1. A piecewise linear frequency modulation interference elimination method based on self-adaptive windowing is characterized by comprising the following steps:

step 1: performing radio frequency processing and digital processing on a radio frequency signal received by a single antenna receiver to obtain a digital received signal vector X of N sampling points; x ═ X (1) X (2), …, X (n), …, X (n)]^TWhere N is 1,2, … N, N representing the sampling instant, [ ·]^TRepresenting a transpose;

step 2: obtaining a difference map of signal X, denoted as D (n) ═ X (n) — X (n-1), and multiplying the conjugate of D (n) obtained by itself to obtain | D (n) harin²＝D(n)·D^*(n),(·)^*Representing conjugation, |, representing a modulus value;

and step 3: | D (n) | ventilated wind²Multiplying a sliding window with Q as the window center, the window length being 2Q +1, taking the median value of the values in the window at each moment, i.e. D_m(q)²＝Med[|D(n)|²W_q(n)]Wherein

And 4, step 4: to D_m(q)²Obtaining Δ D (q) by second difference²：ΔD(q)²＝D_m(q)²-D_m(q-1)²Detection of Δ D (q)²The periodicity of the sequence of (a);

and 5: segmenting the received signal according to the obtained interference estimation information, and then eliminating interference components in each segment of data; and then removing residual interference from each processed signal segment.

2. The adaptive windowing based piecewise chirp interference cancellation method of claim 1, wherein: detection in step 4D (q)²The periodicity of the sequence in (a) is specifically:

step 4.1: initializing threshold valuesBeta is a set threshold weight value,representing the mean, identifying local peaks exceeding a threshold as valid peaks, and recording the position of these peaks as P_iIf P is present_i-P_i-1＜H_i，H_iFor a given minimum cycle length threshold, P is deleted_i-1(ii) a ComputingObtaining a cycle start and stop position estimateThe lower corner mark d is 1,2, · · v, d is the period component label, v is the total number of periods;

step 4.2: obtaining linear frequency modulation interference period length estimated valueAnd to the signalIs segmented to obtain a segmented signal X_d(n)；

Step 4.3: local X is converted by discrete polynomial method_d(n) conversion of the signal to M_d(n)，M_d(n)＝X_d(n)X_d ^*(n-a)，a is the set time delay length, L is the set center extraction length, and calculation is carried outObtaining coarse estimation value of linear frequency modulation interference frequency modulation rate

3. The adaptive windowing based piecewise chirp interference cancellation method of claim 2, wherein: step 5, segmenting the received signal according to the obtained interference estimation information, and then eliminating the interference component in each segment of data; then, removing residual interference from each processed segment of signals specifically comprises:

step 5.1: at X_d(n) adding w/4 zeros in tandem, whereink is an adjustable numerical value, and when the w value exceeds the given single maximum processing point number, k is adjusted to enable w to be smaller than a given value;

step 5.2: for the segmented signals after zero padding, a Blackman window with the length of w is lengthened, and the window position isObtaining a windowed Signal X'_d(n)；

Step 5.3: to X'_d(n) if d is 1, go to step 5.4, otherwise, frequency modulation estimate is madeAndthe inner estimates are compared in sequence, the lower subscript i being the index of the component used for comparison, if anyLet p be_d＝p_i，p_iIs [ p ]₁ p₂ ...,p_i ...,p_d-1]If not, the step 5.4 is carried out;

step 5.4: by passingCoarse estimation of optimal orderAccording toObtaining optimal order p using dichotomy scanning in given range_d；

Step 5.5: to X'_d(n) by carrying out p_dFractional Fourier transform, extracting fractional threshold value by mean value statisticsP is given threshold weight, interference energy is removed by using a spectral line cutting method, and a processed signal F is obtained_d(n)；

Step 5.6: to F_d(n) by carrying out p_dThe order fractional order Fourier inverse transformation discards the samples of each w/4 before and after the order fractional order Fourier inverse transformation to obtain the processed signal Y_d(n) and removing the residual pulse in the time domain by using a spectral line cutting method.

Background

Chirp interference signals are a common type of interference for satellite navigation receivers, and such interference may be generated from radar or a malicious jammer and is not easily eliminated by time domain or frequency domain interference suppression techniques. In order to ensure the continuity and reliability of GNSS services, researchers have started from the difference between signals and interference in the time-frequency domain, the space domain and the space-time domain, and some interference suppression methods based on multiple antennas and a single antenna are proposed. The phase center of the receiving antenna of the single-antenna receiver is almost unchanged, errors such as inconsistent amplitude-phase characteristics of multi-array element channels do not exist, the positioning precision is high, the hardware space cost is low, and the method is suitable for high-precision and small platforms.

