noise power can be obtained by applying constraint conditions to the azimuth fuzzy power and establishing a constraint optimization model with a target function as the output noise power, so that the aim of improving the SAR image quality through the filter is fulfilled.

Drawings

Fig. 1 is a flowchart of a signal reconstruction method according to an embodiment of the present disclosure;

fig. 2 is an exemplary multi-channel SAR system parameter map provided by an embodiment of the present application;

fig. 3 is a schematic diagram of changes of AASR with PRF of an exemplary DBF reconstruction method and DBF constraint reconstruction method provided in an embodiment of the present application;

fig. 4 is a schematic diagram illustrating SNR of an exemplary DBF reconstruction method and a DBF constraint reconstruction method according to an embodiment of the present disclosure as a function of PRF;

FIG. 5 is a diagram illustrating the result of azimuthal pulse compression after an exemplary DBF reconstruction method according to an embodiment of the present disclosure;

FIG. 6 is a diagram illustrating the result of azimuthal pulse compression after an exemplary DBF constraint reconstruction method according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of a signal reconstruction apparatus 1 according to an embodiment of the present disclosure;

fig. 8 is a schematic structural diagram of a signal reconstruction apparatus 1 according to an embodiment of the present application.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the present application. And are not intended to limit the present application.

An embodiment of the present application provides a signal reconstruction method, which is applied to a signal reconstruction device, and fig. 1 is a flowchart of the signal reconstruction method provided in the embodiment of the present application, and as shown in fig. 1, the signal reconstruction method may include:

s101, establishing a multi-channel SAR signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of the multi-channel SAR, and obtaining azimuth fuzzy power based on the multi-channel SAR signal model.

In the embodiment of the application, the signal reconstruction device establishes a multi-channel Synthetic Aperture Radar (SAR) signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of the SAR, and obtains azimuth fuzzy power based on the multi-channel SAR signal model.

Specifically, the signal reconstruction device acquires an azimuth receiving signal corresponding to at least one azimuth receiving channel of the multi-channel SAR; sampling the azimuth receiving signal to obtain a sampled azimuth receiving signal; carrying out Fourier transform on the sampled azimuth receiving signal to obtain a frequency domain expression corresponding to the sampled azimuth receiving signal; and establishing a multi-channel SAR signal model according to the frequency domain expression.

Illustratively, assume that the multi-channel SAR system has M antennas evenly distributed in the azimuth direction, where the first antenna is used as both the transmit channel to transmit signals and the other antenna as well as the receive channel to receive signals. In addition, without loss of generality, assume that M is odd (M ≧ 3), and the shortest slant distance to the scene object in the azimuth direction is R₀If the length and the distance between the receiving antennas are both d, the distance between the mth receiving antenna and the transmitting antenna is d_mD, (M-1), M1, 2, …, M. According to the multi-channel SAR system geometry, the received signal of the mth receive channel can be approximated as:

in the above formula (1), t is azimuth slow time, v_sFor SAR platform velocity, s (t) represents the received signal of the first receiving antenna, n_m(t) is the noise signal, exponential termIs a constant term independent of t that can be pre-compensated before SAR signal processing, so this exponential term is ignored. In addition, in the satellite-borne SAR system, the range signal has almost no influence on the reconstruction of the azimuth signal, so that only the azimuth receiving signal needs to be analyzed.

Let the Pulse Repetition Frequency (PRF) (i.e., the sampling rate) be f_pThe sampling interval is T_p＝1/f_pThen the sampled signal can be expressed as:

in the above equation (2), δ (t) is a dirac function, which has a value equal to zero at points other than zero, and its integral over the entire domain is equal to 1, and n is an arbitrary integer. According to the property of fourier transform, the frequency domain expression of the sampled signal is:

in the above formula (3), f is the azimuthal Doppler frequency, S (f) is the frequency spectrum of the signal s (t), n_m(f) Is a noise spectrum, k is an arbitrary integer, N is an integer far larger than M, and Nf is satisfied_p<2v_s/λ≤(N+1)f_pAnd λ is the wavelength. According to the result of the above formula, the spectral vector expression of the sampled multi-channel signal can be obtained as follows:

wherein:

in the above formula (5) [. ]]^TIn order to perform the matrix transposition operation,a spectral vector representing the sampled signal, a_k(f) k-N, …, N denotes the channel vector, N (f) is the noise vector and its covariance matrix is:

in the above equation (6), E (-) represents the desired operator,representing the input noise power, I is the identity matrix. Formula (4) is a multi-channelSignal model of the SAR system.

It should be noted that after the multi-channel SAR signal model is established, the azimuth ambiguity power is obtained based on the multi-channel SAR signal model.

