Three-dimensional imaging method, device and equipment for ground-based synthetic aperture radar

Abstract

The invention belongs to the technical field of radar imaging, and relates to a three-dimensional imaging method, device and equipment of a ground-based synthetic aperture radar. The method comprises the following steps: acquiring echoes of a ground-based synthetic aperture radar, and projecting the echoes to the same imaging area grid in each receiving channel to obtain a two-dimensional radar image of each receiving channel; constructing a pitching direction array flow pattern; pixels of all the two-dimensional radar images in the same distance-azimuth unit form a pixel vector; taking the pitch direction array flow pattern pixel vector as input to obtain a plurality of target pitch direction estimation results; projecting each target pitching direction estimation result to a space rectangular coordinate system through coordinate transformation to obtain a corresponding height estimation result, and further obtaining a three-dimensional point cloud result of the imaging scene; and clustering the three-dimensional point cloud result, and then carrying out noise reduction processing to obtain a three-dimensional point cloud image. According to the invention, super-resolution three-dimensional imaging can be realized, and the imaging quality is improved.

Description

Technical Field

[0001] The present application relates to the field of radar imaging technology, and in particular to a three-dimensional imaging method, device and equipment for ground-based synthetic aperture radar. Background Art

[0002] Ground-Based Synthetic Aperture Radar (GB-SAR) is a SAR system that has garnered significant attention in recent years. Its short revisit period and high monitoring accuracy allow it to continuously monitor a specific scene for extended periods, providing real-time feedback. Consequently, GB-SAR is widely used for building deformation monitoring and landslide early warning.

[0003] During imaging processing, the GB-SAR system's antenna moves along a linear trajectory, limiting the imaging field of view of linear SAR. To achieve wide-area imaging in azimuth, researchers mounted the radar antenna at the end of a vertical arm with controllable rotation speed. The antenna's uniform circular motion along the arm forms a synthetic aperture, resulting in a system called Ground-Based Arc Synthetic Aperture Radar (GB-ArcSAR). The GB-ArcSAR system primarily consists of a rotor arm and a radar system mounted at its end. Unlike linear SAR, the GB-ArcSAR's rotational angular velocity and scanning angle can be customized, offering greater flexibility. The azimuth direction of the GB-ArcSAR is the direction of the arm's rotation, with the range dimension perpendicular to the azimuth direction. In azimuth, the antenna's uniform circular motion simulates an equivalent long-aperture antenna to achieve high azimuth resolution. Due to its unique antenna motion trajectory, the GB-ArcSAR antenna can scan a 360° scene with a single uniform rotation around its center of rotation, enabling wide-area imaging and significantly improving imaging efficiency. Therefore, GB-ArcSAR not only retains the advantage of short revisit period of linear GB-SAR, but also has the capability of wide-area imaging, and can be applied to fields such as landslide deformation measurement and building deformation measurement.

[0004] In the existing technology, research on GB-SAR, especially GB-ArcSAR, mainly focuses on two-dimensional imaging. Three-dimensional imaging methods include: multi-phase center sidelobe suppression method based on three-dimensional point spread function, frequency domain three-dimensional imaging algorithm, BP three-dimensional imaging algorithm with the help of terrain surface, and ArcSAR three-dimensional imaging algorithm based on spherical wave decomposition.

[0005] However, current three-dimensional imaging methods still have the following key problems: in GB-SAR, due to the limitations of observation geometry and signal processing methods, the array aperture length in the altitude direction is limited, resulting in the altitude resolution usually being lower than the range and azimuth resolutions, affecting the imaging quality of the scene or target and making it difficult to achieve super-resolution three-dimensional reconstruction. Summary of the Invention

[0006] Based on this, it is necessary to provide a three-dimensional imaging method, device and equipment for ground-based synthetic aperture radar to address the above technical problems, which can achieve super-resolution three-dimensional imaging and improve imaging quality.

