Azimuth multi-channel sar system time-varying wideband interference detection method and system

Abstract

The application provides a time-varying wideband interference detection method and system for an azimuth multichannel SAR system, comprising the following steps: S1, collecting azimuth multichannel SAR original data; S2, performing distance Fourier transform on the azimuth multichannel SAR original data to calculate a multichannel SAR echo covariance matrix; S3, performing interference detection based on the similarity of the covariance matrix; and S4, fusing the detection results to determine a pixel unit where the interference is located. The application uses the similarity between multichannel echo covariance matrices to perform interference detection, solves the problem of interference detection for an azimuth multichannel SAR system, and has an important supporting role for low-frequency multichannel SAR system imaging processing. The application uses the similarity between multichannel echo covariance matrices, detects interference by calculating the correlation between the covariance matrices, and improves the possibility of interference detection.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and more specifically, to a time-varying broadband interference detection method and system for a multi-channel azimuth SAR system. Background Technology

[0002] Synthetic Aperture Radar (SAR) can perform all-weather, all-time, wide-area, high-resolution imaging of the Earth, and has become an important tool for Earth remote sensing. Azimuth multi-channel SAR systems are an effective way to achieve high-resolution, wide-swath imaging and have received widespread attention and research in recent years. However, due to the increasing number of ground-based electronic devices, ground-based radio frequency interference (RFI) has become a major problem in azimuth multi-channel SAR imaging, posing a significant obstacle to raw data acquisition, imaging processing, and subsequent interpretation. This includes affecting the echo signal acquisition process and amplitude dynamic range, impacting coherent imaging focusing processing, and reducing the quality of image derivatives, severely affecting SAR imaging and application performance. RFI detection is a prerequisite for RFI suppression. Therefore, RFI detection is currently one of the key technologies in ground data processing.

[0003] To address the issue of radio frequency interference suppression, patent document CN113064122B proposes a performance evaluation method, system, and medium for P-band SAR interference suppression algorithms. While this document presents an evaluation method and system for P-band SAR interference suppression algorithms, it does not provide a detailed analysis of the applicability to multi-channel systems. Patent document CN110221256B proposes a SAR interference suppression method based on deep residual networks. This document proposes using deep convolutional neural networks to detect and suppress interference in the original echo, but it does not analyze the suppression effect of multi-channel systems. Patent document CN105974376B proposes a SAR radio frequency interference suppression method. This document proposes a radio frequency interference suppression method using subspace projection, but it does not consider multi-channel systems. Patent document CN108318865A provides a multi-channel SAR deception interference identification and adaptive suppression method. This document utilizes the differences in spatiotemporal characteristics between interference and SAR echoes for adaptive filtering, but this requires the system to have sufficient redundant channels. Patent document CN111239697A proposes a multi-dimensional domain joint SAR broadband interference suppression method using low-rank matrix factorization. This patent document proposes using short-time Fourier transform matrix vectorization to separate the interference signal and the SAR echo signal, effectively avoiding the loss of the echo signal due to interference suppression. However, this method does not consider multi-channel applications. The article "Narrowband RFI Suppression on High-Resolution Wide-Swath SAR Systems via Low-Rank Recovery," 2022 IEEE International Geoscience and Remote Sensing Symposium, pp. 5137-5140, proposes a narrowband RFI mitigation scheme for azimuth multi-channel HRWSSAR systems, but it is not applicable to broadband interference signals. For conventional, undesigned, spaceborne azimuth multi-channel high-resolution wide-swath SAR systems, there is currently no effective interference detection method applicable to all scenarios. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a time-varying broadband interference detection method and system for azimuth multi-channel SAR systems.

[0005] A time-varying broadband interference detection method for a multi-channel azimuth SAR system according to the present invention includes:

[0006] Step S1: Acquire raw azimuth multi-channel SAR data;

[0007] Step S2: Perform range-direction Fourier transform on the raw azimuth multi-channel SAR data to transform the raw echoes to the azimuth time-range frequency domain, and calculate the multi-channel SAR echo covariance matrix along the frequency direction within a set window.

[0008] Step S3: Perform interference detection based on covariance matrix similarity;

[0009] Step S4: Fuse the detection results to determine the pixel unit where the interference is located.

