SAR intermittent sampling forwarding interference suppression method and system based on pulse compression and time-frequency analysis, and storage medium

Abstract

The invention discloses an SAR intermittent sampling forwarding interference suppression method and system based on pulse compression and time-frequency analysis, and a storage medium, and the method comprises the following steps: carrying out the range pulse compression of SAR echoes containing interference pulse by pulse, so as to accurately obtain an interference-containing pulse sequence; and then, carrying out short-time Fourier transform on the pulse with interference after pulse compression and the nearest pulse without interference, and carrying out superposition and differential operation of two different dimensions to realize time-frequency domain positioning of the 2D-ISRJ. And designing a time-frequency domain filter, and carrying out time-frequency domain wave trapping on the positioned 2D-ISRJ. And finally, recovering the missing part of the signal after wave trapping by using the time-frequency data containing the interference pulse and the nearest non-interference pulse after pulse compression. According to the invention, the interference detection precision of typical two-dimensional intermittent sampling forwarding interference forwarding types can be relatively high under the condition of relatively weak interference, and the suppression method not only can achieve a relatively good suppression effect, but also can better protect useful signals.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to a SAR intermittent sampling and forwarding interference suppression method, system and storage medium based on pulse compression and time-frequency analysis. Background Technology

[0002] Synthetic Aperture Radar (SAR) is an active microwave imaging device that improves range resolution by transmitting a wide-bandwidth signal and achieves high azimuth resolution by utilizing the equivalent synthesis of a large antenna array through platform motion. Unlike other Earth observation methods such as infrared detection, SAR's unique imaging mechanism gives it advantages such as all-weather, all-day operation, high resolution, certain penetration, and wide mapping range. It has wide applications in fields such as agricultural monitoring, topographic mapping, military reconnaissance, and deep space exploration.

[0003] However, with the continuous emergence of new jamming technologies, especially the widespread application of Digital Radio Frequency Memory (DRFM) technology, jamming devices can quickly acquire radar signals, modulate them, and then retransmit them, effectively achieving suppression and deception jamming effects. This poses a significant challenge to radar target detection and severely impacts radar performance. Interrupted Sampling Repeater Jamming (ISRJ) is an intra-pulse coherent jamming based on DRFM. Its principle is as follows: A small segment of the SAR linear frequency modulated signal is intercepted, sampled with high fidelity, and then retransmitted. The next segment is sampled and retransmitted, and this process continues in a time-division multiplexing manner, alternating between sampling and retransmission until the large-bandwidth signal ends. Furthermore, leveraging the digital processing capabilities of DRFM, ISRJ jammers can flexibly adjust jamming parameters and implement diverse jamming strategies, posing a serious threat to synthetic aperture radar (SAR) systems. In conclusion, the flexibility and adaptability of ISRJ increase the difficulty of radar anti-jamming and significantly impact the normal operation and combat effectiveness of radar systems. Therefore, researching effective methods to counter ISRJ is of significant practical importance.

[0004] However, in current research, traditional methods for combating intermittent sampling-forwarding interference mainly focus on two dimensions: transmitter waveform design and receiver signal processing. Improving the anti-interference performance of transmitter waveform design methods relies on sensing the jammer's operating parameters, which are difficult to obtain in practice. The anti-interference effect of receiver signal processing methods is closely related to the accuracy of interference detection and suppression, requiring high-precision interference detection methods and high-performance interference suppression methods. While existing methods can achieve a certain level of interference suppression under strong interference scenarios, they still have significant limitations under weak interference conditions, directly restricting the effectiveness of interference suppression. Furthermore, existing methods are primarily geared towards traditional detection radars. For two-dimensional imaging radars such as synthetic aperture radar (SAR), due to their unique azimuth characteristics, current methods are difficult to apply, and related research is limited.

[0005] In conclusion, it is urgent and of great practical significance to conduct in-depth research on synthetic aperture radar anti-interference technology for intermittent sampling and forwarding. Summary of the Invention

[0006] The purpose of this application is to provide a SAR intermittent sampling and forwarding interference suppression method, system and storage medium based on pulse compression and time-frequency analysis, so as to solve or alleviate the problems existing in the prior art.

[0007] To achieve the above objectives, this application provides the following technical solution:

[0008] This application provides a SAR intermittent sampling and forwarding interference suppression method based on pulse compression and time-frequency analysis, including: step S101, performing range pulse compression on the echo data pulse by pulse; step S102, calculating the kurtosis coefficient sequence of the compressed pulses pulse by pulse and then thresholding to obtain the interference-containing pulse sequence; step S103, performing short-time Fourier transform on the interference-containing pulses and the nearest non-interference-containing pulses of the compressed pulses pulse by pulse; and step S104, integrating the time-frequency data of the interference-containing pulses and the nearest non-interference-containing pulses along the frequency domain. Step S105: Integrate the interference-containing distance gate intervals corresponding to the above two along the time dimension and perform a difference operation to obtain the interference-containing frequency sampling point interval; Step S106: Merge the interference-containing distance gate intervals and the interference-containing frequency sampling point intervals to obtain the time-frequency domain location of the interference; Step S107: Design a time-frequency domain filter and assign the location of the interference to the average value of the data corresponding to the nearest interference-free distance gate of the above two time-frequency data; Step S108: Perform the above steps one interference pulse at a time to achieve interference suppression.

