A High-Resolution UAV-Borne SAR Sparse Imaging Method with Band Synthesis

Abstract

The present application provides a high-resolution airborne SAR sparse imaging method for frequency band synthesis. The method includes: obtaining a first matrix of multiple groups of radar echo data; performing sparse sampling on the first matrix of each group of radar echo data to obtain a second matrix of the corresponding first matrix; calculating a third matrix of each group of radar echo data based on the above matrices; respectively performing range migration correction on the third matrix of each group of radar echo data to obtain first target data of each group of radar echo data; performing bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data; constructing an approximate observation operator based on the first matrix of each group of radar echo data, and imaging the second target data based on the approximate observation operator to obtain a pre-output image; performing multiple residual phase error corrections on the pre-output image to obtain an imaging image. The present application can improve the accuracy of the image in the reconstructed scene.

Description

Technical Field

[0001] This application belongs to the technical field of radar imaging, and particularly relates to a high-resolution airborne SAR sparse imaging method for frequency band synthesis. Background Technique

[0002] Synthetic Aperture Radar (SAR) is a microwave imaging radar with two-dimensional high resolution. It achieves high resolution in the range direction through pulse compression technology, and at the same time uses synthetic aperture technology to achieve high resolution in the azimuth direction. With the continuous growth of application requirements, the requirement for the resolution of SAR images is getting higher and higher. In order to obtain high-resolution SAR images, it is necessary to transmit radar signals with a larger bandwidth, and the receiving end needs to have a higher sampling rate of the Analog to Digital Converter (ADC). This undoubtedly poses a huge challenge to the instantaneous bandwidth of data signal processing.

[0003] In order to achieve high-resolution SAR imaging without increasing the system hardware requirements, Stepped Frequency Chirp Synthetic Aperture Radar (SFCSAR) came into being. SFCSAR can achieve high-resolution imaging of the observed scene without increasing the radar instantaneous bandwidth and the front-end ADC sampling rate. Its working principle is to transmit multiple narrow-band sub-pulse signals at the transmitting end, and at the receiving end, these narrow-band sub-pulse signals are synthesized into a large-bandwidth signal through digital signal processing technology, and then this signal is used to achieve high-resolution imaging of the scene. However, to obtain SAR images with the same high resolution, the amount of data to be processed does not decrease, which poses a huge challenge to the operation efficiency of the algorithm and the system memory. How to achieve high-resolution SAR imaging under the existing hardware conditions has become a research hotspot for many scholars. In recent years, with the wide application of new types of small and medium-sized platforms such as unmanned aerial vehicles, the research on airborne SAR systems has received extensive attention. Compared with spaceborne SAR systems, airborne SAR systems are more flexible. However, due to the fact that the carrier aircraft is easily affected by various uncertain factors in the air, the movement trajectory deviates from the ideal flight path. The motion errors caused by these non-ideal motions have a huge impact on the radar echo phase. If the errors are not compensated, it will seriously affect the SAR imaging quality.

[0004] Compared with traditional matched filtering algorithms, the synthetic aperture radar (SAR) imaging method based on compressive sensing theory can achieve sparse reconstruction of the observed scene while significantly reducing the amount of data required. However, for an SFCSAR system where the amount of sampled data in the range and azimuth directions is much larger than that of conventional SAR systems, using traditional exact observation operators will lead to huge computational workloads and memory occupancy problems, which have not been fundamentally solved. Based on this, it has been found that using approximate observation operators can alleviate the problems of computational workload and memory occupancy. However, the range of phase errors that can be corrected by the approximate observation operators is limited, which seriously affects the quality of the reconstructed image. Summary of the Invention

[0005] Embodiments of the present application provide a high-resolution unmanned aerial vehicle (UAV)-borne SAR sparse imaging method, apparatus, device, and medium for frequency band synthesis to solve the problem that the limited range of phase errors corrected by the approximate observation operator seriously affects the quality of the reconstructed image, resulting in low image accuracy in the existing reconstructed scene.