[0010] Preferably, step S3 includes the following sub-steps:

[0011] Step S3.1: Determine the location, time, and distance frequency window of the interference by comparing the similarity of the covariance matrices of adjacent samples in the azimuth direction;

[0012] Step S3.2: Calculate the multi-channel SAR echo covariance matrix along the azimuth direction within a set window;

[0013] Step S3.3: Compare the similarity between the two covariance matrices of the obtained interference-free region and the interference region to determine the frequency cell and azimuth time window where the interference is located.

[0014] Preferably, step S3.1 includes the following sub-steps:

[0015] Step S3.1.1: Set the false detection rate and determine the similarity detection threshold:

[0016]

[0017] In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, and let L be the probability. r The number of frequency-direction samples used for covariance matrix estimation, z is the detection threshold, M is the number of channels in the azimuth multi-channel SAR, and χ is the number of channels. 2 (·) represents χ 2 distributed;

[0018] Step S3.1.2: Calculate the similarity of the covariance matrices of two adjacent samples in the azimuth direction:

[0019]

[0020] In the formula, D sm (m) represents the similarity between the covariance matrix at azimuth position m and the covariance matrix at azimuth position m+1, M is the number of channels in the azimuth multi-channel SAR, and L r C is the number of frequency-directed samples used for covariance matrix estimation. f,m The covariance matrix calculated along the frequency direction for the m-th sample is shown in the table below, where f represents the frequency direction.

[0021] Step S3.1.3: Determine interference based on the calculated covariance similarity along the azimuth direction;

[0022] When D sm (m-2)≤z, and D sm (m+1)≤z, and D sm (m-1)>z, and D sm When (m)>z, it is determined that there is interference in the azimuth time and distance frequency window corresponding to the m-th covariance matrix.

[0023] Preferably, step S3.3 includes the following sub-steps:

[0024] Step S3.3.1: Calculate the similarity between the covariance matrices of the two samples from the interference-free region and the interference-containing region:

[0025]

[0026] In the formula, D sm (n) represents the similarity between the covariance matrix at frequency position n and the covariance matrix at azimuth position n+1, M is the number of channels in the azimuth multi-channel SAR, and L a C is the number of azimuth samples used for covariance matrix estimation. a,n C represents the covariance matrix calculated along the azimuth direction for the nth frequency sample. a,np The covariance matrix representing the region free from radio frequency pollution is calculated along the azimuth direction, with the subscript 'a' indicating the azimuth direction.

[0027] Step S3.3.2: Set the false detection rate and determine the similarity detection threshold:

[0028]

[0029] In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, z be the detection threshold, M be the number of channels in the azimuth multi-channel SAR, and χ be the similarity between the two covariance matrices. 2 (·) represents χ 2 Distribution, L a The number of azimuth samples used for covariance matrix estimation;

[0030] Step S3.3.3: Determine interference based on the calculated frequency-direction covariance similarity;

[0031] Assuming the frequency is at position n, when D sm When (n)>z, it is determined that there is interference in the frequency unit and azimuth window corresponding to the nth covariance matrix.

[0032] Preferably, step S4 includes using the azimuth time and distance frequency window of the interference and the frequency unit and azimuth time window of the interference as the final interference detection result.

[0033] A time-varying broadband interference detection system for a multi-channel azimuth SAR system according to the present invention includes:

[0034] Module M1: Acquires raw azimuth multi-channel SAR data;

[0035] Module M2: Performs range-to-Fourier transform on the raw multi-channel SAR data, transforms the raw echo to the azimuth time-range frequency domain, and calculates the multi-channel SAR echo covariance matrix along a set window in the frequency direction.

[0036] Module M3: Performs interference detection based on covariance matrix similarity;

[0037] Module M4: Fusion of detection results to determine the pixel unit where the interference is located.

[0038] Preferably, module M3 includes the following sub-modules:

[0039] Module M3.1: Determines the location, time, and distance frequency window of interference by comparing the similarity of the covariance matrices of adjacent samples in the azimuth direction;

[0040] Module M3.2: Calculate the multi-channel SAR echo covariance matrix by setting a window along the azimuth direction;

[0041] Module M3.3: Compare the similarity between the two covariance matrices of the obtained interference-free region and the interference region to determine the frequency cell and azimuth time window where the interference is located.