The transform domain interference detection and suppression method suitable for the single antenna receiver is still a research hotspot. The conventional single antenna method is to convert the received signal into time-frequency domain, detect the interference parameter, and then eliminate the interference component by using a filter or a blanking technology. Typical time-frequency transformation methods include: Short-Time Fourier Transform (STFT), Wavelet Transform (WT), Wigner-Ville Distribution (WVD), Fractional Fourier Transform (FrFT). The FrFT method has high estimation accuracy, but when different chirp rate interferences with different lengths appear in a window, the aggregation degree of a fractional domain can be influenced, and meanwhile, the loss of useful signals can be increased through multiple times of fractional domain processing.

Therefore, how to increase the aggregation degree of interference and increase the accuracy of interference suppression, and further increase the adaptive capacity to the piecewise chirp interference signal is an urgent technical problem to be solved.

Disclosure of Invention

In view of the foregoing prior art, the technical problem to be solved by the present invention is to provide a piecewise chirp interference cancellation method based on adaptive windowing for a single antenna receiver, which improves the aggregation degree of interference in a fractional order domain, reduces the overlapping degree of interference and a desired signal, and has less damage to the desired signal when eliminating interference components.

In order to solve the technical problem, the invention provides a piecewise linear frequency modulation interference elimination method based on self-adaptive windowing, which comprises the following steps:

step 1: performing radio frequency processing and digital processing on a radio frequency signal received by a single antenna receiver to obtain a digital received signal vector X of N sampling points; x ═ X (1) X (2), …, X (n), …, X (n)]^TWhere N is 1,2, … N, N representing the sampling instant, [ ·]^TRepresenting a transpose;

step 2: obtaining a difference map of signal X, denoted as D (n) ═ X (n) — X (n-1), and multiplying the conjugate of D (n) obtained by itself to obtain | D (n) harin²＝D(n)·D^*(n),(·)^*Representing conjugation, |, representing a modulus value;

and step 3: | D (n) | ventilated wind²Multiplying a sliding window with Q as the window center, the window length being 2Q +1, taking the median value of the values in the window at each moment, i.e. D_m(q)²＝Med[|D(n)|²W_q(n)]Wherein

And 4, step 4: to D_m(q)²Obtaining Δ D (q) by second difference²：ΔD(q)²＝D_m(q)²-D_m(q-1)²Detection of Δ D (q)²The periodicity of the sequence of (a);

and 5: segmenting the received signal according to the obtained interference estimation information, and then eliminating interference components in each segment of data; and then removing residual interference from each processed signal segment.

The invention also includes:

1. detection in step 4D (q)²The periodicity of the sequence in (a) is specifically:

step 4.1: initializing threshold valuesBeta is a set threshold weight value,representing the mean, identifying local peaks exceeding a threshold as valid peaks, and recording the position of these peaks as P_iIf P is present_i-P_i-1＜H_i，H_iFor a given minimum cycle length threshold, P is deleted_i-1(ii) a ComputingObtaining a cycle start and stop position estimateThe lower corner mark d is 1,2, · · v, d is the period component label, v is the total number of periods;

step 4.2: obtaining linear frequency modulation interference period length estimated value And to the signalIs segmented to obtain a segmented signal X_d(n)；

Step 4.3: local X is converted by discrete polynomial method_d(n) conversion of the signal to M_d(n)，M_d(n)＝X_d(n)X_d ^*(n-a)，a is the set time delay length, L is the set center extraction length, and calculation is carried outObtaining coarse estimation value of linear frequency modulation interference frequency modulation rate

2. Step 5, segmenting the received signal according to the obtained interference estimation information, and then eliminating the interference component in each segment of data; then, removing residual interference from each processed segment of signals specifically comprises:

step 5.1: at X_d(n) adding w/4 zeros in tandem, whereink is an adjustable numerical value, and when the w value exceeds the given single maximum processing point number, k is adjusted to enable w to be smaller than a given value;

step 5.2: for the segmented signals after zero padding, a Blackman window with the length of w is lengthened, and the window position isObtaining a windowed Signal X'_d(n)；

