Non-uniform sampling SAR imaging method combined with sub-channel BP image reconstruction

Abstract

The invention discloses a non-uniform sampling SAR imaging method combined with sub-channel BP image reconstruction, and the method comprises the steps: carrying out the channel division of non-uniform sampling SAR echo data, and obtaining a plurality of uniform sub-channel data; performing BP imaging processing on the data of each sub-channel to obtain a BP image of each sub-channel; based on the relationship between the BP grid and the Doppler frequency, obtaining the position of a ghost target in the BP image and the energy accumulation of the ghost target, and establishing a multi-channel BP image model in combination with the Doppler expression of the BP image of each sub-channel; and on the basis of energy accumulation at the ghost target position, weighted accumulation is carried out on the BP image of each sub-channel by using a multi-channel BP image model, joint reconstruction of the multi-channel BP image is realized, and a reconstructed image is obtained. According to the method, the ghost energy introduced into the fundamental bandwidth internal and external signal components is effectively suppressed, a stable reconstruction model is provided, iteration and optimization are not needed, and the reconstruction processing speed and precision are reliable.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar imaging, and particularly relates to a non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction. Background Art

[0002] Synthetic Aperture Radar (SAR) is a well-performing radar remote sensing imaging technology for the Earth. With the emergence of requirements for high resolution and wide-swath imaging in the current development of SAR, the acquisition method of SAR signals is no longer limited to uniform sampling, and the processing technology of non-uniform sampling data has gradually become a research hotspot in fields such as distributed SAR and interleaved SAR. For example, distributed SAR uses a variable PRF (pulse repetition frequency) scheme to prevent the periodic occurrence of repeated samples and improve the reconstruction process. Interleaved SAR adopts a variable PRF scheme to eliminate the blind range in the case of high-resolution SAR and achieve the purpose of increasing the width. In addition, the signals received by a multi-channel SAR system can also be regarded as periodically non-uniform sampling signals. However, non-uniform sampling will bring some problems, namely irregular spectral grating lobes and the failure of the matched filter.

[0003] Traditional standard frequency-domain imaging algorithms such as the CS (Chirp Scaling) algorithm, PF (Polar Format) algorithm, and typical Omega-K (wavenumber domain imaging) algorithm cannot obtain good imaging performance in a non-uniform SAR system due to the failure of the matched filter. Using standard time-domain imaging algorithms, such as the BP (BackProjection) algorithm, can effectively avoid the problem of the failure of the matched filter, but still cannot suppress the influence of irregular grating lobes in the azimuth direction.

[0004] The filter bank algorithm FGA (Filter group algorithm), also known as the multi-channel spectral reconstruction algorithm, is applicable to the spectral reconstruction of periodically dynamic PRI (Pulse Repetition Interval) time-series signals. The filter bank algorithm can suppress irregular spectral grating lobes by reconstructing non-uniform periodically dynamic PRI time-series into multiple sub-channel data. However, the filter bank algorithm can only eliminate the grating lobes within the baseband, and the grating lobes outside the baseband will still be retained. For similar methods for suppressing grating lobes outside the baseband, some sparse adjustment-based schemes have been extensively studied. However, these methods generally have problems such as high computational complexity and poor real-time performance. With the increase in scene complexity, their convergence accuracy and convergence speed will be greatly affected. Summary of the Invention

[0005] To solve the above problems existing in the prior art, the present invention provides a non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction. The technical problems to be solved by the present invention are achieved through the following technical solutions:

[0006] In a first aspect, the present invention proposes a non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction, including:

[0007] Dividing the non-uniformly sampled SAR echo data into channels to obtain a plurality of uniform sub-channel data;

[0008] Performing BP imaging processing on each sub-channel data to correspondingly obtain the BP image of each sub-channel;

[0009] Based on the relationship between the BP grid and the Doppler frequency, obtaining the position of the virtual target in the BP image and the energy accumulation at the position of the virtual target, and combining the Doppler expressions of the BP images of each sub-channel to establish a multi-channel BP image model;

[0010] Based on the energy accumulation at the position of the virtual target, using the multi-channel BP image model to perform weighted accumulation on the BP images of each sub-channel to realize the joint reconstruction of the multi-channel BP images and obtain the reconstructed image.