[0042] Preferably, module M3.1 includes the following sub-modules:

[0043] Module M3.1.1: Set the false detection rate and determine the similarity detection threshold:

[0044]

[0045] In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, and let L be the probability. r The number of frequency-direction samples used for covariance matrix estimation, z is the detection threshold, M is the number of channels in the azimuth multi-channel SAR, and χ is the number of channels. 2 (·) represents χ 2 distributed;

[0046] Module M3.1.2: Calculate the similarity of the covariance matrices of two adjacent samples in the azimuth direction:

[0047]

[0048] In the formula, D sm (m) represents the similarity between the covariance matrix at azimuth position m and the covariance matrix at azimuth position m+1, M is the number of channels in the azimuth multi-channel SAR, and L r C is the number of frequency-directed samples used for covariance matrix estimation. f,m The covariance matrix calculated along the frequency direction for the m-th sample is shown in the table below, where f represents the frequency direction.

[0049] Module M3.1.3: Determines interference based on the calculated covariance similarity along the azimuth direction;

[0050] When D sm (m-2)≤z, and D sm (m+1)≤z, and D sm (m-1)>z, and D sm When (m)>z, it is determined that there is interference in the azimuth time and distance frequency window corresponding to the m-th covariance matrix.

[0051] Preferably, module M3.3 includes the following sub-modules:

[0052] Module M3.3.1: Calculate the similarity between the covariance matrices of two samples from the interference-free region and the interference-containing region.

[0053]

[0054] In the formula, D sm (n) represents the similarity between the covariance matrix at frequency position n and the covariance matrix at azimuth position n+1, M is the number of channels in the azimuth multi-channel SAR, and L a C is the number of azimuth samples used for covariance matrix estimation. a,n C represents the covariance matrix calculated along the azimuth direction for the nth frequency sample. a,np The covariance matrix representing the region free from radio frequency pollution is calculated along the azimuth direction, with the subscript 'a' indicating the azimuth direction.

[0055] Module M3.3.2: Set the false detection rate and determine the similarity detection threshold:

[0056]

[0057] In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, z be the detection threshold, M be the number of channels in the azimuth multi-channel SAR, and χ be the similarity between the two covariance matrices. 2 (·) represents χ 2 Distribution, L a The number of azimuth samples used for covariance matrix estimation;

[0058] Module M3.3.3: Determines interference based on the calculated frequency-direction covariance similarity;

[0059] Assuming the frequency is at position n, when D sm When (n)>z, it is determined that there is interference in the frequency unit and azimuth window corresponding to the nth covariance matrix.

[0060] Preferably, module M4 includes using the azimuth time and distance frequency window of the interference and the frequency unit and azimuth time window of the interference as the final interference detection result.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] 1. This invention utilizes the similarity between the covariance matrices of multi-channel echoes to perform interference detection, solving the interference detection problem of azimuth multi-channel SAR systems and providing important support for imaging processing of low-frequency, multi-channel SAR systems.

[0063] 2. This invention utilizes the similarity between the covariance matrices of multi-channel echoes and detects interference by calculating the correlation between the covariance matrices of the channels, thereby improving the possibility of interference detection.

[0064] Other beneficial effects of the present invention will be explained in detail through the introduction of specific technical features and technical solutions in specific embodiments. Those skilled in the art should be able to understand the beneficial technical effects brought about by these technical features and technical solutions through the introduction of these technical features and technical solutions. Attached Figure Description

[0065] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0066] Figure 1 This is a schematic diagram of the interference detection and processing flow of the present invention.

[0067] Figure 2 This is a comparison of SAR images before and after interference detection suppression in this invention. Detailed Implementation

[0068] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0069] Reference Figure 1As shown, a time-varying broadband interference detection method for a azimuth multi-channel SAR system includes:

[0070] Step S1: Perform a range-to-Fourier transform on the raw data to transform the raw echo to the azimuth time-range frequency domain;

[0071] Specifically, it includes the following:

[0072] A range-to-Fourier transform is performed on the raw echo data of the azimuth multi-channel SAR to transform the raw echoes into the azimuth time-range frequency domain.