[0009] Preferably, in step S101, range-direction pulse compression is performed on the echo data pulse by pulse, specifically as follows:

[0010] Assuming the signal of each pulse received by the SAR is represented as s(t), its Fast Fourier Transform (FFT) result can be expressed as:

[0011] S FFT (f)=S s (f)+S j (f)

[0012] Among them, S FFT (f) represents the spectrum obtained after the signal s(t) undergoes FFT, S s (f) represents the spectrum of the useful signal in signal s(t) obtained by FFT, S j (f) represents the spectrum of the interference signal in signal s(t) after FFT.

[0013] Furthermore, the range-matched filter in the frequency domain can be expressed as:

[0014]

[0015] Where B represents the SAR signal bandwidth, K r f represents frequency modulation, and f represents frequency.

[0016] Furthermore, the signal spectrum after pulse compression can be expressed as:

[0017] S PC (f)=S FFT (f)·H(f)

[0018] Then, through S PC (f) Performing the inverse FFT (IFFT) yields the time-domain SAR echo signal after range pulse compression, which can be represented as:

[0019] S PC (t) = IFFT(S) PC (f))

[0020] Finally, by performing the above process pulse by pulse, the data results after range pulse compression can be obtained.

[0021] Preferably, in step S102, the obtained variance sequence is processed using a prominent edge sensing algorithm to obtain an interference-containing pulse sequence, specifically as follows:

[0022] Kurtosis is an important statistical indicator for describing the waveform characteristics of data. Theoretically, it characterizes the sharpness or flatness of the peaks in a data distribution through the central moments of a random variable, providing a quantitative basis for data feature analysis and anomaly identification. Its calculation formula can be expressed as:

[0023]

[0024] Where N represents the total number of data points, X(n) represents the nth data value, μ represents the average of all data samples, ||·|| represents the complex amplitude, and σ represents the standard deviation of ||X||. The moment kurtosis coefficient is the ratio of the fourth central moment to the square of the second central moment. This structure makes it highly sensitive to extreme values ​​in the data, such as outliers and spikes, thus giving it a unique advantage in interference detection.

[0025] Then, the kurtosis value of the pulse compression result is calculated, and the result can be expressed as:

[0026] K(i)=Kur(|s PC (i)| 2 )

[0027] Where i = 1, 2, ..., N a s PC (i) represents the compression result of the i-th pulse.

[0028] By performing the above processing pulse by pulse, the sequence of kurtosis coefficients for all pulses can be obtained, which can be expressed as:

[0029] K PCS =[K(1),K(2),…,K(i),…,K(N a )] T

[0030] Finally, the K-means clustering algorithm was used to cluster the K clusters. PCS Binary segmentation is performed to obtain the final detection result, which can be represented as:

[0031] Φ=[Φ(1),Φ(2),…,Φ(i),…,Φ(N a )] T

[0032] Where Φ(i) represents the detection result of the i-th pulse, and constants 1 and 0 represent the presence of 2D-ISRJ and the absence of 2D-ISRJ, respectively.

[0033] Preferably, in step S103, short-time Fourier transforms are performed on the interference-containing pulse and the nearest interference-free pulse in the obtained pulse compression result, specifically:

[0034] Since STFT is a linear transformation, the STFTs of two signals satisfy the linear superposition property. When both a target and interference are present in the radar received signal, assuming any range pulse signal received by the radar is represented as s(t), its STFT transformation yields:

[0035]

[0036] S(t,f)=Ss (t,f)+S j (t,f)

[0037] Where S(t,f) is the time spectrum obtained after the signal s(t) passes through the STFT, S s (t,f) represents the time spectrum of the target signal in signal s(t) after passing through the STFT, S j (t,f) represents the time spectrum obtained after the interference signal in signal s(t) is passed through STFT.

[0038] By traversing the interference detection results, the pulse containing interference and the nearest interference-free pulse can be found respectively, denoted as S. J (t,f) and The pulse compression results of these pulses are then subjected to STFT, and the results can be expressed as follows:

[0039]

[0040] Among them, S J (t,f) represents the signal The time spectrum obtained after STFT Indicates signal The time spectrum obtained after STFT.

[0041] Preferably, in step S104, the time-frequency data containing the interfering pulse and the nearest non-interfering pulse are integrated along the frequency domain and the difference is calculated to obtain the interference-containing distance threshold interval, specifically:

[0042] The obtained time-frequency function S J (t,f) and The absolute value is squared and integrated along the frequency dimension to obtain the time-varying energy function. The result is then subjected to a difference operation to make the interference more prominent, thereby enabling higher-precision location of the range gate where the interference is located. This can be expressed as:

[0043]

[0044] In addition, the K-means clustering algorithm was used to analyze the Diff. TE (t) Perform binary segmentation and identify the peak positions corresponding to clusters with larger values. For each peak position, track left and right respectively until the boundary where the energy stops decreasing is reached. Merge the intervals defined by the two boundaries to accurately locate the range gate intervals of each interference slice with the same time delay in the time-frequency domain. Record the start and end positions of each interference range gate interval, which can be represented as:

[0045] G s =[g s(1),g s (2),…,g s (i),…,g s (n)]

[0046] G e =[g e (1),g e (2),…,g e (i),…,g e (n)]

[0047] Where n represents the total number of interference range gate intervals, G s and G e The elements correspond one-to-one, representing the start and end positions of each interference distance gate interval.