[0006] The present application is implemented through the following technical solutions:

[0007] In a first aspect, embodiments of the present application provide a high-resolution UAV-borne SAR sparse imaging method for frequency band synthesis, including:

[0008] Obtain a first matrix of multiple groups of radar echo data; wherein, the first matrix is a full-sampling matrix, and the frequencies of the radar echo data in each group are different.

[0009] Perform sparse sampling on the first matrix of each group of radar echo data to obtain a second matrix of the corresponding first matrix.

[0010] Based on the first matrix of each group of radar echo data and the second matrix of the corresponding first matrix, calculate a third matrix of each group of radar echo data.

[0011] Perform range migration correction on the third matrix of each group of radar echo data to obtain first target data of each group of radar echo data.

[0012] Perform bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data.

[0013] Based on the first matrix of each group of radar echo data, construct an approximate observation operator, and perform imaging on the second target data based on the approximate observation operator to obtain a pre-output image.

[0014] Perform multiple residual phase error corrections on the pre-output image to obtain an imaging image.

[0015] In combination with the first aspect, in some possible implementation manners, constructing an approximate observation operator based on the first matrix of each group of radar echo data includes:

[0016] Process the first matrix of each group of radar echo data based on the clutter locking method to obtain the Doppler center frequency of each group of radar echo data; process the first matrix of each group of radar echo data based on the PGA algorithm to obtain the first error of each group of radar echo data; wherein, the first error includes the azimuth phase error and the range migration error.

[0017] Construct an approximate observation operator based on the Doppler center frequency of each group of radar echo data and the first error of each group of radar echo data.

[0018] Combined with the first aspect, in some possible implementation manners, constructing an approximate observation operator based on the Doppler center frequency of each group of radar echo data and the first error of each group of radar echo data includes:

[0019] Use the first formula to construct an approximate observation operator;

[0020] The first formula is:

[0021]

[0022] Wherein, Γ -1 (X) represents the approximate observation operator of image X, represents the azimuth compression factor, φ1 represents the mean value of the azimuth phase error of each group of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, F r represents the range FFT transform, represents the inverse azimuth FFT transform, f c represents the center frequency of the chirp signal obtained after narrowband sub-pulse synthesis, f d represents the mean value of the Doppler center frequency of each group of radar echo data, f r represents the range frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, t represents the time, Φ X represents the residual phase error after focusing of image X.

[0023] Combined with the first aspect, in some possible implementation manners, performing bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data includes:

[0024] Perform bandwidth synthesis on the first target data of each group of radar echo data based on the frequency domain bandwidth synthesis algorithm to obtain second target data.

[0025] Combined with the first aspect, in some possible implementation manners, imaging the second target data based on the approximate observation operator to obtain a pre-output image includes:

[0026] Using the second formula, the second target data is imaged with an approximate observation operator to obtain a pre-output image;

[0027] The second formula is:

[0028] G = Γ(E) = Fa(Φ G ·Θ(E)) = Fa(Φ G ·Fr H (E·Hc)·Ha)

[0029] where G represents the pre-output image, E = Γ -1 (G), E represents the second target data, Θ(E) = F r H (E·H c )·H a , Fa represents the azimuth FFT transform, F H

[0030] r represents the range inverse FFT transform, Φ G represents the residual phase error after focusing the pre-output image G.

[0031] Combined with the first aspect, in some possible implementation manners, the pre-output image is corrected for the residual phase error multiple times to obtain an imaging image, including:

[0032] Using the third formula, the pre-output image is corrected for the residual phase error multiple times;

[0033] The third formula is:

[0034]

[0035] where G i+1 represents the imaging image, the image after the (i + 1)-th residual phase error correction, E represents the second target data, L represents the sparse sampling operator, G represents the pre-output image, Φ i+1 represents the residual phase error during the i-th residual phase error correction, ζ represents the regularization coefficient, represents the azimuth compression factor, φ1 represents the mean of the azimuth phase errors of each group of radar echo data, represents the range migration correction factor, φ0 represents the mean of the range migration errors of each group of radar echo data, F r represents the range FFT transform, f c represents the center frequency of the chirp signal obtained after narrowband sub-pulse synthesis, f d represents the mean of the Doppler center frequencies of each group of radar echo data, f rLet \(f_d\) denote the Doppler frequency, \(R\) denote the distance between the radar and the target, \(\lambda\) denote the wavelength, \(v\) denote the wave velocity, and \(t\) denote the time.

