SAR image interference suppression method based on frequency domain signal recovery

Abstract

The invention discloses a synthetic aperture radar (SAR) image interference suppression method based on frequency domain signal recovery, which comprises the following steps of: receiving synthetic aperture radar image data containing radio frequency interference, and performing Fourier transform on the image data along a distance dimension to obtain two-dimensional frequency spectrums of a distance frequency domain and an azimuth time domain; kurtosis is calculated for the two-dimensional frequency spectrum, and an outlier detection threshold value is set based on the kurtosis; interference detection is carried out on the kurtosis, sampling points larger than a threshold value are regarded as interference, and a one-dimensional mask is constructed; constructing an effective data selection matrix based on the one-dimensional mask; constructing an amplitude guiding matrix; extracting frequency domain pulses of each row in the two-dimensional frequency spectrum, and constructing an amplitude spectrum and an effective data covariance matrix by using the frequency domain pulses, the amplitude guiding matrix and the effective data selection matrix; reconstructing time domain pulses corresponding to the frequency domain pulses; the reconstructed time domain pulse combination corresponding to all the frequency domain pulses is the SAR image after interference suppression. According to the invention, fine and effective suppression of the SLC image with interference is realized.

Description

Technical Field

[0001] The present invention relates to the field of radar signal processing, and in particular to a SAR image interference suppression method based on frequency domain signal recovery. Background Art

[0002] Spaceborne Synthetic Aperture Radar (SAR) has the characteristics of all-day, all-weather, and high-resolution earth observation. The radio frequency interference (RFI) received by spaceborne SAR is becoming more and more serious, reducing the quality of SAR images and affecting subsequent image interpretation. Among them, frequency domain notching uses the characteristics of RFI unidirectional propagation and high energy to set energy threshold interference suppression. It is an efficient and robust RFI suppression method and is a popular application in engineering practice.

[0003] Since the energy threshold selection lacks prior information, it is easy to cause residual interference or serious loss of useful signals. Part of the frequency domain signal is set to zero, which leads to the increase of side lobes at strong scattering points in the image, thereby greatly reducing the image quality at strong scattering points. The current main method for missing signal recovery is the Missing-data Iterative Adaptive Approach (MIAA). This method is mainly aimed at the recovery of time domain signals, and due to the large amount of calculation required and multiple iterations, it is difficult to achieve efficient interference suppression in the face of the huge amount of data from spaceborne SAR. Summary of the invention

[0004] The purpose of the present invention is to provide a SAR image interference suppression method based on frequency domain signal recovery, aiming to eliminate the sidelobe lifting phenomenon of strong scattering points after frequency domain notching, so as to achieve refined and effective suppression of SLC images with interference.

[0005] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0006] A SAR image interference suppression method based on frequency domain signal recovery, comprising:

[0007] Receive synthetic aperture radar image data containing radio frequency interference, perform Fourier transform on the image data along the range dimension to obtain a two-dimensional spectrum in the range frequency domain and the azimuth time domain;

[0008] Calculating the kurtosis of the two-dimensional spectrum and setting an outlier detection threshold based on the kurtosis; performing interference detection on the kurtosis, wherein sampling points greater than the threshold are considered to be interference, and constructing a one-dimensional mask;

[0009] constructing a valid data selection matrix based on the one-dimensional mask;

[0010] Construct an amplitude-oriented matrix;

[0011] Extract the frequency domain pulse of each row in the two-dimensional spectrum, and use the frequency domain pulse, amplitude steering matrix and effective data selection matrix to construct the amplitude spectrum and effective data covariance matrix:

[0012] Based on the spectrum amplitude and the effective data covariance matrix, the time domain pulses corresponding to the frequency domain pulses are reconstructed; then the reconstructed time domain pulse combination corresponding to all frequency domain pulses is the SAR image after interference suppression.

[0013] Further, the calculating the kurtosis for the two-dimensional spectrum and setting the outlier detection threshold based on the kurtosis includes:

[0014] For the two-dimensional spectrum X(f,η), calculate the kurtosis K of X(f,η) along the distance direction:

[0015]

[0016] Among them, X(f) is the vector of column-wise mean of X(f,η), is the mean of X(f), is the variance of X(f), x i ∈X(f), n is the length of X(f); f represents the range frequency, η represents the azimuth slow time, and E{·} represents the expectation;

[0017] The threshold Thr is expressed as:

[0018] Thr=μ K -3σ X (2)

[0019] in, is the mean value of kurtosis K, m is the length of the kurtosis K.