[0073] In azimuth multi-channel SAR imaging, the antenna simultaneously receives signals from K sub-apertures in the azimuth direction. When RFI (Radio Frequency Injection) is present, the SAR echo received by the k-th channel can be expressed in the range-frequency-azimuth time domain as follows:

[0074] y k (η,f)=s k (η,f)+x k (η,f)+n k (η,f);

[0075] Where η is the azimuth time, f is the range frequency, and s k Let x be the useful signal of the k-th channel. k Let n be the interference signal of the k-th channel. k Let y be the system noise of the k-th channel. k Let k be the SAR echo received by the k-th channel, where the subscript k indicates the k-th channel. The azimuth multi-channel echo signal can be written in vector form:

[0076] y(n,f)=[y1(n,f)y1(n,f)…y K (η,f)] T ;

[0077] In the formula, the superscript T represents the transpose operation.

[0078] Step S2: Calculate the multi-channel SAR echo covariance matrix along the frequency direction within a set window;

[0079] Specifically, it includes the following:

[0080] Calculate the multi-channel SAR echo covariance matrix along a frequency-defined window.

[0081] Assume the number of frequency vector points used for covariance matrix estimation is L. r Then the azimuth and time η m The signal covariance matrix C f,m It can be represented as:

[0082]

[0083] In the formula, the superscript H represents the conjugate transpose operation.

[0084] Step S3: Determine the location, time, and distance frequency window of the interference by comparing the similarity of the covariance matrices of adjacent samples in the azimuth direction;

[0085] Specifically, it includes the following:

[0086] The location, time, and distance-frequency window of the interference are determined by comparing the similarity of the covariance matrices of adjacent samples in the azimuth direction.

[0087] The false positive rate is set, and the similarity detection threshold is determined as follows:

[0088]

[0089] In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, and let L be the probability. r The number of frequency-direction samples used for covariance matrix estimation, z is the detection threshold, M is the number of channels in the azimuth multi-channel SAR, and χ is the number of channels. 2 (·) represents χ 2 distributed.

[0090] Calculate the similarity of the covariance matrices of two adjacent samples in the azimuth direction:

[0091]

[0092] In the formula, D sm (m) represents the similarity between the covariance matrix at azimuth position m and the covariance matrix at azimuth position m+1, M is the number of channels in the azimuth multi-channel SAR, and L r C is the number of frequency-directed samples used for covariance matrix estimation. f,m This represents the covariance matrix calculated along the frequency direction for the m-th sample. In the table below, f represents the frequency direction.

[0093] Interference is determined based on the calculated covariance similarity along the azimuth direction: when D sm (m-2)≤z, and D sm (m+1)≤z, and D sm (m-1)>z, and D sm When (m)>z, it is determined that there is interference in the azimuth time and distance frequency window corresponding to the m-th covariance matrix.

[0094] Step S4: Calculate the multi-channel SAR echo covariance matrix along the azimuth direction within a set window;

[0095] Specifically, it includes the following:

[0096] Calculate the multi-channel SAR echo covariance matrix by setting a window along the azimuth direction.

[0097] Assume the number of azimuth points used for covariance matrix estimation is L. a Then the frequency point f n The signal covariance matrix C at point a,n It can be represented as

[0098]

[0099] In the formula, the superscript H represents the conjugate transpose operation, and L a Let y(η) be the number of azimuth points in the window used for covariance matrix estimation. m ,f n ) represents the azimuth multi-channel echo signal vector, η m For the m-th azimuth time, f n This is the nth frequency point.

[0100] Step S5: Determine the frequency cell and azimuth time window of the interference by comparing the similarity between the two covariance matrices of the interference-free region and the interference region calculated in step S3.

[0101] Specifically, it includes the following:

[0102] Calculate the similarity between the covariance matrices of the two samples obtained in step S3, one from the interference-free region and the other from the interference region:

[0103]

[0104] In the formula, D sm (n) represents the similarity between the covariance matrix at frequency position n and the covariance matrix at azimuth position n+1, M is the number of channels in the azimuth multi-channel SAR, and L a C is the number of azimuth samples used for covariance matrix estimation. a,n C represents the covariance matrix calculated along the azimuth direction for the nth frequency sample. a,np This represents the covariance matrix calculated along the azimuth direction for the region free from radio frequency pollution, with the subscript 'a' indicating the azimuth direction.

