A fast time-domain imaging method for vehicle-borne SAR based on FFBP algorithm

Abstract

The present invention belongs to the technical field of vehicle-mounted synthetic aperture radar imaging, and discloses a vehicle-mounted SAR time domain fast imaging method based on the FFBP algorithm, including: distance compression of the original data of the vehicle-mounted SAR, and then motion compensation by the phase gradient autofocusing algorithm PGA and time domain imaging by the fast decomposition back projection algorithm FFBP; the FFBP algorithm back-projects the radar echo data to each pixel in the imaging area, and the pixel value is imaged by calculating the delay of the distance between the radar antenna and the image pixel of the radar echo. The present invention improves the imaging speed and accuracy of the system by dividing the full aperture into blocks and compensating for motion errors by the PGA algorithm; the FFBP algorithm uses the coherent accumulation of time domain signals in azimuth processing, which makes it easier to realize motion error compensation and obtain higher resolution SAR images by interpolation. The present invention has a faster computing speed and a wider range of applications.

Description

Technical Field

[0001] The invention belongs to the technical field of vehicle-mounted synthetic aperture radar imaging, and in particular relates to a vehicle-mounted SAR time domain fast imaging method based on FFBP algorithm. Background Art

[0002] At present, many provinces and cities across the country have issued corresponding no-fly regulations for drones, setting airports, chemical plants, military bases, transportation hubs and other sensitive areas as no-fly zones for "low, small and slow" aircraft. Therefore, it is inconvenient to use drones equipped with synthetic aperture radars to detect and image cities and other related areas. Vehicle-mounted synthetic aperture radar imaging can well adapt to various terrains and road conditions in cities.

[0003] Synthetic aperture radar has the ability to perform high-resolution imaging at all times, in all weather conditions, and over long distances, and is widely used in military and civilian fields. It can obtain high-resolution radar images similar to optical photography under extremely low visibility weather conditions. The radar uses the relative motion between the radar and the target to synthesize a smaller real antenna aperture into a larger equivalent antenna aperture using data processing methods.

[0004] Through the above analysis, the problems and defects of the existing technology are: drones are banned from flying in existing urban areas, and there are imaging difficulties when using drones equipped with synthetic aperture radar to detect cities and other related areas.

[0005] The difficulty of solving the above problems and defects is: how to solve the problem of drone flight ban in urban areas, and how to complete the detection problem in relevant areas of the city, and how to complete the detection task quickly and efficiently.

[0006] The significance of solving the above problems and defects is that the vehicle-mounted synthetic aperture radar used in this method can make up for the problem that drones cannot detect in urban areas, and the imaging range of vehicle-mounted synthetic aperture radar is larger, the distance is longer, and the efficiency is higher. It can quickly and efficiently meet the detection needs. Summary of the invention

[0007] In view of the problems existing in the prior art, the present invention provides a vehicle-mounted SAR time-domain rapid imaging method based on the FFBP algorithm, and more particularly relates to a vehicle-mounted synthetic aperture radar imaging method, system, medium, equipment and information processing terminal based on a fast factorized back-projection algorithm (Fast Factorized Back-Projection, FFBP) and phase gradient autofocus (Phase Gradient Autofocus, PGA), aiming to solve the problem of difficulty in urban detection imaging caused by the ban on drones in urban areas.

[0008] The present invention is implemented as follows: a vehicle-mounted SAR time domain fast imaging method based on the FFBP algorithm, the vehicle-mounted SAR time domain fast imaging method based on the FFBP algorithm includes: distance compression of the original data of the vehicle-mounted SAR, motion compensation by the phase gradient autofocusing algorithm PGA and time domain imaging by the fast decomposition back projection algorithm FFBP; the FFBP algorithm back-projects the radar echo data to each pixel in the imaging area, and the pixel value is imaged by calculating the delay of the radar echo between the radar antenna and the image pixel.

[0009] Furthermore, the vehicle-mounted SAR time domain rapid imaging method based on the FFBP algorithm comprises the following steps:

[0010] Step 1: The vehicle-mounted signal generator transmits a linear frequency modulation signal, and the distance width of the irradiated area is determined by the transmission bandwidth; after a period of time delay, the vehicle-mounted signal receiver receives the reflected signal; the received data is read, the distance compression processing is performed, and the radar system parameters in the received data are read;

[0011] Function: After obtaining the raw data, the radar system parameters can be used to obtain necessary parameters such as range and azimuth resolution, beam width, etc.

[0012] Step 2: The FFBP algorithm divides the total aperture into several sub-apertures according to the system parameters and plans the grid size in the polar coordinate domain;

[0013] Function: The divided aperture and grid can clearly define the number of fusions and the resolution of the polar coordinate domain.

[0014] Step 3, in the polar coordinate domain, each sub-aperture is imaged by calculating the delay of the radar echo at the distance between the radar antenna and the image pixel, and the pulses are coherently integrated using range interpolation;

[0015] Function: Through the first fusion, a low-resolution image of each sub-aperture is obtained, thus greatly reducing the imaging time of BP.