[0036] In combination with the first aspect, in some possible implementation manners, based on the first matrix of each group of radar echo data and the second matrix of the corresponding first matrix, calculating a third matrix of each group of radar echo data includes:

[0037] Calculating the sum of the first matrix of each group of radar echo data and its corresponding second matrix respectively to obtain the third matrix of each group of radar echo data.

[0038] In a second aspect, an embodiment of the present application provides a high-resolution unmanned aerial vehicle (UAV)-borne SAR sparse imaging device for band synthesis, including:

[0039] A data acquisition module, configured to acquire the first matrix of multiple groups of radar echo data; wherein, the first matrix is a full-sampling matrix, and the frequencies of the radar echo data of each group are different.

[0040] A first calculation module, configured to perform sparse sampling on the first matrix of each group of radar echo data to obtain the second matrix of the corresponding first matrix.

[0041] A second calculation module, configured to calculate a third matrix of each group of radar echo data based on the first matrix of each group of radar echo data and the second matrix of the corresponding first matrix.

[0042] A third calculation module, configured to perform range migration correction on the third matrix of each group of radar echo data respectively to obtain the first target data of each group of radar echo data.

[0043] A first synthesis module, configured to perform bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data.

[0044] A first correction module, configured to construct an approximate observation operator based on the first matrix of each group of radar echo data, and perform imaging on the second target data based on the approximate observation operator to obtain a pre-output image.

[0045] A result imaging module, configured to perform multiple residual phase error corrections on the pre-output image to obtain an imaging image.

[0046] In a third aspect, an embodiment of the present application provides a terminal device, including: a processor and a memory, where the memory is used to store a computer program, and when the processor executes the computer program, it implements the high-resolution UAV-borne SAR sparse imaging method for band synthesis as described in any item of the first aspect.

[0047] Fourthly, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the high-resolution airborne SAR sparse imaging method for band synthesis as described in any item of the first aspect.

[0048] It can be understood that the beneficial effects of the above second aspect to the fourth aspect can be referred to the relevant descriptions in the first aspect above, and will not be elaborated here.

[0049] The beneficial effects of the embodiments of the present application compared with the prior art are as follows:

[0050] Based on the first matrix of multiple groups of radar echo data, the present application first calculates an approximate observation operator. Subsequently, sparse sampling is performed on this first matrix to generate a second matrix. Then, multiple third matrices are generated using the first matrix and the second matrix, and range migration correction and bandwidth synthesis are performed on these third matrices to obtain second target data. After that, the second target data is imaged using the previously constructed approximate observation operator to obtain a pre-output image. Finally, the final imaging image is obtained by performing multiple residual phase error corrections on the pre-output image. The approximate observation operator of the present application is obtained based on the calculation of azimuth phase error, range migration error, and Doppler center frequency, which expands the range of phase errors that the approximate observation operator can correct. Therefore, when using this approximate observation operator to image the second target data, the accuracy of the pre-output image can be improved, thereby ultimately enhancing the quality and accuracy of the imaging.

[0051] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0053] Figure 1 It is a schematic flowchart of a high-resolution airborne SAR sparse imaging method for band synthesis provided by an embodiment of the present application;

[0054] Figure 2 It is a schematic structural diagram of a high-resolution airborne SAR sparse imaging device for band synthesis provided by an embodiment of the present application;

[0055] Figure 3 It is a schematic structural diagram of a terminal device provided by an embodiment of the present application. Detailed implementation manners

[0056] In the following description, specific details such as specific system architectures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0057] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0058] It should also be understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0059] As used in the specification of the present application and the appended claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if detecting [the described condition or event]" can be interpreted as meaning "once determined", "in response to determining", "once detecting [the described condition or event]", or "in response to detecting [the described condition or event]" depending on the context.