[0007] A three-dimensional imaging method for ground-based synthetic aperture radar, comprising: Acquire the echo of the ground-based synthetic aperture radar, project the echo onto the same imaging area grid in each receiving channel, and obtain a two-dimensional radar image for each receiving channel; The pitch angle estimation range is obtained and the pitch array manifold is constructed. All pixels in the same range-azimuth unit of the 2D radar image are combined into a pixel vector. The pitch array manifold and the pixel vector are used as inputs to the OMP algorithm to obtain the pitch estimation results of multiple targets corresponding to different range-azimuth units. Project the pitch estimation result of each target into the spatial rectangular coordinate system through coordinate transformation to obtain the corresponding height estimation result; based on all the height estimation results, obtain the three-dimensional point cloud result of the imaging scene; A density-based spatial clustering algorithm is used to cluster the 3D point cloud results, and then noise reduction is performed to obtain a 3D point cloud image.

[0008] In one embodiment, an echo of a ground-based synthetic aperture radar is acquired, and the echo is projected onto the same imaging area grid in each receiving channel to obtain a two-dimensional radar image for each receiving channel, including: Acquire the echo of ground-based synthetic aperture radar; For a receiving channel, a back-projection algorithm is used to project the echo onto the imaging area grid to obtain a two-dimensional radar image of the receiving channel; All receiving channels are traversed and the back-projection algorithm is used to project the echoes onto the same imaging area grid to obtain a two-dimensional radar image for each receiving channel.

[0009] In one embodiment, an elevation angle estimation range is obtained and an elevation array manifold is constructed. All pixels in the same range-azimuth unit of the two-dimensional radar image are combined into a pixel vector. The elevation array manifold and the pixel vector are respectively used as inputs of an OMP algorithm to obtain multiple target elevation estimation results corresponding to different range-azimuth units, including: Obtaining a pitch angle estimation range, and evenly dividing the pitch angle estimation range into a plurality of grids, so as to construct a pitch array flow pattern according to the divided plurality of grids; All pixels of the two-dimensional radar image in the same range-azimuth unit are combined into a pixel vector; Traverse all range-azimuth units and obtain the pixel vector of each range-azimuth unit; The pitch array flow pattern and a pixel vector are used as inputs of the OMP algorithm to obtain the target pitch estimation result of the corresponding range-azimuth unit. Traverse all pixel vectors to obtain the target pitch estimation result corresponding to each range-azimuth unit.

[0010] In one embodiment, obtaining a pitch angle estimation range and evenly dividing the pitch angle estimation range into a plurality of grids, and constructing a pitch array flow pattern according to the divided plurality of grids, includes:

[0011] Where, is the pitch array flow pattern, is the pitch angle, is the center carrier frequency, is the array element spacing, is the speed of light, is the number of array elements uniformly distributed vertically, for The transpose of .

[0012] In one embodiment, the OMP algorithm includes: Input pitch array flow pattern and the pixel vector of the same range-azimuth unit , let the residual , select the angle set , number of iterations , to complete the initialization; Choose an angle that best correlates with the current residual The corresponding steering vector ; The selected Add angle collection ; Solve the sparse representation of the target by least squares ; Update residual , ; If the convergence condition is not reached, increase the number of iterations and let , repeatedly select the steering vector and update the residual until the convergence condition is reached. In one embodiment, each target pitch estimation result is projected into a spatial rectangular coordinate system through coordinate transformation to obtain a corresponding height estimation result; based on all the height estimation results, a three-dimensional point cloud result of the imaging scene is obtained, including: Using the coordinate transformation formula, the pitch estimation result of each target is projected into the spatial rectangular coordinate system through coordinate transformation to obtain the corresponding height estimation result; Based on all the height estimation results, the three-dimensional point cloud result of the imaging scene is obtained.

[0013] In one embodiment, the coordinate transformation formula is:

[0014] Where, is the true height of the target, is the distance to the target, The direction of the target, is the target's elevation angle.

[0015] In one embodiment, a density-based spatial clustering algorithm is used to cluster the three-dimensional point cloud results, and then noise reduction processing is performed to obtain a three-dimensional point cloud image, including: Determine the parameters of density-based spatial clustering algorithm based on the echo of ground-based synthetic aperture radar; A density-based spatial clustering algorithm is used to cluster the 3D point cloud results, and the 3D point cloud results are divided into different clusters. For the three-dimensional point cloud results divided into different clusters, the outlier noise points marked as noise clusters are removed to obtain a three-dimensional point cloud image.