[0016] Step 4: In the polar coordinate domain, the sub-apertures are fused in pairs by angular domain interpolation to obtain a high-resolution image in the azimuth direction, and a total aperture image after all sub-apertures are fused;

[0017] Function: Through the continuous fusion of sub-aperture images, the azimuth resolution of the image is continuously improved.

[0018] Step 5, performing motion error compensation of the fused image using a PGA algorithm according to system parameters, wherein the PGA algorithm includes cyclic shift, window width selection, phase error estimation and iterative correction;

[0019] Function: Motion error compensation through PGA algorithm can reduce the phase error caused by two-dimensional interpolation and original data.

[0020] Step six, according to the parameter relationship between polar coordinates and time domain, the total aperture image is converted from the polar coordinate domain to the time domain to obtain the imaging result.

[0021] Function: To ensure that the signal can be accurately processed by the system and obtain a high-resolution total aperture image in the time domain

[0022] Furthermore, in the step 1, the vehicle-mounted signal generator transmits a linear frequency modulation signal, and the distance width of the irradiated area is determined by the transmission bandwidth; after a period of time delay, the vehicle-mounted signal receiver receives the reflected signal; the received data is read and distance compression processing is performed, and the radar system parameters in the received data are read, including:

[0023] (1) Read radar system parameters and range compression matched filter parameters;

[0024] The direction of the vehicle's travel route is the azimuth direction, and the direction perpendicular to the azimuth direction is the range direction;

[0025] The radar system parameters include operating carrier frequency fc, system bandwidth B, pulse repetition frequency PRF, pulse width Tp, system sampling rate Fs, radar slant angle θ, speed of light C, which are constants, and slant range R represents the distance from the vehicle to the center of the scene.

[0026] is the frequency domain matched filter function; Kr is the range modulation frequency.

[0027] (2) The range modulation frequency K is obtained according to the system bandwidth B and the pulse width Tp; in the time domain signal, the matched filter is designed to be the complex conjugate after time anti-convolution, with the complex conjugate of the quadratic phase; after the signal passes through the matched filter, the quadratic phase in the signal is offset.

[0028] is the baseband signal after distance compression.

[0029] Where τ is the fast time, t m is the slow time in the distance direction, k rc is the wave number center.

[0030] The FFBP algorithm in step 2 divides the total aperture into several sub-apertures according to the system parameters, and plans the grid size in the polar coordinate domain including:

[0031] (1) Plan the grid size. Set the size of the ground grid in the X direction to Xp and the size of the ground grid in the Y direction to Yp. According to the resolution indicators ρr and ρa, the grid pixel interval in the X direction is Δx, the grid pixel interval in the Y direction is Δy, and the number of ground imaging grid points is Nx×Ny:

[0032] X p =N x *Δx;

[0033] Y p =N y *Δy;

[0034] (2) Assuming the length of the synthetic aperture is La, the entire synthetic aperture is divided into K sub-apertures, then the length of each sub-aperture is l = L a / K; the number of initial sub-apertures is K (1) =2 G-1 , where G is an integer greater than 1, representing the total number of stages.

[0035] Further, in the step 3, in the polar coordinate domain, each sub-aperture is imaged by calculating the delay of the radar echo between the radar antenna and the image pixel, and the pulse is coherently accumulated using the distance interpolation, including:

[0036] (1) Using BP algorithm to image echo data;

[0037] In polar coordinates, for t m Antenna phase center to pixel point at time The instantaneous slope distance is:

[0038]

[0039] Read the sub-aperture echo data. For a certain section of echo data, call the imaging grid, calculate the grid angle size of each imaging grid in the polar coordinate domain, and calculate the distance between the radar and all grid points in each azimuth.

[0040] (2) Calculate the position of the echo corresponding to the current grid point in the grid, and calculate the echo value corresponding to the grid point by distance interpolation. For the reconstruction of a certain point, the pulses at each azimuth time are coherently accumulated as follows:

[0041]

[0042] Among them, t m is the slow time in the distance direction, k rc Accurate slant distance calculation for the wave number center is an important part of BP imaging and is related to the quality of the image.

[0043] Furthermore, in step 4, in the polar coordinate domain, the sub-apertures are fused in pairs by angular domain interpolation to obtain a high-resolution image in the azimuth direction, and a total aperture image after all sub-apertures are fused includes:

[0044] (1) Each sub-aperture image is fused two by two. Assuming that the number of sub-apertures in the g-th stage is K, a butterfly operation structure is established, and the azimuth coordinates of the sub-aperture center are:

[0045]

[0046] Each sub-image is reconstructed in a local polar coordinate system with the center of its sub-aperture as the origin. Therefore, the sub-images are located in different polar coordinate systems. Then, integration is performed along the k-th sub-aperture, and the reconstructed sub-image is expressed as:

[0047]

[0048] (2) The fusion of sub-images is completed through two-dimensional processing of angle domain interpolation and distance interpolation. The sub-images in the previous stage are coherently added to obtain the current sub-image, that is:

[0049]

[0050] Furthermore, the step 5 of performing motion error compensation of the fused image using the PGA algorithm according to the system parameters includes:

[0051] (1) For the PGA algorithm, the system calculates the slant range between the radar and the target and the distance the radar moves during the beam center crossing time; starting from the image domain, the strongest point on each distance unit is selected and circularly shifted to the center of the image.