[0011] In a second aspect, the present invention proposes a non-uniform sampling SAR imaging device for joint sub-channel BP image reconstruction to implement the method proposed in the first aspect of the present invention. The device includes:

[0012] A channel division module for dividing the non-uniformly sampled SAR echo data into channels to obtain a plurality of uniform sub-channel data;

[0013] An imaging module for performing BP imaging processing on each sub-channel data to correspondingly obtain the BP image of each sub-channel;

[0014] A model establishment module for obtaining the position of the virtual target in the BP image and the energy accumulation at the position of the virtual target based on the relationship between the BP grid and the Doppler frequency, and combining the Doppler expressions of the BP images of each sub-channel to establish a multi-channel BP image model;

[0015] An image reconstruction module for performing weighted accumulation on the BP images of each sub-channel using the multi-channel BP image model based on the energy accumulation at the position of the virtual target to realize the joint reconstruction of the multi-channel BP images and obtain the reconstructed image.

[0016] In a third aspect, the present invention proposes an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus;

[0017] A memory for storing a computer program;

[0018] A processor for executing the program stored in the memory to implement the method provided in the first aspect of the present invention.

[0019] In a fourth aspect, the present invention proposes a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method provided in the first aspect of the present invention is implemented.

[0020] Advantages of the present invention:

[0021] The non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction proposed by the present invention first divides the non-uniform sampled SAR echo data into channels to obtain multiple uniform sub-channel data; then performs BP imaging processing on each sub-channel data to correspondingly obtain the BP image of each sub-channel; then based on the relationship between the BP grid and the Doppler frequency, obtains the virtual target positions in the BP image of each sub-channel and the energy accumulation at the virtual target positions, and combines the Doppler expressions of the BP images of each sub-channel to establish a multi-channel BP image model; finally, based on the energy accumulation at the virtual target positions, uses the multi-channel BP image model to perform weighted accumulation on the BP images of each sub-channel to realize the joint reconstruction of the multi-channel BP images and obtain the reconstructed image. This method is based on the non-uniform sampling model, cleverly explores the mapping relationship between the BP image and the Doppler spectrum, and establishes a multi-channel BP image model. Based on this model, the reconstruction process of the joint sub-channel BP image is realized. Compared with the existing algorithms, the method proposed by the present invention can effectively suppress the virtual image energy introduced by the signal components inside and outside the fundamental frequency bandwidth, can completely solve the problem of irregular spectral grating lobes existing in non-uniform sampling, and this algorithm has a stable reconstruction model, without iteration and optimization, and the speed and accuracy of the reconstruction process are reliable, and it can adapt to the dynamic PRI timing signal imaging processing process of flexible trajectory SAR.

[0022] The following will further elaborate on the present invention in detail with reference to the accompanying drawings and embodiments. Description of the Drawings

[0023] Figure 1 is a schematic flowchart of a non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction provided by an embodiment of the present invention;

[0024] Figure 2 is another schematic flowchart of a non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction provided by an embodiment of the present invention;

[0025] Figure 3 is a schematic diagram of the time offset corresponding to the periodic dynamic PRI timing in the simulation experiment;

[0026] Figure 4 is the Doppler domain result corresponding to the original signal in the simulation experiment;

[0027] Figure 5 is the BP imaging result corresponding to the original signal in the simulation experiment;

[0028] Figure 6 is the range-Doppler result corresponding to the original signal in the simulation experiment;

[0029] Figure 7 is the Doppler domain result after being processed by the FGA reconstruction algorithm in the simulation experiment;

[0030] Figure 8 is the BP imaging result after being processed by the FGA reconstruction algorithm in the simulation experiment;

[0031] Figure 9 is the range-Doppler result after being processed by the FGA reconstruction algorithm in the simulation experiment;

[0032] Figure 10 is the imaging result using the joint sub-channel BP image reconstruction algorithm of the present invention;

[0033] Figure 11 is the range-Doppler result using the joint sub-channel BP image reconstruction algorithm of the present invention;

[0034] Figure 12 is the two-dimensional Contour result of the selected point target A in the processing result using the joint sub-channel BP image reconstruction algorithm of the present invention;

[0035] Figure 13 is the two-dimensional Contour result of the selected point target B in the processing result using the joint sub-channel BP image reconstruction algorithm of the present invention;