[0048] Preferably, in step S105, the interference-containing distance gate intervals corresponding to the above two are integrated along the time dimension, and the difference is calculated to obtain the interference-containing frequency sampling point interval, specifically:

[0049] For the time-frequency function S respectively J (t,f) and The time-frequency data for each corresponding interference distance threshold is accumulated along the time dimension. The results are then subjected to differential operations to make the interference more prominent, thereby enabling higher-precision localization of the frequency sampling point where the interference is located. This can be expressed as:

[0050]

[0051] Where i = 1, 2, ..., n, g s (i) and g e (i) represent the start and end positions of each interference distance threshold interval, S J (t,f) and These represent time-frequency data containing interference pulses and data without recent interference pulses, respectively.

[0052] In addition, the K-means clustering algorithm was used to analyze the Diff. FE (f i Binary segmentation is performed to locate the frequency sampling point interval where interference occurs. The start and end positions of each frequency sampling point interval are recorded, which can be represented as:

[0053] Fs s =[f s (1),f s (2),…,f s (i),…,f s (m)]

[0054] F e =[fe (1),f e (2),…,f e (i),…,f e (m)]

[0055] Where m represents the total number of frequency sampling point intervals in the interference slice, and F s and F e The elements correspond one-to-one, representing the start and end positions of the frequency sampling point interval for each interference slice.

[0056] Preferably, in step S106, the interference-containing distance threshold interval and the interference-containing frequency sampling point interval are merged to obtain the time-frequency domain location of the interference, specifically:

[0057] By merging the interference distance threshold interval and the frequency sampling point interval, the interference can be located in the time-frequency domain, which can be expressed as:

[0058]

[0059] Where, N f N represents the number of frequency dimension sampling points. t Indicates the number of doors at a distance. i (j) = 1 indicates a point containing interference, p i (j) = 0 indicates that there are no interfering points.

[0060] Preferably, in step S107, a time-frequency domain filter is designed, and the location of the interference is assigned the average value of the data corresponding to the nearest interference-free distance gate of the two time-frequency data. Specifically:

[0061] First, a time-frequency domain filter is designed based on the obtained interference time-frequency domain localization results to perform notch filtering, which can be expressed as:

[0062]

[0063] Where γ(i,j) represents the interference-free signal at position (i,j), i = 1, 2, ... N t j = 1, 2, ..., N f .

[0064] Secondly, for each interference-containing distance gate match, find the nearest interference-free distance gate match and record its index, represented as:

[0065] M=[m(1),m(2),…,m(j),…,m(sum)]

[0066] Where j represents the j-th range gate with interference, m(j) represents the index of the nearest interference-free range gate matched by the j-th range gate with interference, and sum represents the total number of range gates with interference in the time-frequency domain.

[0067] Furthermore, extract the data containing the interference range gate and the nearest interference-free range gate, denoted as S respectively. J (m(j),f) and Then calculate the average value of the corresponding elements, which can be expressed as:

[0068]

[0069] The obtained results are then used to compensate for the missing information at the notch location, and can be expressed as:

[0070]

[0071] Finally, after performing the above process sequentially at the distance gate at each notch location, an interference-free pulse can be obtained by performing inverse STFT and inverse pulse compression.

[0072] Preferably, in step S108, the above steps are performed on each interference pulse to achieve interference suppression, specifically as follows:

[0073] The above process is executed sequentially for each pulse containing interference until the traversal is complete, thus obtaining the raw SAR echo data without 2D-ISRJ.

[0074] This application also provides a SAR intermittent sampling and forwarding interference suppression system based on pulse compression and time-frequency analysis, comprising: a feature extraction unit configured to perform range pulse compression pulse by pulse from synthetic aperture radar data input to the system, and then calculate and obtain a kurtosis coefficient sequence pulse by pulse; an interference pulse detection unit configured to perform binary segmentation of the obtained kurtosis coefficient sequence using a K-means clustering algorithm to accurately obtain the interference pulse sequence; and an interference slice localization unit configured to perform a short-time Fourier transform on the pulse-compressed interference pulse and the nearest non-interference pulse. Then, through two superposition and differential operations of different dimensions, the time-frequency domain positioning of 2D-ISRJ is achieved; the time-frequency domain filter design unit is configured to design a time-frequency domain filter for the time-frequency domain location of the interference, that is, the data of its non-interference location remains unchanged, and the interference location is the average of the data corresponding to the nearest interference-free distance gate of the time-frequency data of the pulse compression containing the interference and the nearest interference-free pulse; the interference suppression unit is configured to multiply the time-frequency distribution of the filter response for each interference pulse, and then perform inverse STFT and inverse pulse compression in sequence to obtain interference-free SAR echo data.

[0075] This application also provides a computer-readable storage medium storing a computer program thereon, the program being the SAR intermittent sampling and forwarding interference suppression method based on pulse compression and time-frequency analysis as described above.

[0076] This application also provides an electronic device, including: a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the SAR intermittent sampling and forwarding interference suppression method based on pulse compression and time-frequency analysis as described above.