[0016] A three-dimensional imaging device for a ground-based synthetic aperture radar, comprising: The first module is used to obtain the echo of the ground-based synthetic aperture radar, project the echo to the same imaging area grid in each receiving channel, and obtain a two-dimensional radar image for each receiving channel; The second module is used to obtain the pitch angle estimation range and construct the pitch array flow pattern. All pixels in the same range-azimuth unit of the 2D radar image are combined into a pixel vector. The pitch array flow pattern and the pixel vector are used as inputs to the OMP algorithm to obtain the pitch angle estimation results for multiple targets corresponding to different range-azimuth units. The third module is used to project the pitch estimation results of each target into a spatial rectangular coordinate system through coordinate transformation to obtain the corresponding height estimation results; based on all the height estimation results, a three-dimensional point cloud result of the imaging scene is obtained; The fourth module is used to cluster the three-dimensional point cloud results using a density-based spatial clustering algorithm, and then perform noise reduction processing to obtain a three-dimensional point cloud image.

[0017] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0018] The above-mentioned three-dimensional imaging method, device and equipment of the ground-based synthetic aperture radar solves the problem of low altitude resolution and inability to achieve super-resolution three-dimensional imaging of existing imaging methods by using the orthogonal matching pursuit (OMP) sparse reconstruction method based on the compressed sensing framework, effectively realizes the altitude super-resolution imaging of the ground-based synthetic aperture radar, and then uses the DBSCAN clustering method to further reduce noise and improve imaging quality, laying the foundation for subsequent target recognition and parameter estimation steps. It provides a more reliable solution for the application of ground-based synthetic aperture radar in scenarios such as anti-ship warfare and unmanned system perception, and has great application value in actual engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 1 is a flow chart of a three-dimensional imaging method of a ground-based synthetic aperture radar in one embodiment; Figure 2 is an optical image of an experimental scene in a specific embodiment; Figure 3 is a SAR imaging result diagram of an observation area in a specific embodiment; Figure 4 A three-dimensional result diagram of a prior art Fourier transform method in a specific embodiment; Figure 5 A three-dimensional result diagram of the method of the present application in a specific embodiment; Figure 6 This is a diagram showing the step height estimation result based on the Fourier transform method in the prior art in a specific embodiment; Figure 7 A diagram showing a step height difference estimation result based on a Fourier transform method in the prior art in a specific embodiment; Figure 8 This is a diagram showing the step height estimation result of the method of the present application in a specific embodiment; Figure 9 A diagram showing the step height difference estimation result of the method of the present application in a specific embodiment; Figure 10 is a structural block diagram of a three-dimensional imaging device of a ground-based synthetic aperture radar in one embodiment; Figure 11 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for explaining this application and are not intended to limit this application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in this application without creative work are within the scope of protection of this application.

[0021] In addition, the terms "first," "second," and so on, used in this application are for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "multiple groups" means at least two groups, such as two groups, three groups, and so on, unless otherwise specifically defined.

[0022] In this application, unless otherwise specified or limited, the terms "connect," "fix," etc. should be understood in a broad sense. For example, "fix" can mean a fixed connection, a detachable connection, or an integral connection; it can mean a mechanical connection, an electrical connection, a physical connection, or a wireless communication connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean internal communication between two elements or an interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0023] In addition, the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0024] This application provides a three-dimensional imaging method of ground-based synthetic aperture radar, such as Figure 1 The flowchart shown, in one embodiment, includes: Step 101 : Acquire the echo of the ground-based synthetic aperture radar, project the echo onto the same imaging area grid in each receiving channel, and obtain a two-dimensional radar image of each receiving channel.

[0025] Specifically: Acquire the echo of ground-based synthetic aperture radar; For a receiving channel, the back projection (BP) algorithm is used to project the echo onto the imaging area grid to obtain the two-dimensional radar image of the receiving channel; All receiving channels are traversed and the back-projection algorithm is used to project the echoes onto the same imaging area grid to obtain a two-dimensional radar image for each receiving channel.

[0026] In this step, the back projection (BP) algorithm belongs to the existing technology and will not be described in detail here.