[0105] Determine the similarity detection threshold: Set a false positive rate and determine the similarity detection threshold as follows:

[0106]

[0107] In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, z be the detection threshold, M be the number of channels in the azimuth multi-channel SAR, and χ be the similarity between the two covariance matrices. 2 (·) represents χ 2 Distribution, L aThe number of azimuth samples used for covariance matrix estimation.

[0108] Interference is determined based on the calculated frequency-direction covariance similarity: Assuming the frequency-direction position is n, when D sm When (n)>z, it is determined that there is interference in the frequency unit and azimuth window corresponding to the nth covariance matrix.

[0109] Step S6: Combine the detection results of Step S3 and Step S5 to determine the pixel unit where the interference is located. The method for determining the pixel unit where the interference is located is to determine the azimuth / frequency unit where the interference is detected simultaneously in Step S3 and Step S5 as the final interference detection result.

[0110] Reference Figure 2 As shown, the method provided in this invention is verified by superimposing simulated interference data with measured airborne azimuth multi-channel high-resolution wide-swath SAR data. This SAR system is a five-channel airborne SAR system, with a time-domain interference-to-signal ratio of 0 dB when time-varying broadband signals are added. Figure 2 a and Figure 2 b presents SAR images before and after interference suppression using the detection results of this invention. Figure 2 It can be seen that the interference was effectively suppressed after processing by the method proposed in this invention.

[0111] This invention utilizes the similarity between the covariance matrices of multi-channel echoes for interference detection, solving the interference detection problem of azimuth multi-channel SAR systems and providing important support for imaging processing of low-frequency, multi-channel SAR systems.

[0112] The present invention also provides a time-varying broadband interference detection system for an azimuth multi-channel SAR system. The time-varying broadband interference detection system for an azimuth multi-channel SAR system can be implemented by executing the process steps of the time-varying broadband interference detection method for an azimuth multi-channel SAR system. That is, those skilled in the art can understand the time-varying broadband interference detection method for an azimuth multi-channel SAR system as a preferred embodiment of the time-varying broadband interference detection system for an azimuth multi-channel SAR system.

[0113] Specifically, a time-varying broadband interference detection system for a multi-channel azimuth SAR system includes:

[0114] Module M1: Acquires raw azimuth multi-channel SAR data;

[0115] Module M2: Performs range-to-Fourier transform on the raw multi-channel SAR data, transforms the raw echo to the azimuth time-range frequency domain, and calculates the multi-channel SAR echo covariance matrix along a set window in the frequency direction.

[0116] Module M3: Performs interference detection based on covariance matrix similarity;

[0117] Module M4: Fusion of detection results to determine the pixel unit where the interference is located.

[0118] The module M3 includes the following sub-modules:

[0119] Module M3.1: Determines the location, time, and distance frequency window of interference by comparing the similarity of the covariance matrices of adjacent samples in the azimuth direction;

[0120] Module M3.2: Calculate the multi-channel SAR echo covariance matrix by setting a window along the azimuth direction;

[0121] Module M3.3: Compare the similarity between the two covariance matrices of the obtained interference-free region and the interference region to determine the frequency cell and azimuth time window where the interference is located.

[0122] The module M3.1 includes the following sub-modules:

[0123] Module M3.1.1: Set the false detection rate and determine the similarity detection threshold:

[0124]

[0125] In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, and let L be the probability. r The number of frequency-direction samples used for covariance matrix estimation, z is the detection threshold, M is the number of channels in the azimuth multi-channel SAR, and χ is the number of channels. 2 (·) represents χ 2 distributed;

[0126] Module M3.1.2: Calculate the similarity of the covariance matrices of two adjacent samples in the azimuth direction:

[0127]

[0128] In the formula, D sm (m) represents the similarity between the covariance matrix at azimuth position m and the covariance matrix at azimuth position m+1, M is the number of channels in the azimuth multi-channel SAR, and L r C is the number of frequency-directed samples used for covariance matrix estimation. f,m The covariance matrix calculated along the frequency direction for the m-th sample is shown in the table below, where f represents the frequency direction.

[0129] Module M3.1.3: Determines interference based on the calculated covariance similarity along the azimuth direction;

[0130] When D sm (m-2)≤z, and D sm (m+1)≤z, and D sm (m-1)>z, and Dsm When (m)>z, it is determined that there is interference in the azimuth time and distance frequency window corresponding to the m-th covariance matrix.