[0060] In addition, in the description of the specification of the present application and the appended claims, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0061] The reference to "one embodiment" or "some embodiments" etc. described in the specification of the present application means that a specific feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0062] The embodiment of the present application provides a high-resolution UAV-borne SAR sparse imaging method for band synthesis. Figure 1 It is a schematic flowchart of the high-resolution UAV-borne SAR sparse imaging method for band synthesis provided by an embodiment of the present application. Referring to Figure 1 , the detailed description of the high-resolution UAV-borne SAR sparse imaging method for band synthesis is as follows:

[0063] Step 101: Obtain the first matrix of multiple groups of radar echo data; where the first matrix is a full-sampling matrix, and the frequencies of the radar echo data in each group are different.

[0064] Step 102: Perform sparse sampling on the first matrix of each group of radar echo data to obtain the second matrix of the corresponding first matrix.

[0065] Step 103: Calculate the third matrix of each group of radar echo data based on the first matrix of each group of radar echo data and the second matrix of the corresponding first matrix.

[0066] Exemplarily, step 103 may include:

[0067] Calculate the sum of the first matrix of each group of radar echo data and its corresponding second matrix respectively to obtain the third matrix of each group of radar echo data.

[0068] Step 104: Perform range migration correction on the third matrix of each group of radar echo data respectively to obtain the first target data of each group of radar echo data.

[0069] Exemplarily, the specific process of performing range migration correction on the third matrix of each group of radar echo data includes:

[0070] For each third matrix, obtain the range migration error of the third matrix; based on the range migration error, construct a phase compensation factor; calculate the product of the radar echo data corresponding to the third matrix and the phase compensation factor to obtain the first target data corresponding to the third matrix.

[0071] Step 105: Perform bandwidth synthesis on the first target data of each group of radar echo data to obtain the second target data.

[0072] Exemplarily, the performing bandwidth synthesis on the first target data of each group of radar echo data to obtain the second target data includes:

[0073] Perform bandwidth synthesis on the first target data of each group of radar echo data based on the frequency-domain bandwidth synthesis algorithm (i.e., the algorithm of first range compression and then synthesis) to obtain the second target data.

[0074] Step 106: Based on the first matrix of each group of radar echo data, construct an approximate observation operator, and image the second target data based on the approximate observation operator to obtain a pre-output image.

[0075] Exemplarily, calculating the approximate observation operator based on the first matrix of each group of radar echo data may include:

[0076] Process the first matrix of each group of radar echo data based on the clutter locking method to obtain the Doppler center frequency of each group of radar echo data; process the first matrix of each group of radar echo data based on the PGA algorithm to obtain the first error of each group of radar echo data; wherein, the first error includes azimuth phase error and range migration error.

[0077] Construct an approximate observation operator based on the Doppler center frequency of each group of radar echo data and the first error of each group of radar echo data.

[0078] Exemplarily, the first error further includes a residual phase error, and the PGA algorithm can also estimate the phase error (residual phase error) of a single sub-pulse echo data, and this phase error can be used as the phase error of the synthetic bandwidth echo data (a group of radar echo data), because the difference between the phase error estimated for a single sub-pulse and the phase error estimated for the full bandwidth pulse is very small.

[0079] Exemplarily, since the approximate observation operator is calculated based on the azimuth phase error, range migration error, and Doppler center frequency, when using the approximate observation operator to reconstruct the second target data, the data accuracy of the pre-output image can be improved, and finally the quality and accuracy of imaging can be improved.

[0080] Exemplarily, constructing an approximate observation operator based on the Doppler center frequency of each group of radar echo data and the first error of each group of radar echo data may include:

[0081] Use the first formula to construct an approximate observation operator;

[0082] The first formula is:

[0083]

[0084] Wherein, Γ -1 (X) represents the approximate observation operator of image X, represents the azimuth compression factor, φ1 represents the mean value of the azimuth phase error of each group of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, F r represents the range FFT transform, Denotes the azimuth inverse FFT transform, f c Denotes the center frequency of the chirp signal obtained after narrowband sub - pulse synthesis, f d Denotes the mean of the Doppler center frequencies of each group of radar echo data, f r Denotes the range frequency, R denotes the distance between the radar and the target, λ denotes the wavelength, v denotes the wave speed, t denotes the time, Φ X Denotes the residual phase error after X - focusing of the image.

