Single-bit SAR (Synthetic Aperture Radar) signal recovery method and device based on sparsity judgment

Abstract

The invention provides a single-bit SAR signal recovery method and device based on sparsity judgment, and relates to the technical field of signal processing, and the method comprises the steps: carrying out the range migration correction of a to-be-recovered single-bit quantization satellite-borne SAR echo signal, and obtaining a two-dimensional linear frequency modulation signal; performing space grid division on the target imaging area, and establishing an SAR imaging observation model in grid points obtained by division according to the two-dimensional linear frequency modulation signal; establishing a sparseness optimization model according to the SAR imaging observation model, and performing iterative updating on the sparseness optimization model to obtain an optimal estimated value of the scene sparseness; and recovering the corrected echo signal according to the optimal estimation value to obtain a reconstructed echo signal conforming to the quality standard. According to the scheme, the problem that target scene sparsity is difficult to obtain in the imaging processing process can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to a single-bit SAR signal recovery method and device based on sparsity judgment. Background Art

[0002] In recent years, with the continuous development of spaceborne SAR (synthetic aperture radar) technology, observation capabilities have been continuously improved. However, the demand for high-resolution and wide-band observations has led to an increasing amount of SAR echo data, which has intensified the pressure on satellite-to-ground data transmission, inter-satellite data transmission, and inter-satellite coordination. Under some application conditions, the imaging results do not require very strict radiation accuracy, but require faster data transmission, processing, and distribution capabilities. Therefore, single-bit SAR technology came into being.

[0003] Related technologies usually utilize sparse priors and restore the relative amplitude of echoes by using optimization algorithms to reduce the interference caused by strong targets in the imaging results. However, the solution of the optimization algorithm needs to be performed under the condition of known sparsity, which makes it impossible to improve the SAR image quality in the actual imaging process when the sparsity is unknown.

[0004] Based on this, there is an urgent need for a single-bit SAR signal recovery method and device based on sparsity judgment to solve the above technical problems. Summary of the invention

[0005] The embodiments of the present invention provide a single-bit SAR signal recovery method and device based on sparsity judgment, which can solve the problem that the sparsity of the target scene is difficult to obtain during the imaging process.

[0006] In a first aspect, an embodiment of the present invention provides a single-bit SAR signal recovery method based on sparsity judgment, comprising:

[0007] Performing range migration correction on the single-bit quantized spaceborne SAR echo signal to be recovered to obtain a two-dimensional linear frequency modulation signal; the two-dimensional linear frequency modulation signal includes two dimensions, azimuth and range;

[0008] Performing spatial grid division on the target imaging area, and establishing a SAR imaging observation model in the grid points obtained by the division according to the two-dimensional linear frequency modulation signal;

[0009] Establishing a sparsity optimization model according to the SAR imaging observation model, and iteratively updating the sparsity optimization model to obtain an optimal estimate of the scene sparsity;

[0010] The corrected echo signal is restored according to the optimal estimation value to obtain a reconstructed echo signal that meets the quality standard.

[0011] In a second aspect, an embodiment of the present invention further provides a single-bit SAR signal recovery device based on sparsity judgment, comprising:

[0012] A correction module is used to perform range migration correction on the single-bit quantized spaceborne SAR echo signal to be recovered to obtain a two-dimensional linear frequency modulation signal; the two-dimensional linear frequency modulation signal includes two dimensions, azimuth and range;

[0013] A modeling module, used for performing spatial grid division on the target imaging area, and establishing a SAR imaging observation model in the grid points obtained by the division according to the two-dimensional linear frequency modulation signal;

[0014] An updating module, used for establishing a sparsity optimization model according to the SAR imaging observation model, and iteratively updating the sparsity optimization model to obtain an optimal estimate of the scene sparsity;

[0015] The recovery module is used to recover the corrected echo signal according to the optimal estimation value to obtain a reconstructed echo signal that meets the quality standard.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the method described in any embodiment of this specification.