[0036] Figure 14 is the two-dimensional Contour result of the selected point target C in the processing result using the joint sub-channel BP image reconstruction algorithm of the present invention;

[0037] Figure 15 is the Doppler result of the dynamic PRI timing SAR signal;

[0038] Figure 16 is the result of direct BP processing;

[0039] Figure 17 After sub-channel division processing, a certain sub-channel signal is taken for analysis to obtain its Doppler result;

[0040] Figure 18 is the BP processing result of the sub-channel signal;

[0041] Figure 19 The clean SAR image obtained by jointly reconstructing multiple sub-channel BP images using the algorithm proposed in the present invention;

[0042] Figure 20 It is the structural block diagram of the non-uniform sampling SAR imaging device for jointly reconstructing sub-channel BP images provided by the embodiments of the present invention. Detailed implementation manners

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Before introducing the solution of the present invention, the BP algorithm will be briefly introduced first.

[0045] First of all, the core expression of the BP algorithm is:

[0046]

[0047] In the formula, i(x i , y i ) represents the pixel value of the image generated by the BP algorithm at the position (x i , y i ), xc1() represents the data after pulse compression, t r , t n respectively represent the fast time and the non-uniform azimuth slow time, c represents the speed of light, B represents the bandwidth, R() represents the slant range, and λ represents the wavelength;

[0048] Let

[0049]

[0050] Performing NUDFT (Non-uniform Discrete Fourier Transform) processing on the above formula, the expression at ω = 0 can be obtained as:

[0051]

[0052] Obtain i(x i , y i ) = Yc1(t r , jω)| ω＝0 .

[0053] It can be seen that the BP projection process is essentially a kind of Fourier transform, and further it can be obtained that:

[0054]

[0055] However, within the baseband (-π / T, π / T) corresponding to Yc1(tr, jω), there are M grating lobes. In other words, except at ω = 0, there is also energy accumulation at which corresponds to the ghost image (also known as ghosting) in BP imaging.

[0056] In the BP image, the energy accumulation corresponding to the grid at (x i + k1Δx bp , y i ) can be expressed as:

[0057]

[0058] In the formula, i(x i + k1Δx bp , y i ) represents the pixel value at the position (x i + k1Δx bp , y i ) in the BP image, x i and y i respectively represent the coordinates of the target point in the azimuth direction and the range direction, δ() represents the impulse function, R() represents the slant range, Yc1() represents the spectral component of the sub-channel signal, q is the index of the ghost, representing the q-th ghost, Δx bp and Δy bp are respectively the azimuth and range grid sizes of the BP image.

[0059] It should be noted that the BP algorithm is the most ideal state of the PFA algorithm, and essentially both are to construct the point spread function (PSF) scenario in the Doppler domain. Combining with the above formula (5), a direct relationship between the BP image and the Doppler frequency f bp can be established, which creates conditions for subsequent image domain reconstruction processing.

[0060] When 2v·qΔx bp / λR B = 1 / T M , that is:

[0061]

[0062] The ghost target will appear at (xi + l·R B ·λ / (2·v·T M), near (yi). Combining equations (5) and (6), it can be seen that when the signal is received in a dynamic PRI timing sequence, ghost targets in the BP imaging result are inevitable, and the position where grating lobes appear can be further calculated according to the above equation (6).

[0063] Based on this, the first aspect of the present invention proposes a non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction.

[0064] Please refer to the joint reference Figure 1 and Figure 2 , Figure 1 is a schematic flow chart of a non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction provided by an embodiment of the present invention. Figure 2 is another schematic flow chart of a non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction provided by an embodiment of the present invention. The method mainly includes the following steps:

[0065] Step 1: Divide the non-uniformly sampled SAR echo data into channels to obtain multiple uniform sub-channel data.

[0066] It can be understood that the data being acquired processed by the present invention is the data after the SAR echo has undergone range pulse compression processing. Therefore, before performing channel division, it is necessary to perform pulse compression processing on the non-uniformly sampled SAR echo data.

[0067] Specifically, in the SAR system, after the echo has undergone pulse compression processing, the pulse-compressed data obtained is expressed as:

[0068]

[0069] In the formula, xc1() represents the pulse-compressed data, t r , t n respectively represent fast time and non-uniform azimuth slow time, W a () represents the azimuth window, sinc() represents the sinc function, c represents the speed of light, B represents the bandwidth, R() represents the slant range, and λ represents the wavelength.