[0131] The module M3.3 includes the following sub-modules:

[0132] Module M3.3.1: Calculate the similarity between the covariance matrices of two samples from the interference-free region and the interference-containing region.

[0133]

[0134] In the formula, D sm (n) represents the similarity between the covariance matrix at frequency position n and the covariance matrix at azimuth position n+1, M is the number of channels in the azimuth multi-channel SAR, and L a C is the number of azimuth samples used for covariance matrix estimation. a,n C represents the covariance matrix calculated along the azimuth direction for the nth frequency sample. a,np The covariance matrix representing the region free from radio frequency pollution is calculated along the azimuth direction, with the subscript 'a' indicating the azimuth direction.

[0135] Module M3.3.2: Set the false detection rate and determine the similarity detection threshold:

[0136]

[0137] In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, z be the detection threshold, M be the number of channels in the azimuth multi-channel SAR, and χ be the similarity between the two covariance matrices. 2 (·) represents χ 2 Distribution, L a The number of azimuth samples used for covariance matrix estimation;

[0138] Module M3.3.3: Determines interference based on the calculated frequency-direction covariance similarity;

[0139] Assuming the frequency is at position n, when D sm When (n)>z, it is determined that there is interference in the frequency unit and azimuth window corresponding to the nth covariance matrix.

[0140] The module M4 includes using the azimuth time and distance frequency window of the interference and the frequency unit and azimuth time window of the interference as the final interference detection result.

[0141] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0142] In the description of this application, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0143] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for detecting time-varying broadband interference in a multi-channel azimuth SAR system, characterized in that, include: Step S1: Acquire raw azimuth multi-channel SAR data; Step S2: Perform range-direction Fourier transform on the raw azimuth multi-channel SAR data to transform the raw echoes to the azimuth time-range frequency domain, and calculate the multi-channel SAR echo covariance matrix along the frequency direction within a set window. Step S3: Perform interference detection based on covariance matrix similarity; Step S4: Fuse the detection results to determine the pixel unit where the interference is located; Step S3 includes the following sub-steps: Step S3.1: Determine the location, time, and distance frequency window of the interference by comparing the similarity of the covariance matrices of adjacent samples in the azimuth direction; Step S3.2: Calculate the multi-channel SAR echo covariance matrix along the azimuth direction within a set window; Step S3.3: Compare the similarity between the two covariance matrices of the obtained interference-free region and the interference region to determine the frequency cell and azimuth time window where the interference is located; Step S3.1 includes the following sub-steps: Step S3.1.1: Set the false detection rate and determine the similarity detection threshold: In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, and let L be the probability. r The number of frequency-direction samples used for covariance matrix estimation, z is the detection threshold, M is the number of channels in the azimuth multi-channel SAR, and χ is the number of channels. 2 (·) represents χ 2 distributed; Step S3.1.2: Calculate the similarity of the covariance matrices of two adjacent samples in the azimuth direction: In the formula, D sm (m) represents the similarity between the covariance matrix at azimuth position m and the covariance matrix at azimuth position m+1, M is the number of channels in the azimuth multi-channel SAR, and L r C is the number of frequency-directed samples used for covariance matrix estimation. f,m The covariance matrix calculated along the frequency direction for the m-th sample is shown in the table below, where f represents the frequency direction. Step S3.1.3: Determine interference based on the calculated covariance similarity along the azimuth direction; When D sm (m-2)≤z, and D sm (m+1)≤z, and D sm (m-1)>z, and D sm When (m)>z, it is determined that there is interference in the azimuth time and distance frequency window corresponding to the m-th covariance matrix; Step S3.3 includes the following sub-steps: Step S3.3.1: Calculate the similarity between the covariance matrices of the two samples from the interference-free region and the interference-containing region: In the formula, D sm (n) represents the similarity between the covariance matrix at frequency position n and the covariance matrix at frequency position n+1, M is the number of channels in the azimuth multi-channel SAR, and L a C is the number of azimuth samples used for covariance matrix estimation. a,n C represents the covariance matrix calculated along the azimuth direction for the nth frequency sample. a,np The covariance matrix representing the region free from radio frequency pollution is calculated along the azimuth direction, with the subscript 'a' indicating the azimuth direction. Step S3.3.2: Set the false detection rate and determine the similarity detection threshold: In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, z be the detection threshold, M be the number of channels in the azimuth multi-channel SAR, and χ be the similarity between the two covariance matrices. 2 (·) represents χ 2 Distribution, L a The number of azimuth samples used for covariance matrix estimation; Step S3.3.3: Determine interference based on the calculated frequency-direction covariance similarity; Assuming the frequency position is n, when D sm When (n)>z, it is determined that there is interference in the frequency unit and azimuth window corresponding to the nth covariance matrix.