[0018] The embodiment of the present invention provides a single-bit SAR signal recovery method and device based on sparsity judgment. The method is based on the sparse prior characteristics of the scene and adopts a sparse optimization algorithm. In order to solve the problem of unknown sparsity in the actual imaging scene, the sparsity judgment is first performed before the echo signal is recovered, and the sparsity estimation value of the scene is obtained through iteration. Then, the orthogonal matching pursuit algorithm is used to recover the single-bit echo signal. The method solves the problem that the sparsity of the target scene is difficult to obtain during the imaging process by estimating the sparsity of the scene using an adaptive iterative algorithm. At the same time, compared with the traditional method based on compressed sensing, the method reduces the spatial complexity of the algorithm by replacing the vector observation model with a matrix observation model, and can be applied to wide-width imaging scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 is a flow chart of a single-bit SAR signal recovery method based on sparsity judgment provided by an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of a point target imaging result provided by an embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of false target intensity comparison provided by an embodiment of the present invention;

[0023] Figure 4 It is a schematic diagram of the X-band surface target echo simulation imaging result provided by an embodiment of the present invention;

[0024] Figure 5 It is a schematic diagram of a C-band surface target echo simulation imaging result provided by an embodiment of the present invention;

[0025] Figure 6 It is a schematic diagram of the imaging result of the Haisi-1 SAR echo data provided by an embodiment of the present invention;

[0026] Figure 7 is a hardware architecture diagram of an electronic device provided by an embodiment of the present invention;

[0027] Figure 8 It is a structural diagram of a single-bit SAR signal recovery device based on sparsity judgment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0029] As mentioned above, in the prior art, the sparsity is often unknown in the actual imaging process, which makes it difficult to obtain high-quality imaging that meets the requirements after restoring the echo signal.

[0030] Based on this, the concept of the present invention is to first perform sparsity judgment before restoring the echo signal, obtain the sparsity estimation value of the scene through iteration, and then use the orthogonal matching pursuit algorithm to restore the single-bit echo signal.

[0031] The specific implementation of the above concept is described below.

[0032] Please refer to Figure 1 The embodiment of the present invention provides a single-bit SAR signal recovery method based on sparsity judgment, the method comprising:

[0033] Step 100, performing range migration correction on the single-bit quantized spaceborne SAR echo signal to be recovered to obtain a two-dimensional linear frequency modulation signal; the two-dimensional linear frequency modulation signal includes two dimensions, azimuth and range;

[0034] Step 102, performing spatial grid division on the target imaging area, and establishing a SAR imaging observation model in the grid points obtained by the division according to the two-dimensional linear frequency modulation signal;

[0035] Step 104, establishing a sparsity optimization model according to the SAR imaging observation model, and iteratively updating the sparsity optimization model to obtain an optimal estimate of the scene sparsity;

[0036] Step 106: restore the corrected echo signal according to the optimal estimation value to obtain a reconstructed echo signal that meets the quality standard.

[0037] In the embodiment of the present invention, based on the sparse prior characteristics of the scene, a sparse optimization algorithm is used. To address the problem of unknown sparsity in the actual imaging scene, a sparsity judgment is first performed before the echo signal is restored, and the sparsity estimation value of the scene is obtained through iteration. Then, an orthogonal matching pursuit algorithm is used to restore the single-bit echo signal. This method solves the problem that the sparsity of the target scene is difficult to obtain during the imaging process by estimating the sparsity of the scene using an adaptive iterative algorithm. At the same time, compared with the traditional method based on compressed sensing, this method reduces the spatial complexity of the algorithm by replacing the vector observation model with a matrix observation model, and can be applied to wide-area imaging scenes.

[0038] Described below Figure 1 How the various steps are performed.

[0039] First, with respect to step 100, the single-bit quantized spaceborne SAR echo signal to be recovered is subjected to range migration correction to obtain a two-dimensional linear frequency modulation signal.

[0040] Due to range migration, there are coupling terms between the azimuth and range parts of the SAR echo signal, which will lead to observation model mismatch. Therefore, it is necessary to perform range migration correction processing to decouple the azimuth signal and the range signal.