[0070] It should be noted that the non-uniformly sampled SAR echo data referred to in this embodiment is a periodic dynamic PRI timing sequence signal, and the pulse-compressed data can be further expressed in the following form:

[0071]

[0072] In the formula, xc() represents another form of the pulse-compressed data, M represents the total number of channels, m represents the channel number, t represents time, k represents the kth period, T represents the standard sampling period, and r m represents the percentage measurement value of the time offset.

[0073] Observing the above equation, the arrangement of the data xc1(t r ,t n ) can be decomposed into M sub-channel signals, and the m-th sub-channel signal after decomposition is expressed as:

[0074] xc1,m(tr,ts) = xc(tr,ts - (rm + m)T) (9);

[0075] In the formula, xc 1,m (t r ,t s ) represents the data of the m-th sub-channel, t s represents the azimuth sampling time obtained after the sub-channel data is partitioned, and the corresponding new sampling period and sampling frequency are MT and prfd = 1 / (MT) respectively, and m represents the channel number.

[0076] It should be noted that although each sub-channel signal is uniformly sampled, its corresponding prf d often cannot accommodate unambiguous scene information, that is, there is scene Doppler ambiguity, which is intuitively expressed as:

[0077]

[0078] In the formula, B inst is expressed as IDBW and is determined by the observed scene. Under the condition of broadside view, a scene (X min , X max ) that is symmetric with the scene center as the coordinate origin is established, and X min and X max are the leftmost and rightmost coordinates of the scene respectively, and they satisfy the condition X max + X min = 0.

[0079] Therefore, the BP grid size layout should meet the following conditions: (Q·Δxbp) ≥ (2Xmax), where Q is the total number of grid points. Then, the above formula (10) can be further expressed as:

[0080]

[0081] Referring to formula (6), the position offset corresponding to the potential ghost target can also be obtained as:

[0082]

[0083] That is to say, the virtual image target will appear at (x i + qΔx bp , y i ). When x i meets the following conditions:

[0084]

[0085] Expand the absolute value formula and discuss it in two cases: One case is when x i < 0, that is when, it can be obtained that:

[0086]

[0087] It can be found that the ghost image will appear in the valid scene, and the position is:

[0088]

[0089] The other case is when x i ≥ 0, that is it can be obtained that:

[0090]

[0091] Similarly, it is noted that the virtual image also exists in the SAR scene, and the position is:

[0092]

[0093] Thus, when there is scene Doppler ambiguity in the sub-channel signal, the energy of the virtual target will inevitably appear in the sub-channel BP image.

[0094] Step 2: Perform BP imaging processing on the data of each sub-channel to obtain the BP image corresponding to each sub-channel.

[0095] Specifically, in order to solve the problem of virtual images in the BP image introduced by the periodic dynamic PRI timing, an equivalent multi-channel BP image model needs to be established.

[0096] Optionally, in order to improve the algorithm efficiency, this embodiment introduces the GCBP (Ground Cartesian BackProjection) algorithm for imaging processing. After performing BP processing on the data of the m-th sub-channel, the vector expression of its corresponding image is:

[0097] Im(x,y) = [i(x1,y1), …, i(xq,yq), …i(x Q ,y Q )] (18);

[0098] In the formula, Im(x,y) represents the BP image of the m-th sub-channel, and i(xq,yq) represents the pixel value at the position (x q ,y q ), 1 ≤ q ≤ Q, and Q is the total number of BP grid points set.

[0099] Step 3: Based on the relationship between the BP grid and the Doppler frequency, obtain the position of the ghost target in the BP image and the energy accumulation at the position of the ghost target, and establish a multi-channel BP image model in combination with the Doppler expression of the BP image of each sub-channel.

[0100] Specifically, the known formula (5) shows the corresponding relationship between the BP grid point position and the Doppler frequency, that is Therefore, the position corresponding to the ghost target can be further obtained as:

[0101]

[0102] In the formula, x represents the abscissa of the target, k1 represents the pixel index, Δx bp represents the azimuth pixel interval, v represents the speed of the radar platform, λ represents the wavelength, R B represents the closest distance from the scene center to the radar trajectory, f bp represents the Doppler frequency component in the BP image, l represents the index of Doppler ambiguity, prf d represents the sampling frequency of the sub-channel, and Z represents the total number of Doppler ambiguities.