[0085] Exemplarily, imaging the second target data based on the approximate observation operator to obtain a pre - output image, including:

[0086] Using the second formula, imaging the second target data with the approximate observation operator to obtain a pre - output image;

[0087] The second formula is:

[0088] G = Γ(E)= Fa(Φ G ·Θ(E)) = Fa(Φ G ·Fr H (E·Hc)·Ha)

[0089] Where G denotes the pre - output image, E = Γ -1 (G), E denotes the second target data, Θ(E)= F r H (E·H c )·H a , Fa denotes the azimuth FFT transform, F H

[0090] r Denotes the range inverse FFT transform, Φ G Denotes the residual phase error after focusing the pre - output image G.

[0091] Exemplarily, compared with the traditional method of using an exact observation operator to correct the image, the approximate observation operator of the present application can greatly improve the correction efficiency and reduce the memory occupation of the computer when the algorithm runs. At the same time, in the present application, the calculation of the approximate observation operator is improved by combining the Doppler center frequency. This improvement expands the range of phase errors that the approximate observation operator can correct, thus ensuring the accuracy of scene reconstruction.

[0092] Step 107, performing multiple residual phase error corrections on the pre - output image to obtain an imaged image.

[0093] Exemplarily, step 107 may include:

[0094] Using the third formula to perform multiple residual phase error corrections on the pre - output image;

[0095] The third formula is as follows:

[0096]

[0097] Wherein, G i+1 represents the imaging image, the image after the (i + 1)-th residual phase error correction, E represents the second target data, L represents the sparse sampling operator, G represents the pre-output image, Φ i+1 represents the residual phase error during the i-th residual phase error correction, ζ represents the regularization coefficient, represents the azimuth compression factor, φ1 represents the mean value of the azimuth phase error of each group of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, F r represents the range FFT transform, f c represents the center frequency of the chirp signal obtained after narrowband sub-pulse synthesis, f d represents the mean value of the Doppler center frequency of each group of radar echo data, f r represents the range frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave velocity, and t represents the time.

[0098] Exemplarily, step 107 of the present application utilizes the idea of multiple loop corrections, which can further reduce the error of the image. Multiple loop corrections can effectively improve the reconstruction accuracy of the scene and enhance the calculation efficiency.

[0099] For the above high-resolution UAV-borne SAR sparse imaging method based on band synthesis, based on the first matrix of multiple groups of radar echo data, an approximate observation operator is first calculated. Subsequently, sparse sampling is performed on this first matrix to generate a second matrix. Then, multiple third matrices are generated using the first matrix and the second matrix, and range migration correction and bandwidth synthesis are performed on these third matrices to obtain the second target data. After that, the second target data is imaged using the previously constructed approximate observation operator to obtain a pre-output image. Finally, the final imaging image is obtained by performing multiple residual phase error corrections on the pre-output image. The approximate observation operator of the present application is obtained based on the calculation of the azimuth phase error, range migration error, and Doppler center frequency, and it expands the range of phase errors that the approximate observation operator can correct. Therefore, when using this approximate observation operator to image the second target data, the accuracy of the pre-output image can be improved, thereby ultimately enhancing the quality and accuracy of the imaging.

[0100] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0101] A high-resolution UAV-borne SAR sparse imaging method corresponding to the band synthesis described in the above embodiments. Figure 2 The structural block diagram of the high-resolution UAV-borne SAR sparse imaging device with band synthesis provided by the embodiments of the present application is shown. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown.

[0102] See Figure 2 , the high-resolution UAV-borne SAR sparse imaging device with band synthesis in the embodiments of the present application may include:

[0103] A data acquisition module 201, configured to acquire a first matrix of multiple groups of radar echo data; wherein, the first matrix is a full-sampling matrix, and the frequencies of the radar echo data in each group are different.

[0104] A first calculation module 202, configured to perform sparse sampling on the first matrix of each group of radar echo data to obtain a second matrix of the corresponding first matrix.

[0105] A second calculation module 203, configured to calculate a third matrix of each group of radar echo data based on the first matrix of each group of radar echo data and the second matrix of the corresponding first matrix.

[0106] A third calculation module 204, configured to perform range migration correction on the third matrix of each group of radar echo data respectively to obtain the first target data of each group of radar echo data.