[0077] Beneficial effects:

[0078] The SAR intermittent sampling forwarding interference suppression method based on pulse compression and time-frequency analysis provided in this application firstly performs range-direction pulse compression on each pulse of the interfering SAR echo, calculates and obtains the kurtosis coefficient sequence, and then performs threshold segmentation to accurately obtain the interfering pulse sequence. Subsequently, a short-time Fourier transform is performed on the pulse-compressed interfering pulse and the nearest interference-free pulse. Then, through two superposition and difference operations in different dimensions, the time-frequency domain location of 2D-ISRJ is achieved. A time-frequency domain filter is then designed to perform time-frequency domain notch filtering on the located 2D-ISRJ. Finally, the time-frequency data of the pulse-compressed interfering pulse and the nearest interference-free pulse are used to recover the missing signal portion after notch filtering. This invention achieves high interference detection accuracy for typical two-dimensional intermittent sampling forwarding interference types such as 2D-ISDRJ, 2D-ISPRJ, and 2D-ISCRJ under weak interference conditions. Furthermore, the suppression method not only achieves good suppression effects but also better protects useful signals. Attached Figure Description

[0079] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. Wherein:

[0080] Figure 1 This is a schematic diagram of the process of the present invention;

[0081] Figure 2 These are experimental comparison images described in the embodiments of the present invention;

[0082] Figure 3 This is a unit configuration diagram according to this application. Detailed Implementation

[0083] The present application will now be described in detail with reference to the accompanying drawings and embodiments. Various examples are provided by way of explanation and not by way of limitation. In fact, those skilled in the art will recognize that modifications and variations can be made to the present application without departing from the scope or spirit thereof. For example, a feature shown or described as part of one embodiment may be used in another embodiment to produce yet another embodiment. Therefore, it is desirable that the present application encompass such modifications and variations that fall within the scope of the appended claims and their equivalents.

[0084] Exemplary methods

[0085] like Figure 1 As shown, the SAR intermittent sampling and forwarding interference suppression method based on pulse compression and time-frequency analysis includes:

[0086] In step S101, range-direction pulse compression is performed on each pulse of the echo data;

[0087] Specifically: Assuming the signal of each pulse received by the SAR is represented as s(t), its Fast Fourier Transform (FFT) result can be expressed as:

[0088] S FFT (f)=S s (f)+S j (f)

[0089] Among them, S FFT (f) represents the spectrum obtained after the signal s(t) undergoes FFT, S s (f) represents the spectrum of the useful signal in signal s(t) obtained by FFT, S j (f) represents the spectrum of the interference signal in signal s(t) after FFT.

[0090] Furthermore, the range-matched filter in the frequency domain can be expressed as:

[0091]

[0092] Where B represents the SAR signal bandwidth, K r Indicates frequency modulation.

[0093] Furthermore, the signal spectrum after pulse compression can be expressed as:

[0094] S PC (f)=S FFT (f)·H(f)

[0095] Then, through S PC (f) Performing the inverse FFT (IFFT) yields the time-domain SAR echo signal after range pulse compression, which can be represented as:

[0096] s PC (t) = IFFT(S) PC (f))

[0097] Finally, by performing the above process pulse by pulse, the data results after range pulse compression can be obtained.

[0098] In step S102, the compressed pulse result is calculated pulse by pulse to obtain the kurtosis coefficient sequence, and then threshold segmentation is performed to obtain the pulse sequence containing interference.

[0099] Specifically, the kurtosis coefficient is an important statistical indicator describing the waveform characteristics of data. Theoretically, it characterizes the sharpness or flatness of the peaks in a data distribution through the central moments of a random variable, providing a quantitative basis for data feature analysis and anomaly identification. Its calculation formula can be expressed as:

[0100]

[0101] Where N represents the total number of data points, X(n) represents the nth data value, μ represents the average of all data samples, ||·|| represents the complex amplitude, and σ represents the standard deviation of ||X||. The moment kurtosis coefficient is the ratio of the fourth central moment to the square of the second central moment. This structure makes it highly sensitive to extreme values ​​in the data, such as outliers and spikes, thus giving it a unique advantage in interference detection.

[0102] Then, the kurtosis value of the pulse compression result is calculated, and the result can be expressed as:

[0103] K(i)=Kur(|S PC (i)| 2 )

[0104] Where i = 1, 2, ..., N a s PC (i) represents the compression result of the i-th pulse.

[0105] By performing the above processing pulse by pulse, the sequence of kurtosis coefficients for all pulses can be obtained, which can be expressed as:

[0106] K PCS =[K(1),K(2),…,K(i),…,K(N a )] T

[0107] Finally, the K-means clustering algorithm was used to cluster the K clusters. PCS Binary segmentation is performed to obtain the final detection result, which can be represented as:

[0108] Φ=[Φ(1),Φ(2),…,Φ(i),…,Φ(N a )] T

[0109] Where Φ(i) represents the detection result of the i-th pulse, and constants 1 and 0 represent the presence of 2D-ISRJ and the absence of 2D-ISRJ, respectively.

[0110] In step S103, short-time Fourier transforms are performed on the interference-containing pulse and the nearest interference-free pulse in the obtained pulse compression result, respectively.

[0111] Specifically: Since STFT is a linear transformation, the STFTs of two signals satisfy the linear superposition property. When both a target and interference are present in the radar received signal, assuming any range pulse signal received by the radar is represented as s(t), performing an STFT transformation on it yields:

[0112]

[0113] S(t,f)=S s (t,f)+S j (t,f)

[0114] Where S(t,f) is the time spectrum obtained after the signal s(t) passes through the STFT, S s (t,f) represents the time spectrum of the target signal in signal s(t) after passing through the STFT, S j (t,f) represents the time spectrum obtained after the interference signal in signal s(t) is passed through STFT.