[0103] This means that the ghost targets in other ambiguity regions l·prf d will be introduced into the real scene, resulting in limited BP image quality. In other words, the energy accumulation of the ghost target located at the BP grid (x + k1Δx bp , y i ) is composed of multiple ambiguous energies. The expression of the energy accumulation can be referred to the above formula (5).

[0104] Correspondingly, the Doppler expression corresponding to the BP image of the m-th sub-channel is obtained as:

[0105]

[0106] In the formula, Yc1(f bp + l·prf d ) represents the spectral component of the sub-channel signal at the frequency f bp + l·prf d , f bp represents the Doppler frequency component in the BP image, l represents the index of Doppler ambiguity, prf d represents the sampling frequency of the sub-channel, r m represents the time offset of the sub-channel, r1 represents the time offset of the first sub-channel, and T represents the standard sampling period.

[0107] Therefore, the multi-channel model of the BP image is further obtained as:

[0108]

[0109] Among them, Γ(f bp ) represents the signal matrix in the multi-channel model, and A(f bp + l·prf d ) is the steering vector in the BP image domain, which can be expressed as:

[0110]

[0111] It should be noted that for the BP image grids at different positions, the ghost targets accumulated at their positions are composed of the superposition of different blur components. Therefore, when suppressing the ghost targets at different positions, the image domain steering vectors to be constructed need to be calculated separately.

[0112] Step 4: Based on the energy accumulation at the position of the virtual image target, use the multi-channel BP image model to perform weighted accumulation on the BP images of each sub-channel to achieve the joint reconstruction of the multi-channel BP images and obtain the reconstructed image.

[0113] Specifically, according to formula (21), the energy contribution of the blurred area where the real image scene energy is 0 (l = 0) to the BP image. Therefore, in the process of performing weighted accumulation on the BP images of each sub-channel using the multi-channel BP image model based on the energy accumulation at the position of the virtual image target, the weighted distribution is carried out in the following manner:

[0114] Set non-zero weights for the regions with zero energy in the BP images of each sub-channel, and at the same time set the weights of the regions with non-zero energy in the BP images of each sub-channel to zero, so as to eliminate the virtual image targets in the BP images of each sub-channel.

[0115] Through the above processing, the reconstructed clean BP image is obtained, which is expressed as:

[0116]

[0117] In the formula, I c (f bp ) represents the spectral component of the ghost-free BP image after reconstruction at frequency f bp , f bp represents the Doppler frequency component in the BP image, W(f bp ) represents the weight matrix in the reconstruction process, Γ(fbp) represents the signal matrix in the multi-channel model, A() represents the steering vector in the BP image domain, the symbol “+” represents the pseudo-inverse symbol, and H l represents the identity matrix.

[0118] So far, the non-uniform sampling SAR imaging of the joint sub-channel BP image reconstruction has been realized.

[0119] The non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction provided by the present invention is based on a non-uniform sampling model, cleverly excavates the mapping relationship between the BP image and the Doppler spectrum, and establishes a multi-channel BP image model. Based on this model, the reconstruction process of the joint sub-channel BP image is realized. Compared with the existing algorithms, the method proposed by the present invention can effectively suppress the ghost energy introduced by the signal components inside and outside the fundamental frequency bandwidth, completely solve the problem of irregular spectral aliasing existing in non-uniform sampling, and the algorithm has a stable reconstruction model, without iteration and optimization, and the speed and accuracy of the reconstruction process are reliable, and it can adapt to the dynamic PRI time-series signal imaging process of flexible-trajectory SAR.

[0120] The effectiveness of the present invention can be further illustrated by the following simulation and measured data imaging.

[0121] This simulation experiment is a simulation experiment for an airborne SAR receiving dynamic PRI time series. First, Table 1 gives the specific parameters of the simulation experiment.

[0122] Table 1 Simulation parameters of airborne SAR with dynamic PRI time series

[0123] Carrier Frequency (GHz) 11 Glancing Angle (degrees) 0 Velocity (m / s) 180 PRF (Hz) 610～1070 Bandwidth (MHz) 120 Slant Range (km) 20 Sampling Rate (MHz) 144 Number of Cycles M 5

[0124] In this simulation, the SAR signal is received in a periodic non-uniform sampling manner in the azimuth direction, and the period is M = 5, and the signal accumulation time is 5 s. In the simulation scenario, 15 target points are set, and the corresponding scenario size is (range × azimuth: 400 m × 1600 m).