[0107] A first synthesis module 205, configured to perform bandwidth synthesis on the first target data of each group of radar echo data to obtain the second target data.

[0108] A first correction module 206, configured to construct an approximate observation operator based on the first matrix of each group of radar echo data, and perform imaging on the second target data based on the approximate observation operator to obtain a pre-output image.

[0109] A result imaging module 207, configured to perform multiple residual phase error corrections on the pre-output image to obtain an imaging image.

[0110] Exemplarily, the first correction module 206 may be configured to:

[0111] Process the first matrix of each group of radar echo data based on the clutter locking method to obtain the Doppler center frequency of each group of radar echo data; process the first matrix of each group of radar echo data based on the PGA algorithm to obtain the first error of each group of radar echo data; wherein, the first error includes azimuth phase error and range migration error.

[0112] Construct an approximate observation operator based on the Doppler center frequency of each set of radar echo data and the first error of each set of radar echo data.

[0113] Exemplarily, the first correction module 206 can be used for:

[0114] Construct an approximate observation operator using the first formula;

[0115] The first formula is:

[0116]

[0117] where, Γ -1 (X) represents the approximate observation operator of image X, represents the azimuth compression factor, φ1 represents the mean value of the azimuth phase error of each set of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each set of radar echo data, F r represents the range FFT transform, represents the inverse azimuth FFT transform, f c represents the center frequency of the chirp signal obtained after narrowband sub-pulse synthesis, f d represents the mean value of the Doppler center frequency of each set of radar echo data, f r represents the range frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, t represents the time, Φ X represents the residual phase error after focusing of image X.

[0118] Exemplarily, the first synthesis module 205 can be used for:

[0119] Perform bandwidth synthesis on the first target data of each set of radar echo data based on the frequency domain bandwidth synthesis algorithm to obtain the second target data.

[0120] Exemplarily, the first correction module 206 can be used for:

[0121] Use the second formula and the approximate observation operator to image the second target data to obtain a pre-output image;

[0122] The second formula is:

[0123] G = Γ(E) = Fa(Φ G ·Θ(E)) = Fa(Φ G ·Fr H (E·Hc)·Ha)

[0124] where, G represents the pre-output image, E = Γ -1 (G), E represents the second target data, Θ(E) = Fr H (E·H c )·H a , where Fa represents the azimuth FFT transform, and Fr H represents the inverse FFT transform in the range direction, and Φ G represents the residual phase error after focusing the pre-output image G.

[0125] Exemplarily, the result imaging module 207 can be used for:

[0126] Adopting the third formula to perform multiple residual phase error corrections on the pre-output image;

[0127] The third formula is:

[0128]

[0129] where G i+1 represents the imaging image, the image after the (i + 1)-th residual phase error correction, E represents the second target data, L represents the sparse sampling operator, G represents the pre-output image, and Φ i+1 represents the residual phase error during the i-th residual phase error correction, ζ represents the regularization coefficient, represents the azimuth compression factor, φ1 represents the mean value of the azimuth phase error of each group of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, F r represents the FFT transform in the range direction, f c represents the center frequency of the chirp signal obtained after synthesizing narrowband sub-pulses, f d represents the mean value of the Doppler center frequency of each group of radar echo data, f r represents the range frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, and t represents the time.

[0130] Exemplarily, the second calculation module 203 can be used for:

[0131] Calculating the sum of the first matrix and the corresponding second matrix of each group of radar echo data respectively to obtain the third matrix of each group of radar echo data.

[0132] It should be noted that for the information interaction, execution process, etc. between the above-mentioned devices / units, since they are based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, please refer to the method embodiment part for details, and will not be elaborated here.

[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example for illustration. In practical applications, the above-mentioned functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment and will not be elaborated here.

[0134] The embodiment of this application also provides a terminal device. Refer to Figure 3 , the terminal device 300 may include: at least one processor 310 and a memory 320. The memory 320 is used to store a computer program 321. The processor 310 is used to call and run the computer program 321 stored in the memory 320 to implement the steps in any of the foregoing method embodiments, such as Figure 1 the steps 101 to 107 in the illustrated embodiment. Or, when the processor 310 executes the computer program, it implements the functions of each module / unit in the foregoing device embodiments, such as Figure 2 the functions of each module shown.