[0027] Step 102: Obtain the pitch angle estimation range and construct a pitch array manifold. Pixels in the same range-azimuth unit of all 2D radar images are combined into a pixel vector. The pitch array manifold and the pixel vector are used as inputs to the OMP algorithm to obtain multiple target pitch estimation results corresponding to different range-azimuth units.

[0028] Specifically: Obtaining a pitch angle estimation range, and evenly dividing the pitch angle estimation range into a plurality of grids, so as to construct a pitch array flow pattern according to the divided plurality of grids; All pixels of the two-dimensional radar image in the same range-azimuth unit are combined into a pixel vector; Traverse all range-azimuth units and obtain the pixel vector of each range-azimuth unit; The pitch array flow pattern and a pixel vector are used as inputs of the OMP algorithm to obtain the target pitch estimation result of the corresponding range-azimuth unit. Traverse all pixel vectors to obtain the target pitch estimation result corresponding to each range-azimuth unit.

[0029] More specifically: Obtain the pitch angle estimation range and evenly divide the pitch angle estimation range into multiple grids to construct the pitch array flow pattern based on the divided multiple grids:

[0030] Where, is the pitch array flow pattern, is the pitch angle, , is the center carrier frequency, is the array element spacing, is the speed of light, is the number of array elements uniformly distributed vertically, for The transpose of All pixels of the two-dimensional radar image in the same range-azimuth unit are combined into a pixel vector; Traverse all range-azimuth units and obtain the pixel vector of each range-azimuth unit; The pitch array flow pattern and a pixel vector are used as inputs of the OMP algorithm to obtain the target pitch estimation result of the corresponding range-azimuth unit. Traverse all pixel vectors to obtain the target pitch estimation result corresponding to each range-azimuth unit.

[0031] In this step, the OMP algorithm includes: Input pitch array flow pattern and the pixel vector of the same range-azimuth unit , let the residual , select the angle set , number of iterations , to complete the initialization; Choose an angle that best correlates with the current residual The corresponding steering vector ; The selected Add angle collection ; Solve the sparse representation of the target by least squares ; Update residual , ; If the convergence condition is not reached, increase the number of iterations and let , repeatedly select the steering vector and update the residual until the convergence condition is reached.

[0032] Step 103 : Project each target pitch estimation result into a spatial rectangular coordinate system through coordinate transformation to obtain a corresponding height estimation result; and obtain a three-dimensional point cloud result of the imaging scene based on all the height estimation results.

[0033] Specifically: Using the coordinate transformation formula, the pitch estimation result of each target is projected into the spatial rectangular coordinate system through coordinate transformation to obtain the corresponding height estimation result; Based on all the height estimation results, the three-dimensional point cloud result of the imaging scene is obtained.

[0034] In this step, the coordinate transformation formula is:

[0035] Where, is the true height of the target, is the distance to the target, The direction of the target, is the target's elevation angle.

[0036] Step 104 : clustering the three-dimensional point cloud results using a density-based spatial clustering algorithm, and then performing noise reduction processing to obtain a three-dimensional point cloud image.

[0037] Specifically: According to the echo of ground-based synthetic aperture radar, the parameters of the density-based spatial clustering of applications with noise (DBSCAN) algorithm are determined: the DBSCAN algorithm describes the compactness of sample distribution by presetting the parameters neighborhood radius (Eps) and density threshold (MinPts); when the data point When there are MinPts points in the spherical neighborhood of , the point is the core point; if lie in In the Eps neighborhood, Relative to density is reachable; if there is a sample , Depend on Density direct, and ,but Depend on Density can reach The maximum density of the initial points that can be reached and the density-reachable point set can be aggregated into a cluster (specifically, in practical applications, the DBSAN clustering parameters can be determined through statistical analysis methods: the value of the parameter Eps is set to twice the data dimension. For example, for targets in three-dimensional space, the value of Eps is usually 6; the selection of the parameter MinPts depends on the distance measurement criterion between samples. The K-nearest distance relationship diagram between samples can be calculated, and the position corresponding to the inflection point is used as the appropriate value of MinPts); A density-based spatial clustering algorithm is used to cluster the 3D point cloud results, and the 3D point cloud results are divided into different clusters. For the 3D point cloud results divided into different clusters, outlier noise points marked as noise clusters and clusters with less data are removed to obtain a 3D point cloud image (specifically, clusters with less data are determined based on the number of point clouds, and clusters with less point clouds than 1 / 100 of the total number of point clouds need to be removed).