[0125] Figure 3 The time offset r corresponding to the periodic dynamic PRI time series is given m , which determines the appearance position and energy of the ghost. According to the parameters in Table 1, the total Doppler bandwidth of the signal can be obtained as 1386 Hz (IDBW is 792 Hz, and the accumulation bandwidth is 546 Hz), exceeding the limit of one PRF. This means that the signal components outside the fundamental frequency bandwidth will enter the signal spectrum in the form of aliasing.

[0126] Figures 4 - 6 The processing results of the original signal are given, where Figure 4 is the Doppler domain result of the signal, and it can be clearly observed that there are spectral aliasing and grating lobes, Figure 5 and 6 represent the BP imaging result and the corresponding range-Doppler result respectively, and it can be clearly observed from Figures 4 - 6 the existence of ghost (grating lobe) energy.

[0127] Figures 7 - 9 The processing results using the existing FGA reconstruction algorithm are given, whereFigure 7 The result of the signal Doppler domain obtained by FGA processing shows that the grating lobes caused by the signal Doppler components within the bandwidth are well suppressed, while the energy of the grating lobes caused by the signal Doppler components outside the bandwidth still exists. Figure 8 and 9 respectively give the BP imaging result after processing by the FGA reconstruction algorithm, the range-Doppler result after FGA processing. It can be seen from this that the energy of the ghost images (grating lobes) is well suppressed, but there are still some ghost image targets remaining.

[0128] Figure 10 and Figure 11 are the imaging results obtained by using the joint sub-channel BP image reconstruction algorithm of the present invention and the corresponding range-Doppler results. It can be seen that the energy of the grating lobes or the ghost image targets caused by the signal Doppler components inside and outside the bandwidth are completely suppressed, and the imaging performance is further improved.

[0129] Figures 12 - 14 And Table 2 also gives the two-dimensional Contour results and performance evaluation results of the selected point targets in the processing results of the reconstruction algorithm of the present invention. Among them, Figure 12 is the result of point A, Figure 13 is the result of point B, Figure 14 is the result of point C.

[0130] Table 2 Performance evaluation table of point target response

[0131]

[0132] Furthermore, in this measured data, the airborne SAR operates in the spotlight mode, and the key parameters are shown in Table 3.

[0133] Table 3 Measured system parameters of airborne SAR

[0134] Parameter Value Parameter Value Carrier Frequency (GHz) 8.9 Glancing Angle (degrees) 0 Center Slant Range (m) 7650 Signal Sampling Rate (MHz) 250 Platform Velocity (m / s) 100 Signal Bandwidth (MHz) 150 Pulse Repetition Frequency (Hz) 2000 M 3

[0135] In order to make the two-dimensional imaging resolution better match the real scene, the BP grid size is set to 0.6 m in the range direction and 0.5 m in the azimuth direction. This data is received in a periodic dynamic PRI timing sequence with M = 3, and obvious grating lobe phenomena will inevitably appear in its corresponding Doppler spectrum, as shown in Figure 15 shown.

[0136] Figure 16 gives the direct BP processing result, and the ghost image energy region is marked with a red square. Since the ghost image region is introduced by the signal components in other ambiguous regions, its corresponding slant range history does not match well with the real scene, and there is an obvious defocus phenomenon.

[0137] After the sub-channel division processing, a certain sub-channel signal is taken for analysis, and its Doppler result is as Figure 17 shown, from which the existence of Doppler aliasing can be clearly observed. Correspondingly, Figure 18 gives the BP processing result of the sub-channel signal. Compared with Figure 16 , Figure 18 the ghost target in Figure 19 has stronger energy, which is consistent with the above analysis. Finally, the algorithm proposed in the present invention is used to perform joint reconstruction processing on multiple sub-channel BP images, and a clean SAR image without ghost targets is obtained, as

[0138] shown.

[0139] Thus, the effectiveness of the present invention is verified.