[0041] In the embodiment of the present invention, the range migration correction process includes: transforming the echo signal into the range Doppler domain, and performing residual range migration correction using a linear frequency modulation scaling technique to obtain first correction data, so that the range migration curves at different range gates are corrected to be the same as the range migration curves at the reference slant range;

[0042] transforming the first correction into a two-dimensional frequency domain according to a range Fourier transform, and performing a consistent range migration correction in the two-dimensional frequency domain to obtain second correction data;

[0043] The second corrected data is transformed into the range Doppler domain according to the inverse Fourier transform in the range direction, and the transformed second corrected data is multiplied by the azimuth compensation factor to obtain a two-dimensional linear frequency modulation signal; wherein the compensation factor is used to compensate for the residual phase introduced by the linear scaling; the two-dimensional linear frequency modulation signal includes two dimensions, azimuth and range.

[0044] The specific calculation process in the above steps is well known to those skilled in the art and will not be described in detail here.

[0045] Then, with respect to step 102, the target imaging area is spatially gridded, and a SAR imaging observation model is established in the grid points obtained by the division according to the two-dimensional linear frequency modulation signal.

[0046] In the embodiment of the present invention, the SAR imaging observation model is established in the following manner:

[0047] The target imaging area is divided into P×Q spatial grids; wherein the number of the spatial grids is determined by the width, range gate and azimuth gate of the target imaging area, and each value in the grid represents a backscattering coefficient with the grid as the scattering center;

[0048] The SAR imaging observation model is established according to the two-dimensional linear frequency modulation signal:

[0049]

[0050] Wherein, S is the echo signal matrix; X is the original sparse matrix directly established according to the two-dimensional linear frequency modulation signal; Φ A is the azimuth observation matrix composed of the azimuth linear frequency modulation signal time shift; Φ R The range observation matrix is ​​composed of the time shift of the range linear frequency modulation signal.

[0051] Specifically, the scene is divided into P×Q spatial grids. To restore the echo using the single-bit compressed sensing theory, it is necessary to first establish a SAR imaging observation model and divide the SAR echo signal into the range part and the azimuth part as follows:

[0052]

[0053] Where t is the azimuth time; τ is the distance time; T a is the target illumination time; c is the speed of light; is the distance between the grid point and the radar; T p is the pulse width; k r To adjust the frequency.

[0054] After the range migration correction, the SAR echo signal is decoupled into a two-dimensional linear frequency modulation signal, thereby obtaining the matrix observation model of the SAR echo signal. Among them, Φ A is the azimuth observation matrix, which is composed of the azimuth linear frequency modulation signal time shift:

[0055]

[0056] in, Na is the number of echo signal azimuth points, f c is the carrier frequency; R a (i) is the distance between the grid point in the azimuth direction and the radar when the range direction is at the reference distance.

[0057] Φ R is the range observation matrix, which is composed of the time shift of the range linear frequency modulation signal:

[0058]

[0059] in, R r (j) is the distance between the grid point in the range direction and the radar when the azimuth direction is at the reference distance.

[0060] With respect to step 104, a sparsity optimization model is established according to the SAR imaging observation model, and the sparsity optimization model is iteratively updated to obtain an optimal estimation value of the scene sparsity.

[0061] In the embodiment of the present invention, the optimal estimate is obtained by:

[0062] A sparsity optimization model is established based on the SAR imaging observation model:

[0063]

[0064] in, is a reconstructed sparse matrix; Y is an echo matrix of the two-dimensional linear frequency modulation signal;

[0065] The sparsity optimization model is iteratively updated according to the orthogonal matching pursuit algorithm to obtain the optimal estimate:

[0066] S11, selecting a plurality of atoms satisfying the measurement matrix correlation standard from the two-dimensional linear frequency modulation signal to form a support set;

[0067] S12, calculating the inner product of the support set and the echo matrix of the two-dimensional linear frequency modulation signal;

[0068] S13, judging whether the calculation result satisfies the RIP constraint condition, and updating the sparsity estimation value according to the judgment result to complete an iterative calculation:

[0069] Determine the initial value k of the sparsity estimate:

[0070] k=(k a +k b ) / 2

[0071] Among them, k a =0, k b =M, M is the upper limit of sparsity;

[0072] Determine whether the calculation result satisfies the RIP constraint condition, if:

[0073]

[0074] Then it is determined that the sparsity estimate is greater than the target estimate, and k b The value of is updated to the estimated sparsity value at this time; among them, are the atoms of the supporting set; is the inner product of the support set and the echo matrix after the range migration correction; 2K is the confidence of the RIP constraint;

[0075] like:

[0076]

[0077] Then it is determined that the sparsity estimate is less than the target estimate, and k a The value of is updated to the estimated sparsity at that time.