Step 5.3: to X'_d(n) if d is 1, go to step 5.4, otherwise, frequency modulation estimate is madeAndthe inner estimates are compared in sequence, the lower subscript i being the index of the component used for comparison, if anyLet p be_d＝p_i，p_iIs [ p ]₁ p₂ ...,p_i ...,p_d-1]If not, the step 5.4 is carried out;

step 5.4: by passingCoarse estimation of optimal order According toObtaining optimal order p using dichotomy scanning in given range_d；

Step 5.5: to X'_d(n) by carrying out p_dFractional Fourier transform, extracting fractional threshold value by mean value statisticsP is given threshold weight, interference energy is removed by using a spectral line cutting method, and a processed signal F is obtained_d(n)；

Step 5.6: to F_d(n) by carrying out p_dThe order fractional order Fourier inverse transformation discards the samples of each w/4 before and after the order fractional order Fourier inverse transformation to obtain the processed signal Y_d(n) and removing the residual pulse in the time domain by using a spectral line cutting method.

The invention has the beneficial effects that: the invention provides a piecewise linear frequency modulation interference elimination method based on self-adaptive windowing, which is suitable for a single-antenna satellite navigation receiver and aims to solve the problems that the existing linear frequency modulation interference detection and suppression algorithm suitable for the single-antenna satellite navigation receiver has large damage to an expected satellite signal and has poor suppression effect on a variable-modulation-frequency piecewise linear frequency modulation interference signal. Firstly, by utilizing the characteristic that a difference graph of a periodic linear frequency modulation signal is still a periodic function and has monotonicity in a period, a method for estimating periodic parameters based on the difference graph is provided, the influence of noise and frequency mutation on an estimation result is reduced by using a method of local window median processing, and the frequency modulation period length, the frequency modulation starting and stopping position and the frequency modulation rate change condition of a periodic frequency modulation component in a received signal are estimated; and for the segmented received signals, carrying out front and back zero filling and then carrying out self-adaptive windowing processing according to the data length, ensuring the aggregation degree of interference in a fractional domain, and finally completing interference suppression through a fractional domain combined time domain. The invention can segment the interference of different modulation frequencies, prevent continuous frequency modulation interference of multiple modulation frequencies in the same window, reduce the overlapping degree of the expected signal and the interference signal, and reduce the influence of phase jump and multiple fractional domain processing in the period on the expected signal. The method is suitable for the single-tone frequency and tone-changing frequency piecewise linear frequency modulation interference scene.

Drawings

Fig. 1 is a schematic block diagram of a tone-varying frequency chirp interference cancellation method based on adaptive windowing.

Detailed Description

The invention is further described with reference to the drawings and the detailed description.

The method comprises the steps of carrying out differential map interference parameter estimation on received data, and obtaining the frequency modulation period length, the frequency modulation starting and stopping position and the frequency modulation rate change condition of a period frequency modulation component; the method comprises the steps of segmenting received signals according to cycle information of frequency modulation interference, carrying out zero filling on the segmented signals, adaptively adjusting window length required by fractional Fourier transform to improve the aggregation degree of the interference in a fractional order domain, finally obtaining the signals after the interference is eliminated through fractional domain interference suppression, and removing residual interference in a time domain. The specific implementation steps are as follows:

(1) performing radio frequency processing and digital processing on radio frequency signals received by a single antenna receiver to obtain digital received signal vectors X of N sampling points; x ═ X (1) X (2), …, X (n), …, X (n)]^TWhere N is 1,2, … N, N representing the sampling instant, [ ·]^TRepresenting a transpose;

(2) obtaining a differential diagram of the signal X, wherein the differential diagram is marked as D (n) ═ X (n) — X (n-1), and n is the number of sampling points, and taking the conjugate of the obtained D (n) and multiplying the conjugate with itself to obtain | D (n) (+) viable count²＝D(n)D^*(n)，(·)^*Representing conjugation, |, representing a modulus value;

(3)|D(n)|²multiplying by a sliding window with a window centre of Q and a window length of 2Q +1, taking the median value of the values in the window at each instant, i.e. D_m(q)²＝Med[|D(n)|²W_q(n)]Wherein

(4) Detection of Δ D (q)²The sequence in (1) has periodicity, and the specific steps are as follows:

(ii) pair D_m(q)²Obtaining Δ D (q) by second difference²：ΔD(q)²＝D_m(q)²-D_m(q-1)²；

② initialization threshold valueBeta (12 < beta < 16) is a threshold weight,representing the mean, identifying local peaks exceeding a threshold as valid peaks, and recording the position of these peaks as P_iIf P is present_i-P_i-1＜H_i，H_iFor a given minimum cycle length threshold, P is deleted_i-1(ii) a ComputingObtaining a cycle start and stop position estimateThe subscript d is 1,2, · · v, d is the period component index, v is the total number of periods.