[0139] Based on the same inventive concept, the second aspect of the present invention provides a non-uniform sampling SAR imaging device for joint sub-channel BP image reconstruction to implement the method provided in the first aspect of the present invention. Please refer to Figure 20 , Figure 20 which is the structural block diagram of the non-uniform sampling SAR imaging device for joint sub-channel BP image reconstruction provided by the embodiment of the present invention. The device includes:

[0140] A channel division module for dividing the non-uniformly sampled SAR echo data into channels to obtain a plurality of uniform sub-channel data;

[0141] An imaging module for performing BP imaging processing on each sub-channel data to correspondingly obtain the BP image of each sub-channel;

[0142] A model establishment module for obtaining the position of the virtual image target and the energy accumulation at the position of the virtual image target in the BP image based on the relationship between the BP grid and the Doppler frequency, and establishing a multi-channel BP image model in combination with the Doppler expression of the BP image of each sub-channel;

[0143] An image reconstruction module for weighted accumulation of the BP images of each sub-channel by using the multi-channel BP image model based on the energy accumulation at the position of the virtual image target to realize the joint reconstruction of the multi-channel BP images and obtain the reconstructed image.

[0144] Specifically, for the specific process of each module in the device to implement its method steps, reference can be made to the description of the above method embodiment.

[0145] The third aspect of the present invention further provides an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory communicate with each other through the communication bus.

[0146] The memory is used to store a computer program;

[0147] When the processor is used to execute the program stored in the memory, the method steps provided in the first aspect of the present invention are implemented.

[0148] The method provided by the present invention can be applied to an electronic device. Specifically, the electronic device can be: a desktop computer, a portable computer, a smart mobile terminal, a server, etc. There is no limitation here. Any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0149] The fourth aspect of the present invention also provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium, and when the computer program is executed, it can implement the method provided in the first aspect of the present invention.

[0150] For the device / electronic device / storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiments.

[0151] It should be noted that the device, electronic device, and storage medium of the embodiments of the present invention are respectively the device, electronic device, and storage medium applying the above-mentioned non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction. Then all embodiments of the above-mentioned non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction are applicable to the device, electronic device, and storage medium, and can achieve the same or similar beneficial effects.

[0152] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a device (equipment), or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Here, they are all collectively referred to as "modules" or "systems". Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes. The computer program is stored / distributed in a suitable medium, provided together with other hardware or as a part of the hardware, and can also be in other distribution forms, such as through the Internet or other wired or wireless telecommunication systems.

[0153] The above content is a further detailed description of the present invention in combination with specific preferred implementation manners. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can be made, and all should be regarded as belonging to the protection scope of the present invention.

Claims

1. A non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction, characterized in that: include: Divide the non-uniformly sampled SAR echo data into channels to obtain multiple uniform sub-channel data; Performing BP imaging processing on the sub-channel data to obtain a BP image of each sub-channel; Based on the relationship between the BP grid and the Doppler frequency, the position of the phantom target in the BP image and the energy accumulation at the phantom target position are obtained, and the multi-channel BP image model is established in combination with the Doppler expression of the BP image of each sub-channel; Based on the energy accumulation at the position of the phantom target, the multi-channel BP image model is used to perform weighted accumulation on the BP image of each sub-channel to achieve joint reconstruction of the multi-channel BP image and obtain a reconstructed image.

2. The non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction according to claim 1, characterized in that: The non-uniformly sampled SAR echo data is divided into channels to obtain multiple uniform sub-channel data, including: Pulse compression processing is performed on the non-uniformly sampled SAR echo data to obtain pulse compressed data; the pulse compressed data is expressed as: In the formula, xc1() represents the data after pulse pressure, t r ,t n W represents fast time and non-uniform azimuth slow time respectively. a () represents the azimuth window, sinc() represents the sinc function, c represents the speed of light, B represents the bandwidth, R() represents the slant range, and λ represents the wavelength; Based on the characteristics of the periodic dynamic PRI timing signal, that is, the periodic non-uniform sampling signal, the pulse compressed data is expressed in another form as follows: In the formula, xc() represents another form of data after pulse compression, M represents the total number of channels, m represents the channel number, t represents time, k represents the kth cycle, T represents the standard sampling cycle, and r represents the sampling period. m expressed as a percentage measurement of the time offset; The pulse compressed data is decomposed into several sub-channel signals, and the mth sub-channel signal is expressed as: xc1,m(tr,ts)=xc(tr,ts-(rm+m)T); In the formula, xc 1,m () represents the data of the mth subchannel, t s Indicates the azimuth sampling time obtained after the sub-channel data is divided.