Specifically, a multi-channel signal reconstruction filter is used for reconstructing a multi-channel SAR signal model to obtain a reconstructed multi-channel SAR signal model; determining an orientation fuzzy vector and an output noise power based on the reconstructed multi-channel SAR signal model; and obtaining the azimuth fuzzy power according to the azimuth fuzzy vector.

Illustratively, assume ω_l(f) For a multi-channel signal reconstruction filter, the reconstructed signal can then be expressed as:

in the above formula (7), l is an integer, and l ∈ [ - (M-1)/2]，(·)^HIs a conjugate transpose operation. The orientation fuzzy vector χ can be obtained by the above formula_l(f) And output noise power p_nRespectively is as follows:

from equation (8) above, and from the definition of the orientation blur vector, the orientation blur power can be derived, expressed as:

in equation (9) above, the covariance matrix of the azimuth ambiguities is:

by the above equation (10) and according to the definition of the azimuth ambiguity power, the azimuth ambiguity index of the multi-channel SAR system can be obtained: azimuth ambiguity-to-signal ratio (AASR), expressed as:

in the above formula (11), B_dFor the Doppler processing bandwidth, rect (-) is a rectangular function, which is specified as follows:

in addition, a Signal-to-noise ratio (SNR) is defined as follows:

s102, establishing a constraint condition related to frequency change for the azimuth fuzzy power; and establishing a constraint optimization model of the objective function as the output noise power based on the constraint condition.

In the embodiment of the application, after the signal reconstruction equipment obtains the azimuth fuzzy power, a constraint condition related to frequency change is established for the azimuth fuzzy power; and establishing a constraint optimization model of the objective function as the output noise power based on the constraint condition.

Specifically, a constraint condition related to frequency change is established for the azimuth ambiguity power, and the constraint condition is as follows:

wherein, P_l(f) For azimuthal blur power, omega_l(f) A filter is reconstructed for the multi-channel signal,conjugate transpose of filter for multi-channel signal reconstruction, R_l(f) Covariance matrix for representing azimuth ambiguitiesArray epsilon²(f) Is a constraint related to the frequency variation.

The constraint optimization model specifically comprises the following steps:

in the constraint optimization model (15), a_l(f) Representing channel vectors, equality constraintsIn order to ensure that the doppler spectrum passes through without distortion.

S103, solving the constraint optimization model to obtain a multi-channel signal reconstruction filter when the output noise power is minimum, so as to realize signal reconstruction based on the multi-channel signal reconstruction filter.

In the embodiment of the application, after the signal reconstruction device establishes the constraint optimization model, the constraint optimization model is solved to obtain the multi-channel signal reconstruction filter when the output noise power is minimum, so that signal reconstruction is realized based on the multi-channel signal reconstruction filter.

Specifically, a Lagrange multiplier expression corresponding to the constraint optimization model is constructed; solving the Lagrange multiplier expression to obtain a solution of the constraint optimization model when the value of the Lagrange multiplier expression is minimum; and determining a multi-channel signal reconstruction filter when the output noise power is minimum by using the solution of the constraint optimization model.

Illustratively, let Φ represent all feasible solutions in the constrained optimization model first, and then consider the lagrangian multiplier function of the constrained optimization model, which is expressed as:

wherein the real numbers λ and μ are lagrange multipliers and λ satisfies:

I+λR_l(f)＞0 (17)

thereby making it possible tog₁(λ, μ) can be minimized, and there are

The constrained optimization model is solved in two cases:

the first condition is as follows: when the channel vector a_l(f) Satisfy the requirement of

Under the condition, the optimal solution of the constraint optimization model can be expressed as:

case two: when the channel vector a_l(f) When the condition in equation (19) is not satisfied, equation (16) can be converted to:

for fixed λ and μ, let g₁The optimal solution for minimization of (λ, μ) is:

ω_l(f)＝μ(I+λR_l(f))^-1a_l(f) (22)

at this time g₁The minimum value of (λ, μ) can be expressed as:

and a function g with mu as an objective function₂Maximum value of (lambda, mu)Obtaining:

and g is₂The (λ, μ) maximum can be expressed as:

further, a function g with λ as an objective function₃Maximum value of (lambda)Is obtained and isIs determined by the following formula:

then, the method in the formula (23)Substituting into equation (22) can obtain the optimal solution of the constrained optimization model, which can be expressed as:

it can be understood that the multi-channel SAR reconstruction filter with the azimuth fuzzy power lower than the fuzzy constraint and the lowest output noise level can be obtained by firstly selecting the fuzzy constraint condition and then constraining the optimal solution of the optimization model.

It should be noted that the parameters of the multi-channel SAR system used in the exemplary case presented in the embodiment of the present application are shown in fig. 2.