[0020] Furthermore, the construction of a one-dimensional mask is expressed as:

[0021] Interference detection is performed on the kurtosis K, where the sampling point x greater than the threshold Thr i It is considered to be interference; the interference one-dimensional mask is Mask1D, the sampling point where the interference is located is set to 1, and the other positions are 0. Mask1D is expressed as:

[0022]

[0023] Furthermore, constructing a valid data selection matrix based on the one-dimensional mask includes:

[0024] Convert the one-dimensional mask Mask1D to a size N g ×N effective data selection matrix S g ; where Ng is the number of Mask1D values ​​​​that are 0, and N is the number of pulse sampling points;

[0025] Effective data selection matrix S g It is expressed as:

[0026]

[0027] Among them, Mask1D[k] represents the kth element in the one-dimensional mask Mask1D, {k|Mask1D[k]=0} represents the vector composed of the positions where all elements in Mask1D are 0, and {i} represents the i-th value of the vector.

[0028] Furthermore, the amplitude steering matrix is ​​constructed as:

[0029] A H =[α(ω 1 ) α(ω 2 ) ... α(ω K )] (6)

[0030]

[0031] in,(·) H represents the conjugate transpose, ω k =(2π / K)k, k=1,2,...,K, ωk is the frequency of the created grid, K is the number of amplitude spectrum resolution points, j is the imaginary unit, and e is a natural constant.

[0032] Furthermore, the frequency domain pulse, amplitude steering matrix and effective data selection matrix are used to construct the amplitude spectrum and effective data covariance matrix, which is expressed as:

[0033] P=(A H y·(A H y) H ) / N (8)

[0034] R g =A g H PA g (9)

[0035] Among them, A g =S g A is a valid data-oriented matrix.

[0036] Furthermore, the time domain pulse X after frequency domain recovery is expressed as:

[0037]

[0038] A terminal device comprises a processor, a memory and a computer program stored in the memory; when the processor executes the computer program, the SAR image interference suppression method based on frequency domain signal recovery is implemented.

[0039] A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the SAR image interference suppression method based on frequency domain signal recovery is implemented.

[0040] Compared with the prior art, the present invention has the following technical features:

[0041] The method of the present invention can realize the refined interference suppression of SAR level 1 images containing radio frequency interference; after suppressing the radio frequency interference component by using kurtosis and outlier detection in the range frequency domain, the useful signal frequency domain component is restored, which effectively avoids the problem of sidelobe enhancement of strong scattering points of the image caused by the traditional frequency domain notch method, and the image after the frequency domain signal is restored has a large improvement in the indicators of equivalent view number and image entropy, and the image quality is greatly improved, which plays an important role in subsequent applications such as SAR image interpretation and target detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the process of the present invention;

[0043] Figure 2 is an SLC image with interference in an embodiment of the present invention;

[0044] Figure 3 It is a two-dimensional distance frequency domain-azimuth time domain diagram in an embodiment of the present invention;

[0045] Figure 4 This is a frequency domain kurtosis detection result diagram in an embodiment of the present invention;

[0046] Figure 5 A one-dimensional interference mask image in an embodiment of the present invention;

[0047] Figure 6 It is a two-dimensional range frequency domain-azimuth time domain diagram after interference suppression and signal recovery in an embodiment of the present invention;

[0048] Figure 7 This is an SLC image after interference suppression and signal recovery in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present invention provides a SAR image interference suppression method based on frequency domain signal recovery. According to the characteristics of unidirectional propagation and strong energy of radio frequency interference, the interference pulse is filtered by using KL divergence and outlier detection in the range frequency domain. The MIAA algorithm is improved by the missing signal model, and the frequency domain signal recovery with frequency domain zeroing is completed by using the sparsity of the SAR image in the time domain. The specific steps of the method of the present invention are as follows:

[0050] Step 1, receiving synthetic aperture radar (Single Look Complex, SLC) image data X(t,η) containing radio frequency interference, where t and η represent the fast time of range and the slow time of azimuth respectively; performing Fourier transform on the image data X(t,η) along the range dimension to obtain a two-dimensional spectrum X(f,η) in the range frequency domain and the azimuth time domain, where f represents the range frequency.