3. The non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction according to claim 2, characterized in that: The vector expression of the BP image of each subchannel is: Im(x,y)=[i(x1,y1),…,i(xq,yq),…i(x Q ,y Q )]; In the formula, Im(x,y) represents the BP image of the mth subchannel, i(xq,yq) represents the image at position (x q ,y q ), 1≤q≤Q, Q is the total number of points in the BP grid.

4. The non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction according to claim 3 is characterized in that: The expression of the ghost target position in the BP image is: In the formula, x represents the target horizontal coordinate, k1 represents the pixel index, Δx bp represents the azimuth pixel interval, v represents the speed of the radar platform, λ represents the wavelength, and R B represents the shortest distance from the scene center to the radar track, f bp represents the Doppler frequency component in the BP image, l represents the index of Doppler ambiguity, prf d represents the sampling frequency of the subchannel, and Z represents the total number of Doppler ambiguities; The expression of energy accumulation at the position of the virtual target in the BP image is: In the formula, i(x i +k1Δx bp ,y i ) indicates that the position in the BP image is (x i +k1Δx bp ,y i ), x i and i They represent the coordinates of the target point in azimuth and range, δ() represents the impulse function, R() represents the slant range, Yc1() represents the spectral component of the subchannel signal, q is the index of the ghost, indicating the qth ghost, Δx bp and Δy bp are the orientation and distance grid sizes of the BP image, respectively.

5. The non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction according to claim 4, characterized in that: The multi-channel BP image model is expressed as: Where, Γ(fbp) represents the signal matrix in the multi-channel model, A() represents the steering vector in the BP image domain; I m (f bp ) represents the Doppler expression of the m-th subchannel BP image, and M represents the total number of channels; In the formula, Yc1(f bp +l·prf d ) indicates that the subchannel signal is at frequency f bp +l·prf d The spectral component at bp represents the Doppler frequency component in the BP image, l represents the index of Doppler ambiguity, prf d Indicates the sampling frequency of the subchannel, r m Represents the time offset of the sub-channel, r1 represents the time offset of the first sub-channel, and T represents the standard sampling period.

6. The non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction according to claim 1, characterized in that: In the process of weighted accumulation of the BP image of each sub-channel by using the multi-channel BP image model based on the energy accumulation at the position of the phantom target, weighted allocation is performed in the following manner: A non-zero weight is set for the area with zero energy accumulation in the BP image of each sub-channel, and the weight of the area with non-zero energy accumulation in the BP image of each sub-channel is set to zero, thereby eliminating the ghost targets in the BP images of each sub-channel.

7. The non-uniform sampling SAR imaging method for joint sub-channel BP image reconstruction according to claim 1, characterized in that: The reconstructed image is represented as: In the formula, I c (f bp ) represents the reconstructed ghost-free BP image at frequency f bp The spectral component at bp represents the Doppler frequency component in the BP image, W(f bp ) represents the weight matrix in the reconstruction process, Γ(fbp) represents the signal matrix in the multi-channel model, A() represents the steering vector in the BP image domain, the symbol "+" represents the pseudo-inverse symbol, H l Represents the identity matrix.

8. A non-uniform sampling SAR imaging device for joint sub-channel BP image reconstruction, used to implement the method according to any one of claims 1 to 7, characterized in that: The device comprises: A channel division module is used to divide the non-uniformly sampled SAR echo data into channels to obtain multiple uniform sub-channel data; An imaging module, used for performing BP imaging processing on the data of each sub-channel to obtain a BP image of each sub-channel; A model building module is used to obtain the phantom target position in the BP image and the energy accumulation at the phantom target position based on the relationship between the BP grid and the Doppler frequency, and to build a multi-channel BP image model in combination with the Doppler expression of the BP image of each sub-channel; The image reconstruction module is used to perform weighted accumulation of the BP image of each sub-channel based on the energy accumulation at the phantom target position by using the multi-channel BP image model to achieve joint reconstruction of the multi-channel BP image and obtain a reconstructed image.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, configured to execute a program stored in a memory to implement the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 7 can be implemented.

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