Third, obtaining the estimated value of the period length of the linear frequency modulation interference And to the signalIs segmented to obtain a segmented signal X_d(n)；

Fourthly, local X is converted by a discrete polynomial method_d(n) conversion of the signal to M_d(n)，M_d(n)＝X_d(n)X_d ^*(n-a) herea is a set time delay length, L is a set time delay lengthCenter extraction length, calculatingObtaining coarse estimation value of linear frequency modulation interference frequency modulation rate

(5) Segmenting the received signal according to the obtained interference estimation information, and then eliminating interference components in each segment of data; then removing residual interference from each section of processed signals, and specifically comprising the following steps:

at X_d(n) adding w/4 zeros in tandem, whereink is an adjustable integer value; when the value of w exceeds a given single maximum number of processing points, k is adjusted so that w is less than a given value.

Secondly, for the segmented signals after zero padding, the Blackman window with the length of w is lengthened, and the window position isObtaining a windowed Signal X'_d(n)；

③ to X'_d(n), if d is 1, go to the step (r), otherwise, the estimated frequency modulation value is obtainedAndthe inner estimates are compared in sequence, the lower subscript i being the index of the component used for comparison, if anyLet p be_d＝p_i，p_iIs [ p ]₁ p₂ ...,p_i ...,p_d-1]If not, the corresponding value is transferred to the fourth step;

fourthly, passing throughCoarse estimation of optimal order According toObtaining optimal order p using dichotomy scanning in given range_d。

V to X'_d(n) by carrying out p_dFractional Fourier transform, extracting fractional threshold value by mean value statisticsP(2<P<4) Removing interference energy by using a spectral line cutting method as a threshold weight value to obtain a processed signal F_d(n)。

Sixthly, to F_d(n) by carrying out p_dThe order fractional order Fourier inverse transformation discards the samples of each w/4 before and after the order fractional order Fourier inverse transformation to obtain the processed signal Y_d(n) and removing the residual pulse in the time domain by using a spectral line cutting method.

Specific examples are given below with reference to specific parameters:

the embodiment of the application provides a piecewise linear frequency modulation interference elimination method based on self-adaptive windowing according to monotonicity of a difference image of periodic linear frequency modulation interference. The method aims at the problem that the interference suppression is influenced by various aggregation peaks of the piecewise linear frequency modulation interference in a fractional domain, and estimates interference parameters and processes signals in a grouping mode by using a frequency modulation period estimation method based on a difference graph. The method can carry out zero filling on the piecewise linear frequency modulation interference in a piecewise mode, reduces the overlapping degree of fractional domain interference and the expected signal, and has small damage to the expected signal when eliminating interference components.

In order to more clearly explain the applied method, the embodiment of the present application performs the process description and the effect display through the simulation experiment, but notLimiting the scope of the embodiments of the present application. The experimental conditions were: the method comprises the steps that 20 periods of continuous linear frequency modulation interference signals and 1 satellite navigation signal are subjected to radio frequency, down-conversion and digital processing, the signal-to-noise ratio (SNR) of the satellite navigation signal is-20 dB, the interference-to-noise ratio (INR) of five continuous interference signals is 40dB, and other parameters of the interference signals are shown in a table 1; the down-converted center frequency of the receiver is 1.25MHz, and the digital sampling frequency is 10.24 MHz. The maximum value of the period is set to 70 mus, and the longest data processing length is 2⁶And (4) sampling points.

FIG. 1 is a schematic block diagram of a method of the present invention, comprising:

s110, digital receiving signals:

x＝[x(1) x(2),…,x(n),…,x(N)]^T

where N is 1,2, … N, representing the sampling time, [ · N]^TDenotes transposition, x (n) denotes a received signal model:

wherein j_kWhere (n) is the kth cyclic frequency signal, e.g., d 1, 2.

