Satellite in-orbit silent receiving interference detection method

Abstract

The invention discloses a satellite in-orbit silent receiving interference detection method, which comprises the following steps of: accurately positioning an interference frequency point through silent pulse partitioning and a double-threshold dynamic detection mechanism according to the characteristics of radio frequency interference unidirectional propagation and energy accumulation in a distance frequency domain; and meanwhile, light-weight generation of the interference mask is realized in combination with variable coefficients and signal autocorrelation characteristics. The method aims at efficiently extracting the high-precision interference mask under the constraint condition that on-orbit computing resources of a satellite-borne SAR system are strictly limited, accurate frequency domain prior information is provided for subsequent interference suppression processing, and therefore refined real-time suppression of radio frequency interference echo signals is achieved, and the SAR image quality is remarkably improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of radar, and particularly relates to a satellite in-orbit mute reception interference detection method. BACKGROUND

[0002] Satellite-borne synthetic aperture radar (SAR) has the characteristics of all-weather, all-day and high-resolution observation. The radio frequency interference (RFI) received by the satellite-borne SAR is more and more serious, which reduces the quality of the SAR image and affects the subsequent image interpretation.

[0003] The traditional interference detection method has the following problems: (1) Most of the signal interference detection processing is carried out offline on the ground, which is difficult to meet the real-time requirements of the satellite-borne SAR system for interference detection.

[0004] (2) The satellite-borne SAR platform has limited computing resources in orbit, and most detection algorithms have high computational complexity, which cannot be deployed on the satellite-borne SAR platform in the face of large amounts of satellite-borne SAR data.

[0005] (3) Under the condition of limited computing resources, there is a lack of adaptive adjustment ability to different interference types and intensities.

[0006] Therefore, it is of great significance to study an efficient and real-time satellite-borne SAR in-orbit anti-interference processing method. SUMMARY

[0007] In order to overcome the shortcomings of the prior art, the application provides a satellite in-orbit mute reception interference detection method, which accurately locates the interference frequency points through mute pulse blocking and double-threshold dynamic detection mechanism according to the characteristics of one-way propagation of radio frequency interference and energy aggregation in the range-frequency domain, and realizes the lightweight generation of interference mask by combining the coefficient of variation with the signal autocorrelation characteristics. The application aims to efficiently extract high-precision interference masks under the constraint condition that the in-orbit computing resources of the satellite-borne SAR system are strictly limited, to provide accurate frequency domain prior information for subsequent interference suppression processing, so as to realize fine and real-time suppression of radio frequency interference echo signals and significantly improve the quality of SAR images.

[0008] The technical scheme adopted by the application to solve its technical problems is as follows: Step 1: receiving synthetic aperture radar mute pulse echo data containing radio frequency interference, and uniformly dividing the mute pulse echo data along the range direction into 4 local data slices , respectively, and the two-dimensional frequency spectrum of the distance frequency domain and the azimuth time domain is obtained by performing Fourier transform on each data slice along the distance dimension , denotes the distance frequency; Step 2: The two-dimensional distance frequency spectrum obtained in step 1 has a size of The mean value and the variance of each data slice are calculated in the frequency band respectively The coefficient of variation and the autocorrelation signal of the data slice are obtained (2) At the same time, the kurtosis measures the sharpness of the data distribution along the azimuth direction, and the kurtosis in the signal frequency band is obtained according to the mean value and the variance , and the calculation formula is as follows: (3) In the formula, denotes the number of azimuth units, denotes the number of distance units of the data slice, denotes the total amount of the data slice, denotes the current data, denotes the data mean value, denotes the data standard deviation; Step 3: According to the coefficient of variation in step 2, the sensitive coefficient and the basic threshold are adaptively set, and the adaptive coefficient of mask extraction k is calculated: (4) Then k is brought into the mask extraction threshold , and finally the preliminary interference mask is generated through the signal autocorrelation threshold detection , and the calculation formula is as follows: (5) In the formula, denotes the autocorrelation matrix of the data slice; Step 4: Outlier detection is performed on the kurtosis obtained in step 2, and the kurtosis detection parameters: the sensitive coefficient and the basic threshold are adaptively set according to the coefficient of variation , and the adaptive detection coefficient is calculated​​ The calculation formula is as follows: (6) Finally, kurtosis outlier detection is performed, and the distance-directed location of the outlier signal is output. The calculation formula is as follows: (7) in, It is the distance-directed location of the anomalous signal in kurtosis outlier detection; Step 5: Analyze the distance to the kurtosis outlier values ​​obtained in Step 4. Expand the radius to [value]. radius For the detected outlier locations, move them to the left and right respectively. radius Each sampling point was marked as an interference area. Finally, by using the results obtained in step 3 and The intersection is taken, and the preliminary interference mask result is corrected using the kurtosis outlier detection result to obtain the final refined interference mask. The calculation formula is as follows: (8) An electronic device includes: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the above-described silent reception interference detection method.