[0038] In this step, the density-based spatial clustering (DBSCAN) algorithm belongs to the existing technology and will not be described in detail here.

[0039] The above-mentioned three-dimensional imaging method of ground-based synthetic aperture radar solves the problem of low altitude resolution and inability to achieve super-resolution three-dimensional imaging in existing imaging methods by using the orthogonal matching pursuit (OMP) sparse reconstruction method based on the compressed sensing framework. It effectively realizes altitude super-resolution imaging of ground-based synthetic aperture radar, and then realizes super-resolution three-dimensional imaging. The DBSCAN clustering method is then used to further reduce noise and improve imaging quality, laying the foundation for subsequent target recognition and parameter estimation steps. It provides a more reliable solution for the application of ground-based synthetic aperture radar in scenarios such as anti-ship warfare and unmanned system perception, and has great application value in actual engineering.

[0040] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0041] In a specific embodiment, the method of the present application was verified and compared with the results of the prior art method based on Fourier transform.

[0042] Specifically, real-world experiments were conducted using a multi-baseline rotary scanning SAR imaging experimental platform based on millimeter-wave radar. The millimeter-wave radar system used consists of the TI AWR2944 radar front end and the TI DCA1000EVM acquisition board. The TI AWR2944 has four transmitting antennas and four receiving antennas, with the spacing between two adjacent receiving antennas being half a wavelength. However, the spacing between two adjacent transmitting antennas is one wavelength, and one transmitting antenna is positioned at a different height. Therefore, only the first, third, and fourth transmitting antennas can be combined to form the multi-channel radar required for 3D imaging. The rotary motor in the rotary scanning mechanical subsystem uses electric power steering, allowing for remote control of speed and direction via a mobile phone. The modular setup also allows for flexible adjustment of the arm length. Specific parameters of the experimental system are shown in Table 1.

[0043] Table 1: Specific parameters of the experimental system

[0044] like Figure 2 Optical image of the experimental scene shown: The experimental scene is an open square; there are quasi-arc-shaped steps at the edge of the square. The step facade forms a dihedral angle with the horizontal plane, producing a strong angular reflection effect on radar waves. There are five steps in total, each 0.364m high and 0.796m deep, forming a stepped structure with a height difference of 1.82m.

[0045] In the process of measuring data processing, BP algorithm was used to complete the two-dimensional image reconstruction of each channel. The SAR imaging results of the observation area are as follows: Figure 3 As shown in the box.

[0046] Figure 4 and Figure 5 The three-dimensional results based on the Fourier transform method and the method of the present application are respectively shown. It can be seen that the inversion contour of the steps by the method of the present application is clearer.

[0047] like Figures 6 to 9 As shown in FIG, a quantitative analysis method is used to analyze the step estimation accuracy of different methods. It can be seen that the present invention has a higher estimation accuracy for the step height, which illustrates the effectiveness of the present invention method.

[0048] This application also provides a three-dimensional imaging device of a ground-based synthetic aperture radar, such as Figure 10 As shown, in one embodiment, it includes: a first module 1001, a second module 1002, a third module 1003 and a fourth module 1004, wherein: The first module 1001 is used to obtain the echo of the ground-based synthetic aperture radar, project the echo to the same imaging area grid in each receiving channel, and obtain a two-dimensional radar image for each receiving channel; The second module 1002 is configured to obtain an elevation angle estimation range and construct an elevation array flow pattern. All pixels in the same range-azimuth unit of the 2D radar image are combined into a pixel vector. The elevation array flow pattern and the pixel vector are used as inputs to the OMP algorithm to obtain elevation estimation results for multiple targets corresponding to different range-azimuth units. The third module 1003 is used to project each target pitch estimation result into a spatial rectangular coordinate system through coordinate transformation to obtain a corresponding height estimation result; and obtain a three-dimensional point cloud result of the imaging scene based on all the height estimation results; The fourth module 1004 is used to cluster the three-dimensional point cloud results using a density-based spatial clustering algorithm, and then perform noise reduction processing to obtain a three-dimensional point cloud image.