[0115] By traversing the interference detection results, the pulse containing interference and the nearest interference-free pulse can be found respectively, denoted as S. J (t,f) and The pulse compression results of these pulses are then subjected to STFT, and the results can be expressed as follows:

[0116]

[0117] Among them, S J (t,f) represents the signal The time spectrum obtained after STFT Indicates signal The time spectrum obtained after STFT.

[0118] In step S104, the time-frequency data containing the interference pulse and the nearest non-interference pulse are integrated along the frequency domain and the difference is calculated to obtain the interference distance gate interval;

[0119] Specifically: For the obtained time-frequency function S... J (t,f) and The absolute value is squared and integrated along the frequency dimension to obtain the time-varying energy function. The result is then subjected to a difference operation to make the interference more prominent, thereby enabling higher-precision location of the range gate where the interference is located. This can be expressed as:

[0120]

[0121] In addition, the K-means clustering algorithm was used to analyze the Diff. TE (t) Perform binary segmentation and identify the peak positions corresponding to clusters with larger values. For each peak position, track left and right respectively until the boundary where the energy stops decreasing is reached. Merge the intervals defined by the two boundaries to accurately locate the range gate intervals of each interference slice with the same time delay in the time-frequency domain. Record the start and end positions of each interference range gate interval, which can be represented as:

[0122] G s =[g s (1),g s (2),…,g s (i),…,g s (n)]

[0123] G e =[g e (1),g e (2),…,g e (i),…,g e (n)]

[0124] Where n represents the total number of interference range gate intervals, G s and G e The elements correspond one-to-one, representing the start and end positions of each interference distance gate interval.

[0125] In step S105, the interference-containing distance gate intervals corresponding to the above two are integrated along the time dimension, and the difference is calculated to obtain the interference-containing frequency sampling point intervals.

[0126] Specifically: for the time-frequency function S J (t,f) and The time-frequency data for each corresponding interference distance threshold is accumulated along the time dimension. The results are then subjected to differential operations to make the interference more prominent, thereby enabling higher-precision localization of the frequency sampling point where the interference is located. This can be expressed as:

[0127]

[0128] Where i = 1, 2, ..., n, g s (i) and g e (i) represent the start and end positions of each interference distance threshold interval, S J (t,f) and These represent time-frequency data containing interference pulses and data without recent interference pulses, respectively.

[0129] In addition, the K-means clustering algorithm was used to analyze the Diff. FE(f i Binary segmentation is performed to locate the frequency sampling point interval where interference occurs. The start and end positions of each frequency sampling point interval are recorded, which can be represented as:

[0130] F s =[f s (1),f s (2),…,f s (i),…,f s (m)]

[0131] F e =[f e (1),f e (2),…,f e (i),…,f e (m)]

[0132] Where m represents the total number of frequency sampling point intervals in the interference slice, and F s and F e The elements correspond one-to-one, representing the start and end positions of the frequency sampling point interval for each interference slice.

[0133] In step S106, the distance gate interval containing interference and the sampling point interval containing interference are merged to obtain the time-frequency domain location of the interference;

[0134] Specifically: By merging the interference distance threshold interval and the frequency sampling point interval, the interference can be located in the time-frequency domain, which can be expressed as:

[0135]

[0136] Where, N f N represents the number of frequency dimension sampling points. t Indicates the number of doors at a distance. i (j) = 1 indicates a point containing interference, p i (j) = 0 indicates that there are no interfering points.

[0137] In step S107, a time-frequency domain filter is designed, and the location of the interference is assigned the average value of the data corresponding to the nearest interference-free distance gate of the time-frequency data of the two above.

[0138] Specifically: First, a time-frequency domain filter is designed based on the obtained interference time-frequency domain localization results to perform notch filtering, which can be expressed as:

[0139]

[0140] Where γ(i,j) represents the interference-free signal at position (i,j), i = 1, 2, ... N t j = 1, 2, ..., N f .

[0141] Secondly, for each interference-containing distance gate match, find the nearest interference-free distance gate match and record its index, represented as:

[0142] M=[m(1),m(2),…,m(j),…,m(sum)]

[0143] Where j represents the j-th range gate with interference, m(m) represents the index of the nearest interference-free range gate matched by the j-th range gate with interference, and sum represents the total number of range gates with interference in the time-frequency domain.

[0144] Furthermore, extract the data containing the interference range gate and the nearest interference-free range gate, denoted as S respectively. J (m(j),f) and Then calculate the average value of the corresponding elements, which can be expressed as:

[0145]

[0146] The obtained results are then used to compensate for the missing information at the notch location, and can be expressed as:

[0147]

[0148] Finally, after performing the above process sequentially at the distance gate at each notch location, an interference-free pulse can be obtained by performing inverse STFT and inverse pulse compression.

[0149] Step S108: Perform the above steps for each interference pulse to achieve interference suppression;

[0150] Specifically: The above process is executed sequentially for each interfering pulse until the entire process is completed, thus obtaining the raw SAR echo data without 2D-ISRJ.