2. The method for detecting time-varying broadband interference in a multi-channel azimuth SAR system according to claim 1, characterized in that, Step S4 includes taking the azimuth time and distance frequency window of the interference and the frequency unit and azimuth time window of the interference as the final interference detection result.

3. A time-varying broadband interference detection system for a multi-channel azimuth SAR system, characterized in that, include: Module M1: Acquires raw azimuth multi-channel SAR data; Module M2: Performs range-to-Fourier transform on the raw multi-channel SAR data, transforms the raw echo to the azimuth time-range frequency domain, and calculates the multi-channel SAR echo covariance matrix along a set window in the frequency direction. Module M3: Performs interference detection based on covariance matrix similarity; Module M4: Fusion of detection results to determine the pixel unit where interference occurs; The module M3 includes the following sub-modules: Module M3.1: Determines the location, time, and distance frequency window of interference by comparing the similarity of the covariance matrices of adjacent samples in the azimuth direction; Module M3.2: Calculate the multi-channel SAR echo covariance matrix by setting a window along the azimuth direction; Module M3.3: Compare the similarity between the two covariance matrices of the obtained interference-free area and the interference area to determine the frequency cell and azimuth time window where the interference is located; The module M3.1 includes the following sub-modules: Module M3.1.1: Set the false detection rate and determine the similarity detection threshold: In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, and let L be the probability. r The number of frequency-direction samples used for covariance matrix estimation, z is the detection threshold, M is the number of channels in the azimuth multi-channel SAR, and χ is the number of channels. 2 (·) represents χ 2 distributed; Module M3.1.2: Calculate the similarity of the covariance matrices of two adjacent samples in the azimuth direction: In the formula, D sm (m) represents the similarity between the covariance matrix at azimuth position m and the covariance matrix at azimuth position m+1, M is the number of channels in the azimuth multi-channel SAR, and L r C is the number of frequency-directed samples used for covariance matrix estimation. f,m The covariance matrix calculated along the frequency direction for the m-th sample is shown in the table below, where f represents the frequency direction. Module M3.1.3: Determines interference based on the calculated covariance similarity along the azimuth direction; When D sm (m-2)≤z, and D sm (m+1)≤z, and D sm (m-1)>z, and D sm When (m)>z, it is determined that there is interference in the azimuth time and distance frequency window corresponding to the m-th covariance matrix; The module M3.3 includes the following sub-modules: Module M3.3.1: Calculate the similarity between the covariance matrices of two samples from the interference-free region and the interference-containing region. In the formula, D sm (n) represents the similarity between the covariance matrix at frequency position n and the covariance matrix at frequency position n+1, M is the number of channels in the azimuth multi-channel SAR, and L a C is the number of azimuth samples used for covariance matrix estimation. a,n C represents the covariance matrix calculated along the azimuth direction for the nth frequency sample. a,np The covariance matrix representing the region free from radio frequency pollution is calculated along the azimuth direction, with the subscript 'a' indicating the azimuth direction. Module M3.3.2: Set the false detection rate and determine the similarity detection threshold: In the formula, α is the false detection rate, and D sm Let P{·} represent the similarity between the two covariance matrices, z be the detection threshold, M be the number of channels in the azimuth multi-channel SAR, and χ be the similarity between the two covariance matrices. 2 (·) represents χ 2 Distribution, L a The number of azimuth samples used for covariance matrix estimation; Module M3.3.3: Determines interference based on the calculated frequency-direction covariance similarity; Assuming the frequency position is n, when D sm When (n)>z, it is determined that there is interference in the frequency unit and azimuth window corresponding to the nth covariance matrix.

4. The time-varying broadband interference detection system for a multi-channel azimuth SAR system according to claim 3, characterized in that, The module M4 includes using the azimuth time and distance frequency window of the interference and the frequency unit and azimuth time window of the interference as the final interference detection result.

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