[0009] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described silent reception interference detection method.

[0010] A chip includes a processor for calling and running a computer program from a memory, causing a device equipped with the chip to perform the above-described silent reception interference detection method.

[0011] A computer program product includes a computer storage medium storing a computer program, the computer program including instructions executable by at least one processor, which, when executed by the at least one processor, implement the above-described silent reception interference detection method.

[0012] The beneficial effects of this invention are as follows: This invention utilizes a silent pulse-based on-orbit real-time interference detection method for spaceborne SAR to achieve refined interference detection of echo signals containing radio frequency interference from spaceborne SAR. After the silent pulse data is segmented, a refined interference mask is extracted through statistical characteristic analysis and a dual-threshold dynamic detection mechanism. This effectively solves the problem of refined interference information extraction under conditions of insufficient on-orbit processing computing resources, thereby improving the on-orbit anti-interference capability of the SAR system. Attached Figure Description

[0013] figure 1 This is a flowchart of the method of the present invention.

[0014] figure 2 This is a SAR echo image with interference, as shown in an embodiment of the present invention.

[0015] figure 3 This is a time-domain diagram of a silent pulse containing interference, as shown in an embodiment of the present invention.

[0016] figure 4 This is a time-domain slice diagram of a silent pulse containing interference, as shown in an embodiment of the present invention.

[0017] figure 5 This is a distance-frequency domain diagram of a silent pulse slice with interference, as shown in an embodiment of the present invention.

[0018] figure 6 This is a frequency domain diagram of signal autocorrelation distance in an embodiment of the present invention.

[0019] figure 7 This is an interference mask based on signal autocorrelation in an embodiment of the present invention.

[0020] figure 8 This is a kurtosis outlier detection map along the azimuth direction according to an embodiment of the present invention.

[0021] figure 9 This is the final refined interference mask diagram of the embodiment of the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] The present invention aims to eliminate the interference artifacts caused by radio frequency interference to SAR image data, as well as the strong scattering point sidelobe enhancement phenomenon after eliminating frequency domain notch filtering, thereby achieving refined and effective suppression of SLC images with radio frequency interference.

[0024] To achieve the above objectives, the technical solution of the present invention includes the following steps: Step 1: Acquire synthetic aperture radar silent pulse data containing radio frequency interference, and divide the silent pulse echo data into 4 local data slices along the range direction. Performing a Fourier transform along the range dimension on each data slice yields its range spectrum and its two-dimensional spectrum in the range-azimuth time domain. .

[0025] Step 2: The size obtained in Step 1 is Two-dimensional distance spectrum Calculate the mean of each data slice within the frequency band. and variance , the coefficient of variation (CV) and the autocorrelation signal of the data slice are obtained : (1) (2) Meanwhile, the kurtosis measures the sharpness of the data distribution along the azimuth direction, which can also be obtained according to the mean and the variance , the kurtosis in the signal frequency band is obtained , and the calculation formula is as follows: (3) Step 3: According to the coefficient of variation (CV) in step 2, the sensitivity coefficient and the basic threshold value are adaptively set, and the adaptive coefficient for mask extraction is calculated k : (4) Then k is brought into the mask extraction threshold value , and finally the preliminary interference mask is generated through signal autocorrelation threshold detection , and the calculation formula is as follows: (5) Step 4: Outlier detection is performed on the kurtosis obtained in step 2, and the kurtosis detection parameters: sensitivity coefficient and basic threshold value are adaptively set according to the coefficient of variation (CV), and the adaptive detection coefficient is calculated , and the calculation formula is as follows: (6) Finally, kurtosis outlier detection is performed, and the abnormal signal distance direction position is output, and the calculation formula is as follows: (7) Wherein, is the distance direction position of the abnormal signal in the kurtosis outlier detection.