[0078] S14, repeating steps S1-S4 until the number of iterations is reached or the calculation results simultaneously satisfy all RIP constraints, and outputting the optimal estimated value of the sparsity.

[0079] Specifically, according to the SAR imaging observation model, the compressed sensing algorithm can be used to restore the single-bit quantized echo signal and reconstruct the sparse scene. First, the problem is formulated as the following optimization problem:

[0080]

[0081] There are many methods to solve the above optimization problem, such as the orthogonal matching pursuit algorithm. This algorithm selects one or a group of atoms that are most relevant to the observation matrix from the signal as the support set in each iteration, then restores the signal and calculates the residual with the original signal. New atoms are selected through the residual to update the support set, thereby achieving the purpose of signal reconstruction.

[0082] For example, let Λ=sup(X) be the support set of the original sparse matrix X, and use |·| to represent the sparsity of the set, then |Λ|=K, where K is the target sparsity.

[0083] ||X||0=|sup(X)|

[0084]

[0085] Among them, X Λ represents the matrix composed of elements indexed by Λ in X. Let φ a Φ A A column of r Φ R If a column of is called an atom and denoted by Φ Λ Represents a submatrix with Λ as index.

[0086] Furthermore, the orthogonal matching pursuit algorithm needs to preset the sparsity of the scene before iteration, but the sparsity of the scene is unknown during actual imaging, so it is necessary to estimate the sparsity of the scene first. A set of atoms can be obtained through matching tests, so that its potential is slightly smaller than the actual sparsity. Then the element r in the i-th row and j-th column of V i,j Represents the atom Φ T The inner product with the matrix Y, that is, <Φ T ,Y>. Take the first k maximum value indexes in V to get the set Λ 0 ,|Λ 0 |=k. Assume that the atom Φ satisfies the parameters (2K,δ 2K )'s RIP property, if Then the estimated sparsity k>K, and the above is used as the basis for judging whether the sparsity is overestimated. Then the estimated sparsity k<K.

[0087] Furthermore, according to the above sparsity judgment basis, the appropriate sparsity can be estimated in the iterative process under the condition that the sparsity is unknown. The specific process is shown in the following example: First, let k a =0, k b =M, where M is the upper limit of sparsity. In the iterative solution process, firstly, the sparsity k=(k a +k b ) / 2 Test, if We know that k>K, so let k b =k; if We know that k>K, so let k b =k. Update the sparsity estimate k before the next iteration = (k a +k b ) / 2, and try until the constraints of the two inequalities are satisfied at the same time or the maximum number of iterations is reached, thus ending the iteration and outputting the sparsity estimate at this time as the optimal estimate.

[0088] With respect to step 106, the corrected echo signal is restored according to the optimal estimation value to obtain a reconstructed echo signal that meets the quality standard.

[0089] In the embodiment of the present invention, the recovery process includes the following steps:

[0090] S21, performing recovery initialization according to the input two-dimensional linear frequency modulation signal, the azimuth measurement matrix and the range measurement matrix of the two-dimensional linear frequency modulation signal, and the optimal estimation value; wherein the recovery initialization includes residual initialization, atomic set initialization and iteration number initialization;

[0091] S22, selecting an atom most relevant to the current residual, and calculating the corresponding sparse matrix by the least square method; adding the selected atom to the atom set;

[0092] S23, updating the next residual R according to the sparse matrix j+1 , and end an iterative calculation:

[0093] R j+1 =Y-Φ j X j

[0094] Wherein, Y is the echo matrix of the two-dimensional linear frequency modulation signal; Φ j is the atom most correlated with the residual at the jth iteration; X j is the sparse matrix calculated by the least squares method at the jth iteration;

[0095] S24, determine whether the number of iterations is equal to the optimal sparsity, if so, end the iteration and output the reconstructed echo signal

[0096]

[0097] Among them, X0 is the sparse matrix at the last iteration;

[0098] Otherwise, steps S21-S23 are repeated until the number of iterations is the same as the optimal sparsity.