An embodiment of the present application provides a schematic diagram of changes of AASR with PRF of an exemplary DBF reconstruction method and a DBF constraint reconstruction method, as shown in fig. 3, specifically, a change of an Azimuth-ambiguity-ratio (AASR) with a Pulse Repetition Frequency (PRF) of a Digital Beam Forming (DBF) reconstruction method and a DBF constraint reconstruction method, as seen in fig. 3, after applying a constraint condition to Azimuth ambiguity power, the AASR is the same as that when the constraint condition is not applied.

The embodiment of the present application provides a schematic diagram of changes of SNRs of an exemplary DBF reconstruction method and a DBF constraint reconstruction method with a PRF, as shown in fig. 4, specifically, a change of a defined Signal-to-noise ratio (SNR) of the DBF reconstruction method and the DBF constraint reconstruction method with the PRF. It can be seen from fig. 4 that after applying the constraint to the azimuth-obscuring power, the SNR increases compared to when no constraint is applied.

An embodiment of the present application provides a schematic diagram of an azimuth pulse compression result after an exemplary DBF reconstruction method, as shown in fig. 5, specifically, an azimuth pulse compression result after a DBF reconstruction method when a PRF is 1862Hz, and an embodiment of the present application provides a schematic diagram of an azimuth pulse compression result after an exemplary DBF constraint reconstruction method, as shown in fig. 6, specifically, an azimuth pulse compression result after a DBF constraint reconstruction method when a PRF is 1862Hz, and it can be seen by comparing fig. 5 and fig. 6 that an output noise level can be suppressed to the maximum extent by the signal reconstruction method of the present application under the condition that an azimuth fuzzy power is constant.

The embodiment of the application provides a signal reconstruction method, which comprises the following steps: establishing a multi-channel SAR signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of the multi-channel SAR, and obtaining azimuth fuzzy power based on the multi-channel SAR signal model; establishing a constraint condition related to frequency change for the azimuth fuzzy power; establishing a constraint optimization model of which the objective function is the output noise power based on the constraint condition; solving the constraint optimization model to obtain a multi-channel signal reconstruction filter when the output noise power is minimum, so as to realize signal reconstruction based on the multi-channel signal reconstruction filter; by adopting the implementation scheme, the multi-channel SAR reconstruction filter with the minimum output noise power can be obtained by applying constraint conditions to the azimuth fuzzy power and establishing a constraint optimization model with a target function as the output noise power, so that the aim of improving the SAR image quality through the filter is fulfilled.

Based on the foregoing embodiments, in another embodiment of the present application, a signal reconstruction apparatus 1 is provided, and fig. 7 is a schematic diagram of a composition structure of the signal reconstruction apparatus 1 provided in the present application, as shown in fig. 7, the signal reconstruction apparatus 1 includes:

the data processing unit 10 is configured to establish a multi-channel Synthetic Aperture Radar (SAR) signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of the SAR, and obtain azimuth fuzzy power based on the multi-channel SAR signal model;

the data processing unit 10 is further configured to establish a constraint condition related to frequency variation for the azimuth ambiguity power; establishing a constraint optimization model of which the objective function is the output noise power based on the constraint condition;

and the signal reconstruction unit 11 is configured to solve the constrained optimization model to obtain a multi-channel signal reconstruction filter when the output noise power is minimum, so as to implement signal reconstruction based on the multi-channel signal reconstruction filter.

Optionally, the signal reconstruction apparatus 1 further includes:

the acquisition unit is used for acquiring azimuth receiving signals corresponding to at least one azimuth receiving channel of the multi-channel SAR;

the sampling unit is used for sampling the azimuth receiving signal to obtain a sampled azimuth receiving signal;

the data processing unit is further configured to perform fourier transform on the sampled azimuth receiving signal to obtain a frequency domain expression corresponding to the sampled azimuth receiving signal;

and the data processing unit is also used for establishing a multi-channel SAR signal model according to the frequency domain expression.

Optionally, the signal reconstruction apparatus 1 further includes:

the signal reconstruction unit is used for reconstructing the multi-channel SAR signal model by using a multi-channel signal reconstruction filter to obtain a reconstructed multi-channel SAR signal model;

the determining unit is used for determining an orientation fuzzy vector and output noise power based on the reconstructed multi-channel SAR signal model;

the data processing unit is further configured to obtain the azimuth fuzzy power according to the azimuth fuzzy vector.

The data processing unit is further configured to establish a constraint condition related to frequency change for the azimuth ambiguity power, where the constraint condition is:

wherein, P_l(f) For the azimuth blur power, ω_l(f) A filter is reconstructed for the multi-channel signal,for the conjugate transpose, R, of the multichannel signal reconstruction filter_l(f) To represent the covariance matrix of the azimuthal blur, epsilon²(f) Is a constraint related to the frequency variation.

Wherein the constraint optimization model is as follows:

in the constraint optimization model, a_l(f) Representing the channel vector.