[0052] (2) In each direction, the width is determined by incoherently summing all distance units, and the cyclically shifted image is windowed; after the cyclic shift and windowing processing, the azimuth inverse Fourier transform is performed to return to the range compressed phase history domain to estimate the phase error; the phase error is compared with the threshold value, and if it is greater than the threshold, the phase error is corrected; the image domain is returned to the image domain through Fourier transform to prepare for the next iteration until a certain convergence criterion is met, and the PGA processing is completed, and the error obtained by the PGA is used to compensate the phase of the data in the azimuth and range directions.

[0053] In step 6, according to the parameter relationship between polar coordinates and time domain, the total aperture image is converted from the polar coordinate domain to the time domain to obtain the imaging result, which includes:

[0054] The full spatial resolution image in the polar coordinates is converted into the plane rectangular coordinate system by two-dimensional interpolation.

[0055] Another object of the present invention is to provide a vehicle-mounted SAR imaging system for implementing the vehicle-mounted SAR time domain fast imaging method based on the FFBP algorithm, the vehicle-mounted SAR imaging system comprising:

[0056] The distance compression module is used to compress the raw data of the vehicle-mounted SAR;

[0057] Time domain imaging module, used for motion compensation through phase gradient autofocusing algorithm PGA and fast decomposition back projection algorithm FFBP for time domain imaging;

[0058] A back-projection module is used to back-project the radar echo data to each pixel in the imaging area through the FFBP algorithm;

[0059] The radar imaging module is used to calculate the delay of the radar echo between the radar antenna and the image pixel through the pixel value for imaging.

[0060] Another object of the present invention is to provide a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor performs the following steps:

[0061] The raw data of the vehicle-mounted SAR is compressed by distance, and then motion compensated by the phase gradient autofocusing algorithm PGA and imaged in the time domain by the fast decomposition back projection algorithm FFBP; the FFBP algorithm back-projects the radar echo data to each pixel in the imaging area, and the pixel value is imaged by calculating the delay of the radar echo between the radar antenna and the image pixel.

[0062] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps:

[0063] The raw data of the vehicle-mounted SAR is compressed by distance, and then motion compensated by the phase gradient autofocusing algorithm PGA and imaged in the time domain by the fast decomposition back projection algorithm FFBP; the FFBP algorithm back-projects the radar echo data to each pixel in the imaging area, and the pixel value is imaged by calculating the delay of the radar echo between the radar antenna and the image pixel.

[0064] Another object of the present invention is to provide an information data processing terminal, which is used to implement the vehicle-mounted SAR imaging system.

[0065] Combining all the above technical solutions, the advantages and positive effects of the present invention are as follows: the vehicle-mounted SAR time domain fast imaging method based on the FFBP algorithm provided by the present invention realizes the coherent accumulation of pulses through distance interpolation, realizes sub-aperture fusion through angle domain interpolation, and roughly compensates for motion errors through the PGA algorithm. The present invention improves the imaging speed and accuracy of the system by dividing the full aperture into blocks and compensating for motion errors with the PGA algorithm.

[0066] The FFBP algorithm in the present invention uses the coherent accumulation of time domain signals in azimuth processing, so it is easier to achieve motion error compensation and obtain a higher resolution SAR image through interpolation. Compared with other time domain algorithms, the present invention has a faster operation speed and a wider range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0068] Figure 1 It is a flow chart of a vehicle-mounted SAR time-domain rapid imaging method based on the FFBP algorithm provided in an embodiment of the present invention.

[0069] Figure 2 It is a schematic diagram of a vehicle-mounted SAR time-domain rapid imaging method based on the FFBP algorithm provided in an embodiment of the present invention.

[0070] Figure 3 is a structural block diagram of a vehicle-mounted SAR imaging system provided by an embodiment of the present invention;

[0071] In the figure: 1. Range compression module; 2. Time domain imaging module; 3. Back projection module; 4. Radar imaging module.

[0072] Figure 4 It is a working model diagram of the application and algorithm of vehicle-mounted synthetic aperture radar imaging based on the FFBP algorithm provided in an embodiment of the present invention.

[0073] Figure 5a and Figure 5b It is a schematic diagram of imaging results of three point targets in the polar coordinate domain and the time domain based on the FFBP algorithm vehicle-mounted synthetic aperture radar imaging provided by an embodiment of the present invention.

[0074] Figure 6a and Figure 6bIt is a schematic diagram of frequency domain imaging results of a point target in the polar coordinate domain and the time domain based on the FFBP algorithm vehicle-mounted synthetic aperture radar imaging provided by an embodiment of the present invention.