[0051] Step 2, calculate the kurtosis K for the two-dimensional spectrum X(f,η), set the outlier detection threshold based on the kurtosis K; perform interference detection on the kurtosis K, wherein sampling points greater than the threshold are considered to be interference, and construct a one-dimensional mask Mask1D.

[0052] For the two-dimensional spectrum X(f,η) obtained in step 1, calculate the kurtosis K of X(f,η) along the distance direction:

[0053]

[0054] Among them, X(f) is the vector of column-wise mean of X(f,η), is the mean of X(f), is the variance of X(f), x i ∈X(f), n is the length of X(f).

[0055] Kurtosis is a statistical feature. Interference signals usually cause the distribution of received signals to deviate from the normal distribution. Perform outlier detection on the kurtosis K to locate the interference position and obtain the sampling point position of the interference in the distance frequency domain. Use the threshold for outlier detection. The threshold Thr can be expressed as:

[0056] Thr=μ K -3σ X (2)

[0057] in, is the mean value of kurtosis K, m is the length of the kurtosis K.

[0058] Interference detection is performed on the kurtosis K, where the sampling points greater than the threshold are considered to be interference; the interference one-dimensional mask is Mask1D, the sampling points where the interference is located are set to 1, and the other positions are 0. Mask1D can be expressed as:

[0059]

[0060] Step 3: construct a valid data selection matrix S based on the one-dimensional mask g .

[0061] Convert the interference one-dimensional mask Mask1D obtained in step 2 to a size of N g ×N effective data selection matrix S g ; where N g is the number of Mask1D values ​​that are 0, and N is the number of pulse sampling points.

[0062] Effective data selection matrix S g It can be expressed as:

[0063]

[0064] Among them, Mask1D[k] represents the kth element in the one-dimensional mask Mask1D, {k|Mask1D[k]=0} represents the vector composed of the positions where all elements in Mask1D are 0, and {i} represents the i-th value of the vector.

[0065] Step 4: construct the amplitude-steering matrix A.

[0066] A H =[α(ω 1 ) α(ω 2 ) ... α(ω K )] (6)

[0067]

[0068] in,(·) H represents the conjugate transpose, ω k =(2π / K)k, k = 1, 2, ..., K, ωk is the created grid frequency, K is the number of amplitude spectrum resolution points, j is the imaginary unit, and e is a natural constant. H is the frequency grid created, and ω k This is the frequency corresponding to the grid.

[0069] Step 5: extract the frequency domain pulse y of each row in the two-dimensional spectrum X(f,η), and use the frequency domain pulse y, the steering matrix A and the effective data selection matrix S g Construct the amplitude spectrum P and the effective data covariance matrix R g :

[0070] P=(A H y·(A H y) H ) / N (8)

[0071] Rg =A g H PA g (9)

[0072] Among them, A g =S g A is a valid data-oriented matrix.

[0073] Step 6: Based on the spectrum amplitude P and the effective data covariance matrix R g , reconstruct the time domain pulse X corresponding to the frequency domain pulse y; then the combination of the reconstructed time domain pulses X corresponding to all frequency domain pulses is the SAR image X after interference suppression. m (t,η).

[0074] Among them, starting from step 4, the processing is performed pulse by pulse. After each frequency domain pulse y is processed by steps 4 to 6, the time domain pulse X after frequency domain recovery can be obtained:

[0075]

[0076] Example:

[0077] Step 1: Figure 1 FIG. 1 is a flow chart of a method for frequency domain outlier detection and recovery of SAR image interference suppression according to an embodiment of the present invention. Receive the measured SLC image data X(t, η) of SAR containing radio frequency interference, such as Figure 2 As shown, the size of the SAR measured SLC image selected in this embodiment is 2252×2556, t and η represent the fast time of range and the slow time of azimuth respectively, and Fourier transform is performed along the range dimension to obtain the range spectrum and the two-dimensional spectrum X(f,η) of the range frequency domain and the azimuth time domain, where f represents the range frequency domain, as shown in Figure 3 shown.