[0026] Step 5: The kurtosis outlier obtained in step 4 is appropriately expanded, the expansion radius is set as radius , and the left and right radius sampling points of the detected abnormal value position are marked as interference areas , and finally the intersection of obtained in step 3 and of this step is taken, and the preliminary interference mask result is corrected by the kurtosis outlier detection result, to obtain the final fine interference mask The calculation formula is as follows: (8) Example: Step 1: figure 1 The diagram shown is a flowchart of the method of this invention. Receive measured echo image data from L-band SAR, such as... figure 2 As shown. The signal region containing the silent pulse is selected, with a size of... ,like figure 3 As shown, then a slice of the silent pulse signal is extracted. Size is ,like figure 4 As shown, the two-dimensional spectrum of the silent pulse signal slice data in the range-frequency domain and azimuth-time domain is obtained using the Fast Fourier Transform (FFT). ,like figure 5 As shown. The mean of each data slice is calculated within the frequency band. and variance The coefficient of variation (CV) and signal power of the data slice are obtained. : (1) (2) Meanwhile, kurtosis measures the sharpness of a data distribution along its azimuth, and it can also be calculated based on the mean. and variance The kurtosis within the signal frequency band is obtained. The calculation formula is as follows: (3) Step 3: Adaptively set the sensitivity coefficient based on the coefficient of variation (CV) value from Step 2. and base threshold And calculate the adaptive coefficients for mask extraction. k : (4) Then k Threshold extraction with mask Finally, an initial interference mask is generated through signal autocorrelation threshold detection. ,like figure 7 As shown. The calculation formula is as follows: (5) Step 4: Perform outlier detection on the kurtosis obtained in Step 2, and adaptively set the kurtosis detection parameters based on the coefficient of variation (CV): sensitivity coefficient. and base threshold And calculate the adaptive detection coefficients. The calculation formula is as follows: (6) Finally, kurtosis outlier detection is performed, such as... figure 8 As shown. The distance to the output abnormal signal is determined by the following formula: (7) in, It is the distance position of the abnormal signal in kurtosis outlier detection.

[0027] Step 5: Appropriately expand the distance-oriented position of the kurtosis outliers obtained in Step 4, setting the expansion radius to [value missing]. radius For the detected outlier locations, move them to the left and right respectively. radius Each sampling point was marked as an interference area. Finally, by using the results obtained in step 3 And this step The intersection is taken, and the preliminary interference mask result is corrected using the kurtosis outlier detection result to obtain the final refined interference mask. ,like figure 9 As shown. The calculation formula is as follows: (8).

Claims

1. A method for detecting interference in silent satellite reception, characterized in that, Includes the following steps: Step 1: Receive synthetic aperture radar silent pulse echo data containing radio frequency interference, and divide the silent pulse echo data into 4 local data slices along the range direction. , Representing the azimuth time and range time respectively; performing a Fourier transform along the range dimension on each data slice yields its range spectrum and its two-dimensional spectrum in the range-azimuth time domain. , Indicates the range frequency; Step 2: The size obtained in Step 1 is Two-dimensional distance spectrum Calculate the mean of each data slice within the frequency band. and variance The coefficient of variation of the data slices is obtained. and autocorrelation signal : (1) (2) Meanwhile, kurtosis measures the sharpness of the data distribution along the azimuth direction, based on the mean. and variance The kurtosis within the signal frequency band is obtained. The calculation formula is as follows: (3) In the formula, Indicates the number of azimuth units. Indicates the distance to the data slice from the nearest unit. This indicates the total number of data slices. Indicates the current data. This represents the mean of the data. Indicates the standard deviation of the data; Step 3: Based on the coefficient of variation in Step 2 Size, adaptive sensitivity setting and base threshold And calculate the adaptive coefficients for mask extraction. k : (4) Then k Threshold extraction with mask Finally, an initial interference mask is generated through signal autocorrelation threshold detection. The calculation formula is as follows: (5) in, The autocorrelation matrix representing a data slice; Step 4: Perform outlier detection on the kurtosis obtained in Step 2, and determine the outlier based on the coefficient of variation. Adaptive setting of kurtosis detection parameters: sensitivity coefficient and base threshold And calculate the adaptive detection coefficients. The calculation formula is as follows: (6) Finally, kurtosis outlier detection is performed, and the distance-directed location of the outlier signal is output. The calculation formula is as follows: (7) in, It is the distance-directed location of the anomalous signal in kurtosis outlier detection; Step 5: Analyze the distance to the kurtosis outlier values ​​obtained in Step 4. Expand the radius to [value]. radius For the detected outlier locations, move them to the left and right respectively. radius Each sampling point was marked as an interference area. Finally, by using the results obtained in step 3 and The intersection is taken, and the preliminary interference mask result is corrected using the kurtosis outlier detection result to obtain the final refined interference mask. The calculation formula is as follows: (8)。 2. An electronic device, characterized in that, include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method as described in claim 1.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in claim 1.

4. A chip, characterized in that, include: A processor for retrieving and running a computer program from memory, causing a device on which the chip is mounted to perform the method as described in claim 1.

5. A computer program product, characterized in that, The computer program product includes a computer storage medium storing a computer program, the computer program including instructions executable by at least one processor, which, when executed by the at least one processor, implement the method as described in claim 1.

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