[0075] Figure 7a and Figure 7b It is a schematic diagram of imaging results of a point target imaged by a vehicle-mounted synthetic aperture radar based on the FFBP algorithm provided in an embodiment of the present invention before and after the PGA algorithm is used after adding errors.

[0076] Figure 8 and Fig. 9 This is an imaging diagram of measured data of the vehicle-mounted FFBP of the Hongqi Extra-Large Bridge on the Chan River in Xi'an, Shaanxi Province provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0078] In view of the problems existing in the prior art, the present invention provides a vehicle-mounted SAR time-domain fast imaging method based on the FFBP algorithm. The present invention is described in detail below in conjunction with the accompanying drawings.

[0079] like Figure 1 As shown, the vehicle-mounted SAR time domain rapid imaging method based on the FFBP algorithm provided in an embodiment of the present invention includes the following steps:

[0080] S101, performing distance compression on the raw data of the vehicle-mounted SAR;

[0081] S102, performing motion compensation by using a phase gradient autofocusing algorithm PGA and performing time domain imaging by using a fast decomposition back projection algorithm FFBP;

[0082] S103, back-projecting the radar echo data to each pixel in the imaging area through the FFBP algorithm;

[0083] S104, the pixel value is imaged by calculating the delay of the radar echo at the distance between the radar antenna and the image pixel.

[0084] The principle diagram of the vehicle-mounted SAR time domain fast imaging method based on the FFBP algorithm provided by the embodiment of the present invention is as follows: Figure 2 shown.

[0085] like Figure 3 As shown, the vehicle-mounted SAR imaging system provided by the embodiment of the present invention includes:

[0086] The distance compression module 1 is used to perform distance compression on the original data of the vehicle-mounted SAR;

[0087] A time domain imaging module 2, used for performing motion compensation by using a phase gradient autofocusing algorithm PGA and performing time domain imaging by using a fast decomposition back projection algorithm FFBP;

[0088] A back projection module 3, used for back-projecting the radar echo data to each pixel in the imaging area through the FFBP algorithm;

[0089] The radar imaging module 4 is used to perform imaging by calculating the delay of the distance between the radar antenna and the image pixel of the radar echo through the pixel value.

[0090] The technical solution of the present invention is further described below in conjunction with specific embodiments.

[0091] Example 1

[0092] In order to solve the problem of difficulty in urban detection imaging caused by the ban on drones in urban areas, the present invention provides a method and system based on vehicle-mounted synthetic aperture radar imaging, which improves the imaging speed and accuracy of the system by dividing the full aperture into blocks and compensating for motion errors using the PGA algorithm.

[0093] The present invention discloses a vehicle-mounted imaging method based on FFBP algorithm and PGA algorithm, comprising:

[0094] Step 1: The vehicle-mounted signal generator transmits a linear frequency modulation signal, and the distance width of the irradiated area is determined by the transmission bandwidth. After a period of delay, the vehicle-mounted signal receiver receives the reflected signal. Read the received data, perform distance compression processing, and read the radar system parameters in the received data.

[0095] Step 2: The FFBP algorithm divides the total aperture into several sub-apertures according to the system parameters and plans the grid size in the polar coordinate domain.

[0096] Step 3: Image each sub-aperture in the polar coordinate domain by calculating the time delay of the radar echo at the distance between the radar antenna and the image pixel. Coherently integrate the pulses using range interpolation.

[0097] Step 4: In the polar coordinate domain, the sub-apertures are fused in pairs through angular domain interpolation to obtain a high-resolution image in azimuth, and finally a total aperture image after all sub-apertures are fused.

[0098] Step 5: Perform motion error compensation of the fused image using the PGA algorithm according to the system parameters. The PGA algorithm includes four basic steps: cyclic shift, window width selection, phase error estimation, and iterative correction.

[0099] Step 6: According to the parameter relationship between polar coordinates and time domain, the total aperture image is converted from the polar coordinate domain to the time domain to obtain an imaging result.

[0100] As a further improvement of the present invention, the step 1 specifically comprises:

[0101] Step 101, reading radar system parameters and range compression matched filter parameters;

[0102] The direction of the vehicle's driving route is the azimuth direction, and the direction perpendicular to the azimuth direction is the range direction. The radar system parameters include the operating carrier frequency fc, the system bandwidth B, the pulse repetition frequency PRF, the pulse width Tp, the system sampling rate Fs, the radar slant angle θ, the speed of light C, which is a constant, and the slant range R represents the distance from the vehicle to the center of the scene.

[0103] is the frequency domain matched filter function. Kr is the range modulation frequency.

[0104] Step 102: Obtain the range modulation frequency K according to the system bandwidth B and the pulse width Tp. In the time domain signal, the matched filter is designed to be the complex conjugate after time anti-convolution, with the complex conjugate of the quadratic phase. After the signal passes through the matched filter, the quadratic phase in the signal is offset.

[0105] is the baseband signal after distance compression. Where τ is the fast time, t m is the slow time in the distance direction, k rc is the wave number center.