S120, obtaining the differential image through continuous differential processing to estimate the period information of the interference signal, and the specific steps are as follows:

calculating d (n) ═ X (n) — X (n-1), multiplying self-conjugation to obtain | d (n) — non-combustible cells²＝D(n)·D^*(n)；

② taking the median D by using sliding window_m(q)²＝Med[|D(n)|²W_q(n)]WhereinAnd pair D (q)²And (3) secondary difference: delta D (q)²＝D_m(q)²-D_m(q-1)²；

③ extraction threshold(12<β<16) Local peaks exceeding a threshold are identified as valid peaks and the position of these peaks is recorded as P_iIf P is present_i-P_i-1< 50, delete P_i-1. Thereby obtaining a cycle start-stop position estimate

Fourthly, calculating the estimated value of the cycle length And to the signalIs segmented to obtain a segmented signal X_d(n)；

Fifthly, the local X is processed by a discrete polynomial method_d(n) conversion of the signal to M_d(n)，M_d(n)＝X_d(n)X_d ^*(n-8) hereComputing

S130, carrying out self-adaptive windowing on the segmented signals and removing interference, and specifically comprising the following steps:

adding w/4 zeros before and after each signal segment, wherein

② adding Blackman window with length of w, the window position isObtaining a windowed Signal X'_d(n)；

③ to X'_d(n), if d is 1, go to the step (r), otherwise, the estimated frequency modulation value is obtainedAndthe inner estimates are compared in sequence, the lower subscript i being the index of the component used for comparison, if anyLet p be_d＝p_i，p_iIs [ p ]₁ p₂ ...,p_i ...,p_d-1]If not, the corresponding value is transferred to the fourth step;

fourthly, passing throughCoarse estimation of optimal order According toObtaining optimal order p using dichotomy scanning in given range_d。

V to X'_d(n) by carrying out p_dFractional Fourier transform and threshold extractionRemoving interference energy by using a spectral line cutting method to obtain F_d(n)；

Sixthly, to F_dCarry out p_dThe order fractional order Fourier inverse transformation discards the samples of each w/4 before and after the order fractional order Fourier inverse transformation to obtain the processed signal Y_d(n) and removing residual pulse interference in the time domain by using a spectral line cutting method.

After interference cancellation, the noise is driedOutput signal to interference plus noise ratio (SINRo) of output signal at ratio of 40dB_ut) Normalized Mean Square Error (NMSE) of the satellite navigation signal and the original satellite navigation signal, and correlation Acquisition Factor (AF) result of the satellite signal are shown in Table 2, and the comparison method is a traditional overlapping windowing fractional Fourier interference suppression method. The method provided by the invention can effectively reduce the damage of interference suppression processing on the expected satellite signal, effectively eliminate the interference and ensure the working efficiency of the satellite navigation receiver under the condition of multiple interferences.

In summary, according to the method of this embodiment, according to the difference map characteristic of the chirp signal, the sliding window of 9 sampling points is used to reduce the influence of noise and other factors on the determination result, and the navigation signal containing the piecewise chirp interference is subjected to the piecewise processing. The method can concentrate the interference energy in one segment in the same segment, reduces the search times of the optimal order of the fractional domain, reduces the overlapping degree of the interference and the expected signal in the fractional domain, and has less damage to the expected signal when eliminating the interference component.

TABLE 1 interference Signal parameters

Name (R)	Type (B)	Starting frequency	Frequency modulation	Frequency modulation period
					1	Linear frequency modulation	0.6MHz	20GHZ/S	70.7μs
2	Linear frequency modulation	0.4MHz	30GHZ/S	60.16μs
					3	Linear frequency modulation	0.4MHz	40GHZ/S	60.16μs
4	Linear frequency modulation	0.1MHz	60GHZ/S	50μs
					5	Linear frequency modulation	0.1MHz	80GHZ/S	50μs

TABLE 2 anti-interference effect of the method of the present invention at a dry-to-noise ratio of 40dB

It is understood by those skilled in the art that, in the method according to the embodiments of the present application, the sequence numbers of the steps do not mean the execution sequence, and the execution sequence of the steps should be determined by their functions and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

Finally, it should be noted that the above examples are only intended to describe the technical solutions of the present invention and not to limit the technical methods, the present invention can be extended in application to other modifications, variations, applications and embodiments, and therefore all such modifications, variations, applications, embodiments are considered to be within the spirit and teaching scope of the present invention.