[0151] like Figure 2 As shown, the experimental comparison diagrams of this application are as follows: the images in the leftmost column are experimental data containing 2D-ISDRJ, 2D-ISPRJ, and 2D-ISCRJ respectively; the images in the middle two columns are the processing results of existing methods; and the images in the rightmost column are the processing results of this invention. As can be seen from the comparison of the above results, under the same scene data conditions, the suppression effect of this invention on data containing 2D-ISDRJ, 2D-ISPRJ, and 2D-ISCRJ is more significant.

[0152] Exemplary System

[0153] Figure 3This application also provides a SAR intermittent sampling and forwarding interference suppression system based on pulse compression and time-frequency analysis, comprising: a feature extraction unit configured to perform range pulse compression pulse by pulse from synthetic aperture radar data input to the system, and then calculate and obtain a kurtosis coefficient sequence pulse by pulse; an interference pulse detection unit configured to perform binary segmentation of the obtained kurtosis coefficient sequence using a K-means clustering algorithm to accurately obtain the interference pulse sequence; and an interference slice localization unit configured to perform a short-time Fourier transform on the pulse-compressed interference pulse and the nearest non-interference pulse. Then, through two superposition and differential operations of different dimensions, the time-frequency domain positioning of 2D-ISRJ is achieved; the time-frequency domain filter design unit is configured to design a time-frequency domain filter for the time-frequency domain location of the interference, that is, the data of its non-interference location remains unchanged, and the interference location is the average of the data corresponding to the nearest interference-free distance gate of the time-frequency data of the pulse compression containing the interference and the nearest interference-free pulse; the interference suppression unit is configured to multiply the time-frequency distribution of the filter response for each interference pulse, and then perform inverse STFT and inverse pulse compression in sequence to obtain interference-free SAR echo data.

[0154] The SAR intermittent sampling and forwarding interference suppression system based on pulse compression and time-frequency analysis provided in this application can implement any of the above-mentioned SAR intermittent sampling and forwarding interference suppression steps and processes, and achieve the same technical effect, which will not be described in detail here.

[0155] Exemplary device

[0156] This application provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it causes the device containing the computer-readable storage medium to perform the SAR intermittent sampling and forwarding interference suppression method based on pulse compression and time-frequency analysis as described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), random access memory, and other memories.

[0157] An electronic device includes a storage device and a processor. The processor is adapted to execute various programs. The memory is used to store multiple programs. When the memory executes the programs on the processor, it implements the SAR intermittent sampling and forwarding interference suppression method based on pulse compression and time-frequency analysis as described above.

[0158] If the modules / units integrated in the electronic device described in this application are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0159] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0160] The computer-readable storage medium stores computer-readable instructions, which are executed by a processor in an electronic device to implement the SAR intermittent sampling and forwarding interference suppression method based on pulse compression and time-frequency analysis as described in any of the above embodiments.

[0161] Since the SAR intermittent sampling and forwarding interference suppression steps based on pulse compression and time-frequency analysis have been described in detail in the specific implementation method examples, they will not be repeated here.

[0162] The processor includes a Central Processing Unit (CPU), a Network Processor (NP), etc., and can also be a digital signal processor, an application-specific integrated circuit, an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor.

[0163] Specifically, the processor can be configured as follows: It performs range pulse compression pulse by pulse from the synthetic aperture radar data input from the system, then calculates and obtains the kurtosis coefficient sequence pulse by pulse; it performs binary segmentation of the obtained kurtosis coefficient sequence using the K-means clustering algorithm to accurately obtain the interference-containing pulse sequence; it performs a short-time Fourier transform on the compressed interference-containing pulse and the nearest interference-free pulse, and then achieves time-frequency domain localization of the 2D-ISRJ through two superposition and difference operations of different dimensions; it designs a time-frequency domain filter for the time-frequency domain location of the interference, i.e., its non-interference location data remains unchanged, and the interference location is the average of the data corresponding to the nearest interference-free range gate of the time-frequency data of the compressed interference-containing pulse and the nearest interference-free pulse; it multiplies the time-frequency distribution of the interference-containing pulse using the filter response, and then sequentially performs inverse STFT and inverse pulse compression to obtain interference-free SAR echo data.

[0164] It should be noted that, depending on the implementation needs, the various components / steps described in the embodiments of this application can be broken down into more components / steps, or two or more components / steps or parts of the operation of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of this application.

[0165] The methods described in the embodiments of this application can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code downloaded over a network that is originally stored in a remote recording medium or a non-transitory machine storage medium and will be stored in a local recording medium. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the single-track synthetic aperture radar jamming source localization method described herein is implemented. Furthermore, when a general-purpose computer accesses the code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0166] like Figure 2As shown, the experimental comparison diagrams of this application are as follows: the images in the leftmost column are experimental data containing 2D-ISDRJ, 2D-ISPRJ, and 2D-ISCRJ respectively; the images in the middle two columns are the processing results of existing methods; and the images in the rightmost column are the processing results of this invention. As can be seen from the comparison of the above results, under the same scene data conditions, the suppression effect of this invention on data containing 2D-ISDRJ, 2D-ISPRJ, and 2D-ISCRJ is more significant.

[0167] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application of the technical solution and the constraints involved. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application.

[0168] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments.