[0106] As a further improvement of the present invention, the step 2 specifically comprises:

[0107] Step 201, plan the grid size, set the size of the ground grid in the X direction to Xp, the size of the ground grid in the Y direction to Yp, according to the resolution indicators ρr and ρa, the grid pixel interval in the X direction is Δx, the grid pixel interval in the Y direction is Δy, and the number of ground imaging grid points is Nx×Ny:

[0108] X p =N x *Δx

[0109] Y p =N y *Δy

[0110] Step 202: Assume that the length of the synthetic aperture is La, divide the entire synthetic aperture into K sub-apertures, and the length of each sub-aperture is l=L a / K. The number of initial sub-apertures is K (1) =2 G-1, where G is an integer greater than 1, representing the total number of stages.

[0111] As a further improvement of the present invention, the step 3 specifically comprises:

[0112] Step 301, using BP algorithm to image the echo data;

[0113] In polar coordinates, for t m Antenna phase center to pixel point at time The instantaneous slope distance is:

[0114]

[0115] Read sub-aperture echo data. For a certain section of echo data, first call the imaging grid, calculate the grid angle size of each imaging grid in the polar coordinate domain, and calculate the distance between the radar and all grid points in each azimuth.

[0116] Step 302: Calculate the position of the echo corresponding to the current grid point in the grid, and calculate the echo value corresponding to the grid point by distance interpolation. For the reconstruction of a certain point, the pulses at each azimuth time are coherently accumulated as:

[0117]

[0118] Among them, t m is the slow time in the distance direction, k rc Accurate slant distance calculation for the wave number center is an important part of BP imaging and is related to the quality of the image.

[0119] For further improvement of the present invention, the step 4 specifically comprises:

[0120] Step 401: Each sub-aperture image is fused in pairs. In order to establish a butterfly operation structure, it is assumed that the number of sub-apertures in the g-th stage is K. Accordingly, the azimuth coordinates of the sub-aperture center are:

[0121]

[0122] Each sub-image is reconstructed in a local polar coordinate system with the center of its sub-aperture as the origin, so the sub-images are located in different polar coordinate systems. Then, by integrating along the k-th sub-aperture, the reconstructed sub-image can be expressed as:

[0123]

[0124] Step 402: The fusion of sub-images is completed by two-dimensional processing of angle domain interpolation and distance interpolation. The sub-images of the previous stage are coherently added to obtain the current sub-image, that is:

[0125]

[0126] For further improvement of the present invention, the step 5 specifically includes:

[0127] Step 501: Due to the movement of the target, the echo Doppler signal will have a phase error, causing image defocus. For the PGA algorithm, the system calculates the slant range between the radar and the target and the distance the radar moves during the beam center crossing time. Starting from the image domain, the strongest point on each distance unit is selected and circularly shifted to the center of the image.

[0128] Step 502, in each direction, all distance units are incoherently summed to determine the width, and the cyclic shift image is windowed to reduce background noise and interference from other targets. After cyclic shift and windowing, the azimuth inverse Fourier transform is returned to the range compression phase history domain to estimate the phase error. The phase error is compared with the threshold value. If it is greater than the threshold, the phase error is corrected and returned to the image domain through Fourier transform to prepare for the next iteration until a certain convergence criterion is met and the PGA processing is completed. The error obtained by PGA is then used to compensate the phase of the data in the azimuth and range directions.

[0129] For further improvement of the present invention, the step 6 specifically includes:

[0130] Step 601: Convert the full spatial resolution image in the polar coordinates into the plane rectangular coordinate system through two-dimensional interpolation.

[0131] Figure 4 It is a working model diagram of the application and algorithm of vehicle-mounted synthetic aperture radar imaging based on the FFBP algorithm provided in an embodiment of the present invention.

[0132] FIG5 is a schematic diagram of imaging results of three point targets in the polar coordinate domain and the time domain based on the FFBP algorithm vehicle-mounted synthetic aperture radar imaging provided by an embodiment of the present invention.

[0133] 6 is a schematic diagram of frequency domain imaging results of a point target in the polar coordinate domain and the time domain based on the FFBP algorithm vehicle-mounted synthetic aperture radar imaging provided by an embodiment of the present invention.

[0134] 7 is a schematic diagram of imaging results of a point target imaged by a vehicle-mounted synthetic aperture radar based on the FFBP algorithm provided in an embodiment of the present invention before and after the PGA algorithm is used after adding errors.

[0135] Figure 8 and Fig. 9 It is the measured data imaging of the vehicle-mounted FFBP on the Hongqi Bridge on the Chan River in Xi'an, Shaanxi Province.

[0136] Example 2

[0137] The present invention provides a method for vehicle-mounted synthetic aperture radar imaging based on the FFBP algorithm, which is implemented based on a vehicle-mounted SAR imaging system; wherein:

[0138] Step 1: The vehicle-mounted signal generator transmits a linear frequency modulation signal, and the distance width of the irradiated area is determined by the transmission bandwidth. After a period of delay, the vehicle-mounted signal receiver receives the reflected signal. Read the received data, perform distance compression processing, and read the radar system parameters in the received data.