[0135] Exemplarily, the computer program 321 can be divided into one or more modules / units. One or more modules / units are stored in the memory 320 and executed by the processor 310 to complete this application. The one or more modules / units can be a series of computer program segments capable of completing specific functions, and these program segments are used to describe the execution process of the computer program in the terminal device 300.

[0136] Those skilled in the art can understand that Figure 3 this is only an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or combine some components, or different components, such as input / output devices, network access devices, buses, etc.

[0137] The processor 310 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc.

[0138] The memory 320 may be an internal storage unit of the terminal device, or may also be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. The memory 320 is used to store the computer program and other programs and data required by the terminal device. The memory 320 may also be used to temporarily store data that has been output or is to be output.

[0139] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience in representation, the buses in the drawings of this application are not limited to only one bus or one type of bus.

[0140] The high-resolution airborne SAR sparse imaging method with band synthesis provided by the embodiments of this application can be applied to terminal devices such as computers, wearable devices, vehicle-mounted devices, tablet computers, laptop computers, and netbooks. The embodiments of this application do not impose any restrictions on the specific types of terminal devices.

[0141] The embodiments of this application also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in each of the embodiments of the above-mentioned high-resolution airborne SAR sparse imaging method with band synthesis can be implemented.

[0142] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal is enabled to execute the steps in each of the embodiments of the high-resolution airborne SAR sparse imaging method capable of achieving the above-mentioned band synthesis when executed.

[0143] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps in each of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device capable of carrying the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc.

[0144] In the above embodiments, the descriptions of each embodiment have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0145] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0146] In the embodiments provided in the present application, it should be understood that the disclosed device / network device and method can be implemented in other ways. For example, the device / network device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0147] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] The above-described 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate 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 high-resolution airborne SAR sparse imaging method for band synthesis, characterized in that Including: Obtaining a first matrix of multiple groups of radar echo data; wherein, the first matrix is a full-sampling matrix, and the frequencies of the radar echo data in each group are different; Performing sparse sampling on the first matrix of each group of radar echo data to obtain a second matrix of the corresponding first matrix; Based on the first matrix of each group of radar echo data and the second matrix of the corresponding first matrix, calculating a third matrix of each group of radar echo data; Performing range migration correction on the third matrix of each group of radar echo data respectively to obtain the first target data of each group of radar echo data; Performing bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data; Based on the first matrix of each group of radar echo data, constructing an approximate observation operator, and imaging the second target data based on the approximate observation operator to obtain a pre-output image; Performing multiple residual phase error corrections on the pre-output image to obtain an imaging image.

2. The high-resolution UAV-borne SAR sparse imaging method for band synthesis according to claim 1, characterized in that, The constructing an approximate observation operator based on the first matrix of each group of radar echo data includes: Processing the first matrix of each group of radar echo data based on the clutter locking method to obtain the Doppler center frequency of each group of radar echo data; processing the first matrix of each group of radar echo data based on the PGA algorithm to obtain the first error of each group of radar echo data; wherein, the first error includes azimuth phase error and range migration error; Constructing an approximate observation operator based on the Doppler center frequency of each group of radar echo data and the first error of each group of radar echo data.

3. The high-resolution UAV-borne SAR sparse imaging method for band synthesis according to claim 2, wherein The constructing an approximate observation operator based on the Doppler center frequency of each group of radar echo data and the first error of each group of radar echo data includes: Using a first formula to construct the approximate observation operator; The first formula is: Γ -1 (X) = Θ -1 (Φ X F a H (X)) Among them, Γ -1 (X) represents the approximate observation operator of image X, represents the azimuth compression factor, φ1 represents the mean of the azimuth phase errors of each group of radar echo data, represents the range migration correction factor, φ0 represents the mean of the range migration errors of each group of radar echo data, F r represents the range FFT transform, F a H represents the inverse azimuth FFT transform, f c represents the center frequency of the chirp signal obtained after narrowband sub - pulse synthesis, f d represents the mean of the Doppler center frequencies of each group of radar echo data, f r represents the range frequency, R represents the distance from the radar to the target, λ represents the wavelength, v represents the wave speed, t represents the time, Φ X represents the residual phase error after focusing of image X.