[0099] For example, input: single-bit quantized SAR echo The observation matrix Φ A ,Φ R , sparsity K. Output: single-bit reconstructed echo

[0100] Initialize the residual R 0 =Y, the selected atom set Ω0 is empty, the number of iterations j = 0; select an atom that is most relevant to the current residual, that is, the matrix inner product | <φ a,i φ r,j ,R k-1 >|largest atomic lambda k , add the selected atoms to the atomic set Ω j In the example, the set is set to accommodate the atoms selected each time, thereby avoiding repeated selection of atoms; the sparse matrix X is solved by the least squares method. j ; Update residual R j+1 =Y-ΦX j , j=j+1, determine whether the iteration number j reaches the sparsity K, if not, repeat the above steps, if it reaches, end the iteration and output the reconstructed echo

[0101] The feasibility of the above method is demonstrated by an embodiment below:

[0102] The simulation computing environment configured in this embodiment is Intel(R) Core(TM) i7-11800H@2.30GHz, 64GB memory, 64-bit Microsoft Windows 11 system, and the simulation software uses MATLAB (R2020a).

[0103] The point target scene used in the simulation is a 2*2 dot matrix target with a dot matrix interval of 200m. The size of the imaging area is 1000m*1000m, divided into 500*500 grids. The other point target simulation parameters are shown in Table 1 below:

[0104] Table 1

[0105]

[0106] Figure 2The imaging results of the original echo, the single-bit quantized echo and the echo restored after single-bit quantization are given. Figure 2 The comparison results show that after the echo signal is quantized by single bit, the side lobes at the real point target are lifted and paired false targets appear due to the influence of quantization noise and cross-modulation of the amplitude of strong scattering points. After the echo is restored by the proposed algorithm, the original target is accurately restored and the point target side lobes and false targets are significantly suppressed. Since the main application scenario of this algorithm is the sparse ship scenario in the ocean, the false targets introduced by single-bit quantization will seriously affect the subsequent ship target detection in actual application. Therefore, it is necessary to focus on the algorithm's suppression effect on false targets. Figure 3 The intensity comparison at the false target is given. It can be seen from the figure that the relative intensity of the false target caused by single-bit quantization is above -10dB, and after single-bit quantization echo recovery, the intensity at this position drops to below -45dB, and the suppression effect of false targets is above 30dB, indicating that this method has an excellent suppression effect on false targets.

[0107] Figure 4 This is the experimental result of X-band surface target echo simulation imaging. The simulation scene is a ship scene at sea. The X-band surface target simulation parameters are shown in Table 2 below:

[0108] Table 2

[0109]

[0110] Figure 4 The original scene (a) is a distant sea ship target, (b) is the imaging result after the echo signal is single-bit quantized, and it can be seen that many false targets appear in the imaging result. (c) is the result of restoring the echo signal after single-bit quantization and then imaging, and the false targets are significantly suppressed.

[0111] Figure 5 This is the experimental result of C-band surface target echo simulation imaging. The simulation scene is a ship scene at sea. The C-band surface target simulation parameters are shown in Table 3 below:

[0112] Table 3

[0113]

[0114]

[0115] Figure 5 The original scene (a) is a distant sea ship target, (b) is the imaging result after the echo signal is single-bit quantized, and it can be seen that many false targets appear in the imaging result. (c) is the result of restoring the echo signal after single-bit quantization and then imaging, and the false targets are significantly suppressed.

[0116] Finally, the processing results of the real echo data of the Haisi-1 satellite are shown. The imaging scene is also a ship target at sea. The original echo data, the echo data after single-bit quantization, and the single-bit echo data restored by the proposed method are imaged and processed respectively. Figure 6 The comparison of imaging results is shown. The target in the red box is the real target, and the target in the green box is the false target caused by single-bit quantization. From the comparison of the results in the figure, it can be seen that the method proposed in the present invention can suppress the false target caused by single-bit quantization while preserving the real target.