[0078] Step 2: For the two-dimensional spectrum X(f,η) obtained in step 1, calculate the kurtosis K of X(f,η) along the distance direction.

[0079]

[0080] Among them, X(f) is the vector of column-wise mean of X(f,η), is the mean of X(f), is the variance of X(f), x i ∈X(f), n is the length of X(f), and in this embodiment, n is 2252. Kurtosis is a statistical feature, and interference signals usually cause the distribution of received signals to deviate from the normal distribution, such as Figure 4 As shown. Perform outlier detection on the kurtosis K to locate the interference position, and obtain the sampling point position where the interference is located in the distance frequency domain. Use the threshold for outlier detection, and the threshold Thr can be expressed as:

[0081] Thr=μ X -3σ X (2)

[0082] in, is the mean value of kurtosis K, m is the length of the kurtosis K, which is 2556 in this embodiment. The interference one-dimensional mask is Mask1D, where the interference sampling point is 1 and the other positions are 0, such as Figure 5 As shown, Mask1D can be expressed as:

[0083]

[0084] Step 3: Convert the interference one-dimensional mask Mask1D obtained in step 2 to a size of N g ×N effective data selection matrix S g and size N m ×N missing data selection matrix S m , N g is the number of Mask1D values ​​​​that are 0. In this embodiment, N g =2413. Effective data selection matrix S g It can be expressed as

[0085]

[0086] Among them, Mask1D[k] represents the kth element in the one-dimensional mask Mask1D, {k|Mask1D[k]=0} represents the vector composed of the positions where all elements in Mask1D are 0, and {i} represents the i-th value of the vector.

[0087] Step 4: Process pulse by pulse along the azimuth direction to convert the frequency domain pulse y into interference-free data y g =S g y. And construct the magnitude steering vector α(ω K ) and the steering matrix A, the calculation expression is:

[0088] A H =[α(ω 1 ) α(ω 2 ) ... α(ω K )] (6)

[0089]

[0090] in,(·) H represents the conjugate transpose, ω k=(2π / K)k, k=1,2,...,K, ωk is the created grid frequency, K is the number of amplitude spectrum resolution points, N is the number of pulse sampling points, and in this embodiment, K=2556. The higher the value of K, the more accurate the signal recovery is, but it will increase the algorithm running time.

[0091] Step 5: construct the amplitude spectrum P and the effective data covariance matrix R according to the frequency domain pulse y processed pulse by pulse in step 4 and the steering matrix A g

[0092] P=(A H y·(A H y) H ) / N (8)

[0093] R g =A g H PA g (9)

[0094] Among them, A g =S g A is the effective data-oriented matrix, and N is the number of pulse sampling points.

[0095] Step 6: Use the spectrum amplitude P and effective data covariance R obtained in step 5 g , the time domain pulse X corresponding to the reconstructed data:

[0096]

[0097] Starting from step 4, the pulses are processed one by one. After each pulse frequency domain y is processed through steps 4 to 6, the time domain pulse X after frequency domain recovery can be obtained, and finally the SAR image X after interference suppression can be obtained. m (t, η). In this embodiment, the two-dimensional distance frequency domain-azimuth time domain after interference suppression and signal recovery is as follows: Figure 6 As shown, the SAR image after interference suppression and signal recovery is as follows Figure 7 shown.

[0098] The strong scattering point slices after frequency domain signal recovery have achieved significant improvements in the indicators of equivalent view number and image entropy, as shown in Table 1. The image quality has been greatly improved, which plays an important role in subsequent applications such as SAR image interpretation and target detection. In this embodiment, the equivalent view number of the original interfered SAR image is 0.3285, and the image entropy is 0.0077. The equivalent view number of the SAR image after interference suppression and signal recovery is 0.1725, and the image entropy is 0.0930.