4. The high-resolution unmanned aerial vehicle (UAV)-borne SAR sparse imaging method for band synthesis according to claim 1, characterized in that, The performing bandwidth synthesis on the first target data of each group of radar echo data to obtain second target data includes: Performing bandwidth synthesis on the first target data of each group of radar echo data based on the frequency-domain bandwidth synthesis algorithm to obtain the second target data.

5. The high-resolution UAV-borne SAR sparse imaging method for band synthesis according to claim 3, characterized in that, The imaging the second target data based on the approximate observation operator to obtain a pre-output image includes: Using a second formula to image the second target data with the approximate observation operator to obtain the pre-output image; The second formula is: G = Γ(E) = Fa(Φ G ·Θ(E)) = Fa(Φ G ·Fr H (E·Hc)·Ha) Among them, G represents the pre-output image, and E = Γ -1 (G), E represents the second target data, and Θ(E) = F r H (E · H c ) · H a , F a represents the azimuth FFT transform, and F r H represents the range inverse FFT transform, and Φ G represents the residual phase error after the pre-output image G is focused.

6. The high-resolution UAV-borne SAR sparse imaging method for band synthesis according to claim 1, characterized in that The performing multiple residual phase error corrections on the pre-output image to obtain an imaging image includes: Using a third formula to perform multiple residual phase error corrections on the pre-output image; The third formula is: Among them, G i+1 represents the imaging image, that is, the image after the (i + 1)-th residual phase error correction. E represents the second target data, L represents the sparse sampling operator, G represents the pre-output image, and Φ i+1 represents the residual phase error during the i-th residual phase error correction, ζ represents the regularization coefficient, represents the azimuth compression factor, φ1 represents the mean value of the azimuth phase error of each group of radar echo data, represents the range migration correction factor, φ0 represents the mean value of the range migration error of each group of radar echo data, and F r represents the range FFT transform, and f c represents the center frequency of the chirp signal obtained after narrow-band sub-pulse synthesis, and f d represents the mean value of the Doppler center frequency of each group of radar echo data, and f r represents the range frequency, R represents the distance between the radar and the target, λ represents the wavelength, v represents the wave speed, and t represents the time.

7. The high-resolution UAV-borne SAR sparse imaging method for band synthesis according to claim 1, characterized in that, The calculating a third matrix of each group of radar echo data based on the first matrix of each group of radar echo data and the second matrix of the corresponding first matrix includes: Respectively calculating the sum of the first matrix of each group of radar echo data and its corresponding second matrix to obtain the third matrix of each group of radar echo data.

8. A high-resolution airborne SAR sparse imaging device for frequency band synthesis, characterized in that, Including: A data acquisition module, configured to obtain a first matrix of multiple groups of radar echo data; wherein, the first matrix is a full-sampling matrix, and the frequencies of the radar echo data in each group are different; A first calculation module, configured to perform sparse sampling on the first matrix of each group of radar echo data to obtain a second matrix of the corresponding first matrix; A second calculation module, configured to calculate a third matrix of each set of radar echo data based on a first matrix of each set of radar echo data and a second matrix of the corresponding first matrix; A third calculation module, configured to perform range migration correction on the third matrix of each set of the radar echo data respectively to obtain first target data of each set of radar echo data; A first synthesis module, configured to perform bandwidth synthesis on the first target data of each set of the radar echo data to obtain second target data; A first correction module, configured to construct an approximate observation operator based on the first matrix of each set of radar echo data, and perform imaging on the second target data based on the approximate observation operator to obtain a pre-output image; A result imaging module, configured to perform multiple residual phase error corrections on the pre-output image to obtain an imaging image.

9. A terminal device, comprising: A processor and a memory, wherein the memory stores a computer program that can run on the processor, and is characterized in that when the processor executes the computer program, it implements the high-resolution airborne SAR sparse imaging method for band synthesis according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the high-resolution airborne SAR sparse imaging method for band synthesis according to any one of claims 1 to 7.

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