[0139] Specifically include:

[0140] Step 101, reading radar system parameters and range compression matched filter parameters;

[0141] In this embodiment, the starting point of the working vehicle's marked driving route on the highway is used as the coordinate origin, the vehicle driving direction is the Y axis, the horizontal direction perpendicular to the driving direction is the X axis, and the vertical ground upward is the Z axis to establish a rectangular coordinate system. The radar system parameters include operating carrier frequency fc=0.5GHz, system bandwidth B=0.2GHz, pulse repetition frequency PRF=250Hz, pulse width Tp=4μs, system sampling rate Fs=250MHz, radar slant angle θ=0°, incident angle θi=45°, radar platform height from the ground H=1000m, radar antenna movement speed V=100m / s, echo data range sampling points Nr=512, echo data azimuth sampling points Na=1024. Radar scene center distance R0=1414.2m, reference slant distance Rref=1414.2m, range resolution ρr=0.6m, R0 direction resolution ρa=0.4m.

[0142] Step 102, obtain the range modulation frequency Kr according to the system bandwidth B and the pulse width Tp. In the time domain signal, the matched filter is designed to be the complex conjugate after time folding, with the complex conjugate of the quadratic phase. After the signal passes through the matched filter, the quadratic phase in the signal is offset. The echo signal is multiplied by the matched filter in the frequency domain to obtain the signal after range compression, and then the IFFT function provided by MATLAB is called to convert the frequency signal to the time domain signal. The time domain signal after range compression is

[0143] Step 2: The FFBP algorithm divides the total aperture into several sub-apertures according to the system parameters and plans the grid size in the polar coordinate domain.

[0144] Step 201, plan the grid size. According to the range resolution ρr=0.6m and the azimuth resolution ρa=0.4m at R0, set the number of grids in the range direction to Nx=32×4096, the number of grids in the azimuth direction to Ny=32×4096, and the number of grids in the Z-axis direction to Ny=32×4096.

[0145] Step 202: Divide the total aperture into 64 sub-apertures according to the total number of aperture points of 1024. The number of points of each sub-aperture is 17, and there may be overlaps between sub-apertures.

[0146] Step 3: Image each sub-aperture in the polar coordinate domain by calculating the time delay of the radar echo at the distance between the radar antenna and the image pixel. Coherently integrate the pulses using range interpolation.

[0147] Step 301, use BP algorithm to image the echo data, read the sub-aperture echo data, calculate the instantaneous slant distance from the radar to each distance unit point, calculate the grid angle size in each imaging grid in the polar coordinate domain, and calculate the distance between the radar and all grid points in each azimuth. Then call the imaging grid and interpolate it by the sinc function interpolation method.

[0148] Step 302: Calculate the position of the echo corresponding to the current grid point in the grid, and calculate the echo value corresponding to the grid point by distance interpolation. The instantaneous slant range of each distance unit is a 512×1 column vector, and the pulse at each azimuth moment is coherently accumulated by interpolating the 32-point sinc function. The reconstruction formula for the pixel point is:

[0149]

[0150] Finally, the imaging results of 8 sub-apertures are obtained.

[0151] Step 4: In the polar coordinate domain, the sub-apertures are fused in pairs through angular domain interpolation to obtain a high-resolution image in azimuth, and finally a total aperture image after all sub-apertures are fused.

[0152] Step 401, each sub-aperture image is fused in pairs, and the imaging data of the previous sub-aperture is read. For the first fusion of the sub-aperture, the number of azimuth points of the first fusion is obtained in the polar coordinate domain through the azimuth resolution. Set Rnum=544, Azicita=49, set the row vector of the interpolated distance coordinate to RNum×1, and set the column vector of the interpolated azimuth coordinate to 1×Azicita. The sub-aperture image after BP imaging is [512,17,ns1], where ns1=1,2...128; the image size of the first fusion of the sub-aperture is determined by the beam vector and the angle in the polar coordinate domain as [512,33,ns1 / 2].

[0153] Step 402, the fusion of sub-images is completed through two-dimensional processing of angular domain interpolation and distance interpolation. Call the sinc function, input the instantaneous slant distance of each distance direction as a row vector of 512×1, and obtain a 512×1 difference diffR between each element in the row and column and the initial slant distance. The difference diffR / Rstep (range resolution) is used to further determine the position of each point on the grid. Then, the 1×32 column vector that needs to be interpolated in the distance direction is obtained through the adjacent point interpolation method, and the same operation is performed in the azimuth direction of the polar coordinate domain to obtain a 32×1 row vector that needs to be interpolated in the azimuth direction. By the following formula:

[0154]

[0155] Coherent accumulation is performed on all distance units to obtain an image after the two sub-apertures are fused.

[0156] After the first fusion, 32 sub-images will be obtained. Following the same operation, after seven fusions, a 512×2028 complete total aperture image in the polar coordinate domain is obtained. Because there will be repeated parts of the sub-aperture during fusion, the number of image points is not exactly a power of 2.