[0169] The device and system embodiments described above are merely illustrative. The units referred to as separate entities may or may not be physically separate. The entities mentioned as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0170] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for SAR intermittent sampling repeater jamming suppression based on pulse compression and time-frequency analysis, characterized in that, Comprise: Step S101, the echo data is compressed in distance direction pulse by pulse; Step S102, the obtained pulse compression result is calculated pulse by pulse to obtain the sequence of kurtosis coefficient, and the sequence of interference pulse is obtained after threshold segmentation; Step S103, the short time Fourier transform is carried out on the interference pulse and the nearest non-interference pulse of the obtained pulse compression result respectively; Step S104, the time-frequency data of the interference pulse and the nearest non-interference pulse are integrated along the frequency domain, and the difference operation is carried out to obtain the corresponding interference distance gate interval; Step S105, the interference frequency sampling point interval is obtained by integrating the corresponding interference distance gate interval of the above two along the time dimension and carrying out difference operation; Step S106, the interference time-frequency domain positioning is obtained by merging the interference distance gate interval and the interference frequency sampling point interval; Step S107, a time-frequency domain filter is designed, and the data mean value of the position corresponding to the nearest non-interference distance gate of the time-frequency data of the two is assigned to the position of the interference; Step S108, the above steps are executed for each interference pulse to realize interference suppression.

2. The method of claim 1, wherein the pulse compression and time-frequency analysis based SAR intermittent sampling repeater jamming mitigation method is characterized by, In step S101, the echo data is compressed in distance direction pulse by pulse, specifically: Assume that the signal of each pulse received by the SAR is represented as s(t), and the fast Fourier transform (FFT) result is represented as: S FFT (f) = S s (f) + S j (f) where S FFT (f) represents the spectrum obtained after FFT of the signal d(t), S s (f) represents the spectrum obtained after FFT of the signal s(t), S j (f) represents the spectrum obtained after FFT of the signal s(t), S In addition, the distance direction matching filter in the frequency domain can be represented as: where B denotes the SAR signal bandwidth, K r denotes the frequency modulation rate, and f denotes the frequency; Furthermore, the signal spectrum after pulse compression is represented as: S PC (f) = S FFT (f) · H(f) Then, over S PC (f) Perform inverse FFT, i.e. IFFT, to obtain the range-compressed time-domain SAR echo signal, denoted as: S PC (t) = IFFT(S PC (f)) Finally, after executing the above process in a pulse-by-pulse manner, the data result after distance direction pulse compression can be obtained.

3. The method of claim 1, wherein the method is based on pulse compression and time-frequency analysis for SAR inter-persistent-sampling repeater jamming suppression. In step S102, the obtained pulse compression result is calculated pulse by pulse to obtain the sequence of kurtosis coefficient, and the sequence of interference pulse is obtained after threshold segmentation, specifically: The calculation formula of the kurtosis coefficient is represented as: Wherein, N represents the total number of data points, X(n) represents the nth data value, μ represents the average value of all data samples, ‖·‖ represents the complex amplitude, and σ represents the standard deviation of ‖X‖; the kurtosis coefficient is the ratio of the fourth order central moment to the square of the second order central moment; Then the kurtosis value of the pulse compression result is calculated, and the result is represented as: K(i) = Kur(|s PC (i)| 2 ) where i = 1, 2,... N a , S PC (i) denotes the i-th pulse compression result; By executing the above processing pulse by pulse, the sequence of kurtosis coefficients of all pulses can be obtained, represented as: K PCS = [K(1), K(2),..., K(i),..., K(N a )] T Finally, K-means clustering algorithm is used to cluster K PCS The final detection result is obtained by binary segmentation, and is represented as: Φ = [Φ(1), Φ(2),..., Φ(i),..., Φ(N a )] T Wherein, Φ(i) represents the detection result of the ith pulse, and the constants 1 and 0 represent 2D-ISRJ and no 2D-ISRJ respectively.

4. The method of claim 1, wherein the pulse compression and time-frequency analysis based SAR intermittent sampling repeater jamming mitigation method is characterized by, In step S103, the short time Fourier transform is carried out on the interference pulse and the nearest non-interference pulse of the obtained pulse compression result respectively, specifically: When there are targets and interferences in the radar received signal at the same time, assume that any distance direction pulse signal received by the radar is represented as s(t), and the STFT transform can be obtained: S(t,f) = S s (t,f) + S j (t,f) Wherein, S(t,f) is the time-frequency spectrum obtained by STFT of the signal s(t), S s (t,f) represents the time-frequency spectrum obtained by STFT of the target signal in the signal s(t), S j (t,f) represents the time-frequency spectrum obtained by STFT of the interference signal in the signal s(t). By traversing the interference detection results, the interference pulse and the nearest non-interference pulse are found respectively, which are denoted as S J (t,f) and Their pulse compression results are respectively executed STFT, and the results can be expressed as: where S J (t,f) represents the signal time-frequency spectrum obtained after STFT, represents the signal time-frequency spectrum obtained after STFT.

5. The method of claim 1, wherein the pulse compression and time-frequency analysis based SAR intermittent sampling repeater jamming mitigation method is characterized by, In step S104, the time-frequency data of the interference pulse and the nearest non-interference pulse are integrated along the frequency domain, and the difference operation is carried out to obtain the interference distance gate interval, specifically: The time-frequency function S J (t,f) and The square of the absolute value is calculated and integrated along the frequency dimension to obtain the energy function as a function of time; Then the obtained result is differentiated, represented as: In addition, the K-means clustering algorithm is used to binarize the Diff TE (t) and identify the peak position corresponding to the cluster with larger value: for each peak position, track left and right respectively until the boundary where the energy stops dropping; merge the interval defined by the two boundaries to accurately locate the distance gate interval of each interference slice with the same time delay in the time-frequency domain; record the start position and end position of each interference distance gate interval, denoted as: G s = [g s (1),g s (2),…,g s (i),…,g s (n)] G e = [g e (1),g e (2),…,g e (i),…,g e (n)] wherein n represents the total number of interference distance gate intervals, G s and G e are in one-to-one correspondence with the start position and end position of each interference distance gate interval, respectively.