[0099] Table 1 SAR image evaluation indicators before and after interference suppression

[0100] Image quality assessment Equivalent View Number Image Entropy Original interference SAR image 0.3285 0.0077 SAR image after interference suppression 0.1725 0.0930

[0101] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A SAR image interference suppression method based on frequency domain signal recovery, characterized in that: include: Receive synthetic aperture radar image data containing radio frequency interference, perform Fourier transform on the image data along the range dimension to obtain a two-dimensional spectrum in the range frequency domain and the azimuth time domain; Calculating the kurtosis of the two-dimensional spectrum and setting an outlier detection threshold based on the kurtosis; performing interference detection on the kurtosis, wherein sampling points greater than the threshold are considered to be interference, and constructing a one-dimensional mask; constructing a valid data selection matrix based on the one-dimensional mask; Construct an amplitude-oriented matrix; Extract the frequency domain pulse of each row in the two-dimensional spectrum, and use the frequency domain pulse, amplitude steering matrix and effective data selection matrix to construct the amplitude spectrum and effective data covariance matrix: Based on the spectrum amplitude and the effective data covariance matrix, the time domain pulses corresponding to the frequency domain pulses are reconstructed; then the reconstructed time domain pulse combination corresponding to all frequency domain pulses is the SAR image after interference suppression.

2. The SAR image interference suppression method based on frequency domain signal recovery according to claim 1 is characterized in that: The step of calculating the kurtosis of the two-dimensional spectrum and setting the outlier detection threshold based on the kurtosis includes: For the two-dimensional spectrum X(f,η), calculate the kurtosis K of X(f,η) along the distance direction: Among them, X(f) is the vector of column-wise mean of X(f,η), is the mean of X(f), is the variance of X(f), x i ∈X(f), n is the length of X(f); f represents the range frequency, η represents the azimuth slow time, and E{·} represents the expectation; The threshold Thr is expressed as: Thr=μ K -3s X (2) in, is the mean value of kurtosis K, x j ∈K, m is the length of the kurtosis K.

3. The SAR image interference suppression method based on frequency domain signal recovery according to claim 1 is characterized in that: The one-dimensional mask is constructed as: Interference detection is performed on the kurtosis K, where the sampling point x greater than the threshold Thr i It is considered to be interference; the interference one-dimensional mask is Mask1D, the sampling point where the interference is located is set to 1, and the other positions are 0. Mask1D is expressed as:

4. The SAR image interference suppression method based on frequency domain signal recovery according to claim 1, characterized in that: Constructing an effective data selection matrix based on the one-dimensional mask, comprising: Convert the one-dimensional mask Mask1D to a size N g ×N effective data selection matrix S g ; where N g is the number of Mask1D values ​​​​that are 0, and N is the number of pulse sampling points; Effective data selection matrix S g It is expressed as: Among them, Mask1D[k] represents the kth element in the one-dimensional mask Mask1D, {k|Mask1D[k]=0} represents the vector composed of the positions where all elements in Mask1D are 0, and {i} represents the i-th value of the vector.

5. The SAR image interference suppression method based on frequency domain signal recovery according to claim 1, characterized in that: The amplitude steering matrix is ​​constructed as follows: A H =[α(ω1) α(ω2) ... α(ω K )] (6) in,(·) H represents the conjugate transpose, ω k =(2π / K)k, k=1,2,...,K, ωk is the frequency of the created grid, K is the number of amplitude spectrum resolution points, j is the imaginary unit, and e is a natural constant.

6. The SAR image interference suppression method based on frequency domain signal recovery according to claim 1, characterized in that: The frequency domain pulse, amplitude steering matrix and effective data selection matrix are used to construct the amplitude spectrum and effective data covariance matrix, which is expressed as: P=(A H already H and) H ) / N (8) R g =A g H PA g (9) Among them, A g =S g A is a valid data-oriented matrix.

7. The SAR image interference suppression method based on frequency domain signal recovery according to claim 1, characterized in that: The time domain pulse X after frequency domain recovery is expressed as:

8. A terminal device comprising a processor, a memory and a computer program stored in the memory; characterized in that: When the processor executes the computer program, the SAR image interference suppression method based on frequency domain signal recovery according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, wherein a computer program is stored in the medium; characterized in that: When the computer program is executed by a processor, the SAR image interference suppression method based on frequency domain signal recovery according to any one of claims 1 to 7 is implemented.

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