[0157] Step 5: Perform motion error compensation of the fused image using the PGA algorithm according to the system parameters. The PGA algorithm includes four basic steps: cyclic shift, window width selection, phase error estimation, and iterative correction.

[0158] Step 501: For the PGA algorithm, the system calculates the slant range between the radar and the target and the distance the radar moves during the beam center crossing time. Starting from the image domain, the strongest point on each distance unit is selected and circularly shifted to the center of the image to obtain a 1×2028 estimated error column vector phas_pga.

[0159] Step 501: The error obtained by PGA is used to compensate the phase of the data in the azimuth and range directions.

[0160] Call the FFT function and IFFT function to perform FFT transformation on the row vector and column vector of the total aperture image data, multiply the phase of each column vector by exp(4jπ·Fr·(phas_pga·λ / 4π)) in the frequency domain, compensate the column vector and then perform IFFT processing to return the distance column vector to the imaging domain. After performing phase error compensation on the phase of the azimuth row vector, perform IFFT processing on the row vector to return the data to the imaging domain. Complete PGA processing.

[0161] Step 6: According to the parameter relationship between polar coordinates and time domain, the total aperture image is converted from the polar coordinate domain to the time domain to obtain an imaging result.

[0162] Step 601: Convert the full spatial resolution image in the polar coordinates to the plane rectangular coordinate system through two-dimensional interpolation. The range and azimuth resolutions in the polar coordinates are transformed into the range and azimuth resolutions in the time domain, and the instantaneous slant distance in the polar coordinates is transformed into the instantaneous slant distance in the time domain, and then the final image is obtained by still performing range interpolation and azimuth interpolation.

[0163] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When the use is implemented in whole or in part in the form of a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL) or wireless (e.g., infrared, wireless, microwave, etc.) mode) to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidState Disk (SSD)), etc.

[0164] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A vehicle-mounted SAR time domain rapid imaging method based on FFBP algorithm, characterized in that: The vehicle-mounted SAR time domain fast imaging method based on the FFBP algorithm performs distance compression on the original data of the vehicle-mounted SAR, and then uses the phase gradient autofocusing algorithm PGA to perform motion compensation and the fast decomposition back projection algorithm FFBP to perform time domain imaging; the FFBP algorithm performs imaging by reversely projecting the radar echo data to each pixel in the imaging area, and the pixel value is imaged by calculating the delay of the radar echo between the radar antenna and the image pixel; The vehicle-mounted SAR time domain rapid imaging method based on the FFBP algorithm comprises the following steps: Step 1: The vehicle-mounted signal generator transmits a linear frequency modulation signal, and the distance width of the irradiated area is determined by the transmission bandwidth; after a period of time delay, the vehicle-mounted signal receiver receives the reflected signal; reads the received data, performs distance compression processing, and reads the radar system parameters in the received data; Step 2: The FFBP algorithm divides the total aperture into several sub-apertures according to the system parameters and plans the grid size in the polar coordinate domain; Step 3, in the polar coordinate domain, each sub-aperture is imaged by calculating the delay of the radar echo at the distance between the radar antenna and the image pixel, and the pulses are coherently integrated using range interpolation; Step 4: In the polar coordinate domain, the sub-apertures are fused in pairs by angular domain interpolation to obtain a high-resolution image in the azimuth direction, and a total aperture image after all sub-apertures are fused; Step 5, performing motion error compensation of the fused image using a PGA algorithm according to system parameters, wherein the PGA algorithm includes cyclic shift, window width selection, phase error estimation and iterative correction; Step 6, according to the parameter relationship between polar coordinates and time domain, the total aperture image is converted from the polar coordinate domain to the time domain to obtain an imaging result; In the step 1, the vehicle-mounted signal generator transmits a linear frequency modulation signal, and the distance width of the irradiated area is determined by the transmission bandwidth; after a period of time delay, the vehicle-mounted signal receiver receives the reflected signal; the received data is read and distance compression processing is performed, and the radar system parameters in the received data are read, including: (1) Read radar system parameters and range compression matched filter parameters; The direction of the vehicle's travel route is the azimuth direction, and the direction perpendicular to the azimuth direction is the range direction; The radar system parameters include the operating carrier frequency fc, system bandwidth B, pulse repetition frequency PRF, pulse width Tp, system sampling rate Fs, radar slant angle θ, speed of light C is a constant, and slant range R represents the distance from the vehicle to the center of the scene; is the frequency domain matched filter function; f τ is the frequency in the distance direction, K r is the range modulation frequency; (2) The range modulation frequency Kr is obtained based on the system bandwidth B and the pulse width Tp; in the time domain signal, the matched filter is designed to be the complex conjugate after time anti-convolution, with the complex conjugate of the quadratic phase; after the signal passes through the matched filter, the quadratic phase in the signal is offset; is the baseband signal after distance compression; Where τ is the fast time, t m is the slow time in the distance direction, k rc is the wave number center; The FFBP algorithm in step 2 divides the total aperture into several sub-apertures according to the system parameters, and plans the grid size in the polar coordinate domain including: (1) Plan the grid size and set the size of the ground grid in the X direction to X p , the ground grid size in the Y direction is Y p According to the resolution index ρr and ρa, the grid pixel interval in the X direction is Δx, the grid pixel interval in the Y direction is Δy, and the number of ground imaging grid points is N x ×N y : X p =N x *Δx; Y p =N y *Δy; (2) The length of the synthetic aperture is L a , divide the entire synthetic aperture into K sub-apertures, then the length of each sub-aperture is l = L a / K; the number of initial sub-apertures is K (1) =2 G-1 , where G is an integer greater than 1, representing the total number of stages; In the step 3, in the polar coordinate domain, each sub-aperture is imaged by calculating the delay of the radar echo between the radar antenna and the image pixel, and the pulse is coherently accumulated using the distance interpolation, including: (1) Using BP algorithm to image echo data; In polar coordinates, for t m Antenna phase center to pixel point at time The instantaneous slope distance is: r is the length of the polar coordinate, θ is the angle of the polar coordinate, k is the kth subaperture, and v is the vehicle speed; Read sub-aperture echo data. For a certain section of echo data, call the imaging grid, calculate the grid angle size of each imaging grid in the polar coordinate domain, and calculate the distance between the radar and all grid points in each azimuth direction. (2) Calculate the position of the echo corresponding to the current grid point in the grid, and calculate the echo value corresponding to the grid point by distance interpolation; for the reconstruction of a certain point, coherently accumulate the pulses at each azimuth time as follows: Among them, R p represents the slope distance to point P, t m is the slow time in the distance direction, k rc The accurate slant distance calculation for the wave number center is an important part of BP imaging and is related to the quality of the image; In the step 4, in the polar coordinate domain, the sub-apertures are fused in pairs by angular domain interpolation to obtain a high-resolution image in azimuth, and a total aperture image after all sub-apertures are fused includes: (1) Each sub-aperture image is fused two by two. The number of sub-apertures in the g-th stage is K. A butterfly operation structure is established, and the azimuth coordinates of the sub-aperture center are: The subscript "k" represents the kth subaperture, and l represents the length of the subaperture; Each sub-image is reconstructed in a local polar coordinate system with the center of its sub-aperture as the origin. Therefore, the sub-images are located in different polar coordinate systems. Then, integration is performed along the k-th sub-aperture, and the reconstructed sub-image is expressed as: where x c represents the azimuth coordinate of the subaperture center; R(r, θ (k) ;t m ) indicates t m Antenna phase center to pixel point at time The instantaneous slope distance; (2) The fusion of sub-images is completed through two-dimensional processing of angle domain interpolation and distance interpolation. The sub-images in the previous stage are coherently added to obtain the current sub-image, that is:

2. The vehicle-mounted SAR time domain rapid imaging method based on the FFBP algorithm as claimed in claim 1, characterized in that: The step 5 of performing motion error compensation of the fused image using the PGA algorithm according to the system parameters includes: (1) For the PGA algorithm, the system calculates the slant range between the radar and the target and the distance the radar moves during the beam center crossing time. Starting from the image domain, the strongest point in each range unit is selected and circularly shifted to the center of the image. (2) In each direction, all range units are incoherently summed to determine the width, and the cyclic shifted image is windowed; after the cyclic shift and windowing, the azimuth inverse Fourier transform is performed to return to the range compressed phase history domain to estimate the phase error; the phase error is compared with the threshold value, and if it is greater than the threshold, the phase error is corrected; the Fourier transform is performed back to the image domain to prepare for the next iteration until a certain convergence criterion is met, and the PGA processing is completed, and the error obtained by the PGA is used to compensate the phase of the data in the azimuth and range directions; The step six converts the total aperture image from the polar coordinate domain to the time domain according to the parameter relationship between the polar coordinates and the time domain to obtain the imaging result, including: converting the full spatial resolution image under the polar coordinates into a plane rectangular coordinate system by two-dimensional interpolation.

3. A vehicle-mounted SAR imaging system implementing the vehicle-mounted SAR time domain fast imaging method based on the FFBP algorithm as described in any one of claims 1 to 2, characterized in that: The vehicle-mounted SAR imaging system comprises: The distance compression module is used to compress the raw data of the vehicle-mounted SAR; Time domain imaging module, used for motion compensation through phase gradient autofocusing algorithm PGA and fast decomposition back projection algorithm FFBP for time domain imaging; A back-projection module is used to back-project the radar echo data to each pixel in the imaging area through the FFBP algorithm; The radar imaging module is used to calculate the delay of the radar echo between the radar antenna and the image pixel through the pixel value for imaging.

4. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the vehicle-mounted SAR time domain rapid imaging method based on the FFBP algorithm as described in any one of claims 1 to 2.

5. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the vehicle-mounted SAR time-domain rapid imaging method based on the FFBP algorithm as claimed in any one of claims 1 to 2.

6. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the vehicle-mounted SAR imaging system as claimed in claim 3.

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