[0117] like Figure 7 , Figure 8 As shown, an embodiment of the present invention provides a single-bit SAR signal recovery device based on sparsity judgment. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 7 As shown in FIG. 1 , a hardware architecture diagram of an electronic device in which a single-bit SAR signal recovery device based on sparsity judgment provided by an embodiment of the present invention is located is shown in FIG. Figure 7 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 8 As shown, as a device in a logical sense, the CPU of the electronic device in which it is located reads the corresponding computer program in the non-volatile memory into the internal memory and runs it. This embodiment provides a single-bit SAR signal recovery device based on sparsity judgment, including:

[0118] The correction module 800 is used to perform range migration correction on the single-bit quantized spaceborne SAR echo signal to be recovered to obtain a two-dimensional linear frequency modulation signal; the two-dimensional linear frequency modulation signal includes two dimensions, azimuth and range;

[0119] A modeling module 802 is used to perform spatial grid division on the target imaging area and establish a SAR imaging observation model in the grid points obtained by the division according to the two-dimensional linear frequency modulation signal;

[0120] An updating module 804 is used to establish a sparsity optimization model according to the SAR imaging observation model, and iteratively update the sparsity optimization model to obtain an optimal estimation value of the scene sparsity;

[0121] The recovery module 806 is used to recover the corrected echo signal according to the optimal estimation value to obtain a reconstructed echo signal that meets the quality standard.

[0122] In the embodiment of the present invention, when the correction module 800 performs range migration correction on the single-bit quantized spaceborne SAR echo signal to be recovered to obtain a two-dimensional linear frequency modulation signal, it is specifically used to perform the following operations: transform the echo signal into the range Doppler domain, and use the linear frequency modulation scaling technology to perform residual range migration correction to obtain first correction data, so that the range migration curves at different range gates are corrected to be the same as the range migration curve at the reference slant range; transform the first correction into the two-dimensional frequency domain according to the range Fourier transform, and perform consistent range migration correction in the two-dimensional frequency domain to obtain second correction data; transform the second correction data into the range Doppler domain according to the range inverse Fourier transform, and multiply the transformed second correction data by the azimuth compensation factor to obtain a two-dimensional linear frequency modulation signal; wherein the compensation factor is used to compensate for the residual phase introduced by the linear scaling.

[0123] In the embodiment of the present invention, when the modeling module 802 performs spatial grid division on the target imaging area and establishes the SAR imaging observation model in the grid points obtained by the division according to the two-dimensional linear frequency modulation signal, it is specifically used to perform the following operations:

[0124] The target imaging area is divided into P×Q spatial grids; wherein the number of the spatial grids is determined by the width, range gate and azimuth gate of the target imaging area, and each value in the grid represents a backscattering coefficient with the grid as the scattering center;

[0125] The SAR imaging observation model is established according to the two-dimensional linear frequency modulation signal:

[0126]

[0127] Wherein, S is the echo signal matrix; X is the original sparse matrix directly established according to the two-dimensional linear frequency modulation signal; Φ A is the azimuth observation matrix composed of the azimuth linear frequency modulation signal time shift; Φ R The range observation matrix is ​​composed of the time shift of the range linear frequency modulation signal.

[0128] In the embodiment of the present invention, when the updating module 804 establishes a sparsity optimization model according to the SAR imaging observation model and iteratively updates the sparsity optimization model to obtain the optimal estimated value of the scene sparsity, it is specifically used to perform the following operations: establish a sparsity optimization model according to the SAR imaging observation model:

[0129]

[0130] in, is a reconstructed sparse matrix; Y is an echo matrix of the two-dimensional linear frequency modulation signal;

[0131] The sparsity optimization model is iteratively updated according to the orthogonal matching pursuit algorithm to obtain the optimal estimate value.