6. The method of claim 1, wherein the pulse compression and time-frequency analysis based SAR intermittent sampling repeater jamming mitigation method is characterized by, In step S105, the interference frequency sampling point interval is obtained by integrating the corresponding interference distance gate interval of the above two along the time dimension and carrying out difference operation, specifically: The time-frequency function S J (t,f) and The time-frequency data corresponding to each interference distance gate interval is energy accumulated along the time dimension. Then the obtained results are subjected to difference operation, so that the interference is more prominent, thereby being able to obtain the frequency sampling point where the interference is located with higher precision, denoted as: where i = 1, 2,... n, g s (i) and g e (i) represent the start and end positions of each interference distance gate interval, respectively, S J (t,f) and represent the time-frequency data of the interfering pulse and the nearest non-interfering pulse, respectively; In addition, the K-means clustering algorithm is used to perform binary segmentation on Diff FE (f i ) to locate the frequency sampling point interval where the interference is located; the start position and the end position of each frequency sampling point interval are recorded and represented as: F s = [f s (1), f s (2),..., f s (i),..., f s (m)] F e = [f e (1), f e (2),..., f e (i),..., f e (m)] Wherein, m represents the total number of frequency sampling point intervals of the interference slice, F S and f e The elements one by one correspond to the starting position and the ending position of the frequency sampling point interval of each interference slice, respectively.

7. The method of claim 1, wherein the method is a pulse compression and time-frequency analysis based SAR intermittent sampling repeater jamming mitigation method. In step S106, the interference distance gate interval and the interference frequency sampling point interval are combined to obtain the time-frequency domain positioning of the interference, specifically: The interference distance gate interval and the frequency sampling point interval are combined to complete the positioning of the interference in the time-frequency domain, denoted as: where N f represents the number of frequency dimension sampling points, N t represents the number of distance gates; p i (j) = 1 indicates containing an interference point, p i (j) = 0 indicates not containing an interference point.

8. The method of claim 1, wherein the pulse compression and time-frequency analysis based SAR intermittent sampling repeater jamming mitigation method is characterized by, In step S107, a time-frequency domain filter is designed, and the position where the interference is located is assigned the data mean of the position corresponding to the nearest non-interference distance gate of the time-frequency data of the interference-containing pulse, specifically: First, a time-frequency domain filter is designed based on the obtained time-frequency domain positioning result of the interference to perform notch operation, denoted as: where γ(i,j) represents the unjammed signal at position (i,j), i = 1, 2,... N t , j = 1, 2,..., N f . Second, the nearest non-interference distance gate is matched for each interference-containing distance gate, and the index subscript is recorded, denoted as: M=[m(1),m(2),…,m(j),…,m(sum)] Wherein, j represents the jth distance gate with interference, m(m) represents the index of the nearest non-interference distance gate matched by the jth distance gate with interference, and sum represents the total number of interference-containing distance gates in the time-frequency domain; Further, the data containing the interfering distance gate and the nearest non-interfering distance gate are extracted, respectively denoted as S J (m(j),f) and The average of the corresponding elements is recalculated, which can be represented as: Then, the obtained result is used to compensate for the missing information at the notch position, which can be denoted as: Finally, after the above process is sequentially performed at the distance gate of each notch position, the non-interference pulse can be obtained after inverse STFT and inverse pulse compression.

9. A SAR intermittent sampling repeater jamming suppression system based on pulse compression and time-frequency analysis, characterized in that, Comprise: The feature extraction unit is configured to perform distance direction pulse compression on the synthetic aperture radar data input into the system pulse by pulse, and then calculate and obtain a sequence of kurtosis coefficients pulse by pulse; The interference pulse detection unit is configured to perform binary segmentation on the obtained sequence of kurtosis coefficients using a K-means clustering algorithm to accurately obtain a sequence of interference-containing pulses; The interference slice positioning unit is configured to perform short-time Fourier transform on the interference-containing pulse after pulse compression and the nearest non-interference pulse, and then perform two different dimension superposition and difference operations to realize time-frequency domain positioning of the 2D-ISRJ; The time-frequency domain filter design unit is configured to design a time-frequency domain filter at the time-frequency domain position where the interference is located, that is, the non-interference position data remains unchanged, and the interference position is the data mean of the position corresponding to the nearest non-interference distance gate of the time-frequency data of the interference-containing pulse after pulse compression and the nearest non-interference pulse; The interference suppression unit is configured to multiply the filter response by the time-frequency distribution of each interference-containing pulse, and then sequentially perform inverse STFT and inverse pulse compression to obtain non-interference SAR echo data.

10. A storage device in which a plurality of programs are stored, characterized by The program is loaded and executed by a processor to implement the SAR intermittent sampling retransmission type interference suppression method based on pulse compression and time-frequency analysis according to any one of claims 1-8.

Images