The data processing unit is also used for constructing a Lagrange multiplier expression corresponding to the constraint optimization model;

the data processing unit is further configured to solve the lagrangian multiplier expression to obtain a solution of the constraint optimization model when the value of the lagrangian multiplier expression is minimum;

the determining unit is further configured to determine the multi-channel signal reconstruction filter when the output noise power is minimum by using a solution of the constrained optimization model.

An embodiment of the present application provides a signal reconstruction device, including: establishing a multi-channel SAR signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of the multi-channel SAR, and obtaining azimuth fuzzy power based on the multi-channel SAR signal model; establishing a constraint condition related to frequency change for the azimuth fuzzy power; establishing a constraint optimization model of which the objective function is the output noise power based on the constraint condition; solving the constraint optimization model to obtain a multi-channel signal reconstruction filter when the output noise power is minimum, so as to realize signal reconstruction based on the multi-channel signal reconstruction filter; by adopting the implementation scheme, the multi-channel SAR reconstruction filter with the minimum output noise power can be obtained by applying constraint conditions to the azimuth fuzzy power and establishing a constraint optimization model with a target function as the output noise power, so that the aim of improving the SAR image quality through the filter is fulfilled.

Fig. 8 is a schematic diagram of a second composition structure of a signal reconstruction apparatus 1 according to an embodiment of the present application, and in practical applications, based on the same disclosure concept of the foregoing embodiment, as shown in fig. 8, the signal reconstruction apparatus 1 according to the present embodiment includes: a processor 12, a memory 13, and a communication bus 14.

In a Specific embodiment, the data Processing unit 10, the Signal reconstructing unit 11, the obtaining unit, the sampling unit and the determining unit may be implemented by a Processor 12 located on the Signal reconstructing apparatus 1, and the Processor 12 may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic image Processing Device (PLD), a Field Programmable Gate Array (FPGA), a CPU, a controller, a microcontroller and a microprocessor. It is understood that the electronic devices for implementing the above-mentioned processor functions may be other for different signal reconstruction devices, and the embodiment is not particularly limited.

In the embodiment of the present application, the communication bus 14 is used for realizing connection communication between the processor 12 and the memory 13; the processor 12 implements the following signal reconstruction method when executing the operating program stored in the memory 13:

establishing a multi-channel SAR signal model according to an azimuth receiving signal corresponding to at least one azimuth receiving channel of a multi-channel synthetic aperture radar SAR, and obtaining azimuth fuzzy power based on the multi-channel SAR signal model;

establishing a constraint condition related to frequency change for the azimuth fuzzy power; establishing a constraint optimization model of which the objective function is the output noise power based on the constraint condition;

and solving the constraint optimization model to obtain a multi-channel signal reconstruction filter when the output noise power is minimum, so as to realize signal reconstruction based on the multi-channel signal reconstruction filter.

Optionally, the processor 12 is further configured to acquire an azimuth receiving signal corresponding to at least one azimuth receiving channel of the multi-channel SAR; sampling the azimuth receiving signal to obtain a sampled azimuth receiving signal; carrying out Fourier transform on the sampled azimuth receiving signal to obtain a frequency domain expression corresponding to the sampled azimuth receiving signal; and establishing a multi-channel SAR signal model according to the frequency domain expression.

Optionally, the processor 12 is further configured to reconstruct the multi-channel SAR signal model by using a multi-channel signal reconstruction filter, so as to obtain a reconstructed multi-channel SAR signal model; determining an orientation fuzzy vector and an output noise power based on the reconstructed multi-channel SAR signal model; and obtaining the azimuth fuzzy power according to the azimuth fuzzy vector.

Optionally, the processor 12 is further configured to establish a constraint condition related to a frequency change for the azimuth ambiguity power, where the constraint condition is:

wherein, P_l(f) For the azimuth blur power, ω_l(f) A filter is reconstructed for the multi-channel signal,for the conjugate transpose, R, of the multichannel signal reconstruction filter_l(f) To represent the covariance matrix of the azimuthal blur, epsilon²(f) Is a constraint related to the frequency variation.

Optionally, the processor 12 is further configured to use the constraint optimization model as:

in the constraint optimization model, a_l(f) Representing the channel vector.

Optionally, the processor 12 is further configured to construct a lagrangian multiplier expression corresponding to the constrained optimization model; solving the Lagrange multiplier expression to obtain a solution of the constraint optimization model when the value of the Lagrange multiplier expression is minimum; determining the multi-channel signal reconstruction filter when the output noise power is minimum using a solution of the constrained optimization model.

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.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling an image display device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the signal reconstruction method according to the embodiments of the present disclosure.

The above description is only a preferred embodiment of the present application, and is not intended to limit the scope of the present application.