[0132] In an embodiment of the present invention, the sparsity optimization model is solved according to the orthogonal matching pursuit algorithm to obtain the optimal estimate of sparsity when the symbols are consistent, including: S11, selecting multiple atoms that meet the observation matrix correlation standard from the two-dimensional linear frequency modulation signal to form a support set; S12, calculating the inner product of the support set and the echo matrix of the two-dimensional linear frequency modulation signal; S13, judging whether the calculation result meets the RIP constraint condition, and updating the sparsity estimate value according to the judgment result to complete an iterative calculation; S14, repeating steps S1-S4 until the number of iterations is reached or the calculation result satisfies all RIP constraints at the same time, and outputting the optimal estimate of sparsity.

[0133] In the embodiment of the present invention, determining the magnitude relationship between the calculation result and the RIP discriminant, and updating the sparsity estimation value according to the determination result, includes: determining an initial value k of the sparsity estimation value:

[0134] k=(k a +k b ) / 2

[0135] Among them, k a =0, k b =M, M is the upper limit of sparsity;

[0136] Determine whether the calculation result satisfies the RIP constraint condition, if:

[0137]

[0138] Then it is determined that the sparsity estimate is greater than the target estimate, and k b The value of is updated to the estimated sparsity value at this time; among them, are the atoms of the supporting set; is the inner product of the support set and the echo matrix after the range migration correction; 2K is the confidence of the RIP constraint;

[0139] like:

[0140]

[0141] Then it is determined that the sparsity estimate is less than the target estimate, and k a The value of is updated to the estimated sparsity at that time.

[0142] It is understandable that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a single-bit SAR signal recovery device based on sparsity judgment. In other embodiments of the present invention, a single-bit SAR signal recovery device based on sparsity judgment may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0143] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0144] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a single-bit SAR signal recovery method based on sparsity judgment in any embodiment of the present invention is implemented.

[0145] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes a single-bit SAR signal recovery method based on sparsity judgment in any embodiment of the present invention.

[0146] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can read and execute the program code stored in the storage medium.

[0147] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0148] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0149] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0150] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0151] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical factors in the process, method, article or device including the elements.

[0152] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention 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. However, 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 invention.

Claims

1. A single-bit SAR signal recovery method based on sparsity judgment, characterized in that: include: Performing range migration correction on the single-bit quantized spaceborne SAR echo signal to be recovered to obtain a two-dimensional linear frequency modulation signal; the two-dimensional linear frequency modulation signal includes two dimensions, azimuth and range; Performing spatial grid division on the target imaging area, and establishing a SAR imaging observation model in the grid points obtained by the division according to the two-dimensional linear frequency modulation signal; Establishing a sparsity optimization model according to the SAR imaging observation model, and iteratively updating the sparsity optimization model to obtain an optimal estimate of the scene sparsity; The corrected echo signal is restored according to the optimal estimation value to obtain a reconstructed echo signal that meets the quality standard.

2. The method according to claim 1, characterized in that The step of performing range migration correction on the read single-bit quantized satellite-borne SAR echo signal to obtain a two-dimensional linear frequency modulation signal includes: The echo signal is transformed into a range Doppler domain, and a linear frequency modulation scaling technique is used to perform residual range migration correction to obtain first correction data, so that the range migration curves at different range gates are corrected to be the same as the range migration curve at the reference slant range; transforming the first correction into a two-dimensional frequency domain according to a range Fourier transform, and performing a consistent range migration correction in the two-dimensional frequency domain to obtain second correction data; The second corrected data is transformed into the range Doppler domain according to the range inverse Fourier transform, and the transformed second corrected data is multiplied by the azimuth compensation factor to obtain a two-dimensional linear frequency modulation signal; wherein the compensation factor is used to compensate for the residual phase introduced by the linear scaling.

3. The method according to claim 1, characterized in that: The spatial grid division of the target imaging area and the establishment of a SAR imaging observation model in the grid points obtained by the division according to the two-dimensional linear frequency modulation signal include: The target imaging area is divided into P×Q spatial grids; wherein the number of the spatial grids is determined by the width, range gate and azimuth gate of the target imaging area, and each value in the grid represents a backscattering coefficient with the grid as the scattering center; The SAR imaging observation model is established according to the two-dimensional linear frequency modulation signal: Wherein, S is the echo signal matrix; X is the original sparse matrix directly established according to the two-dimensional linear frequency modulation signal; Φ A is the azimuth observation matrix composed of the azimuth linear frequency modulation signal time shift; Φ R The range observation matrix is ​​composed of the time shift of the range linear frequency modulation signal.

4. The method according to claim 3, characterized in that: The step of establishing a sparsity optimization model according to the SAR imaging observation model and iteratively updating the sparsity optimization model to obtain an optimal estimate of scene sparsity includes: A sparsity optimization model is established based on the SAR imaging observation model: in, is a reconstructed sparse matrix; Y is an echo matrix of the two-dimensional linear frequency modulation signal; The sparsity optimization model is iteratively updated according to an orthogonal matching pursuit algorithm to obtain the optimal estimate.

5. The method according to claim 4, characterized in that Solving the sparsity optimization model according to the orthogonal matching pursuit algorithm to obtain the optimal estimate of sparsity when the symbols are consistent includes: S11, selecting a plurality of atoms satisfying the measurement matrix correlation standard from the two-dimensional linear frequency modulation signal to form a support set; S12, calculating the inner product of the support set and the echo matrix of the two-dimensional linear frequency modulation signal; S13, judging whether the calculation result satisfies the RIP constraint condition, and updating the sparsity estimation value according to the judgment result to complete one iterative calculation; S14, repeating steps S1-S4 until the number of iterations is reached or the calculation results simultaneously satisfy all RIP constraints, and outputting the optimal estimated value of the sparsity.

6. The method according to claim 5, characterized in that The determining the relationship between the calculation result and the RIP discriminant, and updating the sparsity estimation value according to the determination result, includes: Determine the initial value k of the sparsity estimate: k=(k a +k b ) / 2 Among them, k a =0, k b =M, M is the upper limit of sparsity; Determine whether the calculation result satisfies the RIP constraint condition, if: Then it is determined that the sparsity estimate is greater than the target estimate, and k b The value of is updated to the estimated sparsity value at this time; among them, are the atoms of the supporting set; is the inner product of the support set and the echo matrix after the range migration correction; 2K is the confidence of the RIP constraint; like: Then it is determined that the sparsity estimate is less than the target estimate, and k a The value of is updated to the estimated sparsity at that time.

7. The method according to claim 1, characterized in that The step of restoring the corrected echo signal according to the optimal estimated value to obtain a reconstructed echo signal that meets the quality standard includes: S21, performing recovery initialization according to the input two-dimensional linear frequency modulation signal, the azimuth measurement matrix and the range measurement matrix of the two-dimensional linear frequency modulation signal, and the optimal estimation value; wherein the recovery initialization includes residual initialization, atomic set initialization and iteration number initialization; S22, selecting an atom most relevant to the current residual, and calculating the corresponding sparse matrix by the least square method; adding the selected atom to the atom set; S23, updating the next residual R according to the sparse matrix j+1 , and end an iterative calculation: R j+1 =Y-Φ j X j Wherein, Y is the echo matrix of the two-dimensional linear frequency modulation signal; Φ j is the atom most correlated with the residual at the jth iteration; X j is the sparse matrix calculated by the least squares method at the jth iteration; S24, determine whether the number of iterations is equal to the optimal sparsity, if so, end the iteration and output the reconstructed echo signal Among them, X0 is the sparse matrix at the last iteration; Otherwise, steps S21-S23 are repeated until the number of iterations is the same as the optimal sparsity.

8. A single-bit SAR signal recovery device based on sparsity judgment, characterized in that: include: A correction module is used to perform range migration correction on the single-bit quantized spaceborne SAR echo signal to be recovered to obtain a two-dimensional linear frequency modulation signal; the two-dimensional linear frequency modulation signal includes two dimensions, azimuth and range; A modeling module, used for performing spatial grid division on the target imaging area, and establishing a SAR imaging observation model in the grid points obtained by the division according to the two-dimensional linear frequency modulation signal; An updating module, used for establishing a sparsity optimization model according to the SAR imaging observation model, and iteratively updating the sparsity optimization model to obtain an optimal estimate of the scene sparsity; The recovery module is used to recover the corrected echo signal according to the optimal estimation value to obtain a reconstructed echo signal that meets the quality standard.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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