[0049] The specific definition of a ground-based synthetic aperture radar three-dimensional imaging device can be found in the definition of a ground-based synthetic aperture radar three-dimensional imaging method above, and will not be repeated here. Each module in the above-mentioned device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0050] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 11 As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a three-dimensional imaging method for a ground-based synthetic aperture radar is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0051] Those skilled in the art will understand that Figure 11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0052] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0053] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0054] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0055] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0056] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A three-dimensional imaging method for ground-based synthetic aperture radar, characterized in that: include: Acquire the echo of the ground-based synthetic aperture radar, project the echo onto the same imaging area grid in each receiving channel, and obtain a two-dimensional radar image for each receiving channel; The pitch angle estimation range is obtained and the pitch array manifold is constructed. All pixels in the same range-azimuth unit of the 2D radar image are combined into a pixel vector. The pitch array manifold and the pixel vector are used as inputs to the OMP algorithm to obtain the pitch estimation results of multiple targets corresponding to different range-azimuth units. Project the pitch estimation result of each target into the spatial rectangular coordinate system through coordinate transformation to obtain the corresponding height estimation result; based on all the height estimation results, obtain the three-dimensional point cloud result of the imaging scene; A density-based spatial clustering algorithm is used to cluster the 3D point cloud results, and then noise reduction is performed to obtain a 3D point cloud image.

2. The three-dimensional imaging method of a ground-based synthetic aperture radar according to claim 1, characterized in that: Acquire the echo of the ground-based synthetic aperture radar and project it onto the same imaging area grid in each receiving channel to obtain a two-dimensional radar image for each receiving channel, including: Acquire the echo of ground-based synthetic aperture radar; For a receiving channel, a back-projection algorithm is used to project the echo onto the imaging area grid to obtain a two-dimensional radar image of the receiving channel; All receiving channels are traversed and the back-projection algorithm is used to project the echoes onto the same imaging area grid to obtain a two-dimensional radar image for each receiving channel.

3. The three-dimensional imaging method of a ground-based synthetic aperture radar according to claim 1 or 2, characterized in that: The pitch angle estimation range is obtained and the pitch array manifold is constructed. All pixels in the same range-azimuth unit of the 2D radar image are combined into a pixel vector. The pitch array manifold and the pixel vector are used as inputs to the OMP algorithm to obtain multiple target pitch estimation results corresponding to different range-azimuth units, including: Obtaining a pitch angle estimation range, and evenly dividing the pitch angle estimation range into a plurality of grids, so as to construct a pitch array flow pattern according to the divided plurality of grids; All pixels of the two-dimensional radar image in the same range-azimuth unit are combined into a pixel vector; Traverse all range-azimuth units and obtain the pixel vector of each range-azimuth unit; The pitch array flow pattern and a pixel vector are used as inputs of the OMP algorithm to obtain the target pitch estimation result of the corresponding range-azimuth unit. Traverse all pixel vectors to obtain the target pitch estimation result corresponding to each range-azimuth unit.

4. The three-dimensional imaging method of ground-based synthetic aperture radar according to claim 3, characterized in that: Obtain a pitch angle estimation range and evenly divide the pitch angle estimation range into multiple grids, so as to construct a pitch array flow pattern based on the divided multiple grids, including: Where, is the pitch array flow pattern, is the pitch angle, is the center carrier frequency, is the array element spacing, is the speed of light, is the number of array elements uniformly distributed vertically, for The transpose of .

5. The three-dimensional imaging method of ground-based synthetic aperture radar according to claim 4, characterized in that: The OMP algorithm includes: Input pitch array flow pattern and the pixel vector of the same range-azimuth unit , let the residual , select the angle set , number of iterations , to complete the initialization; Choose an angle that best correlates with the current residual The corresponding steering vector ; The selected Add angle collection ; Solve the sparse representation of the target by least squares ; Update residual , ; If the convergence condition is not reached, increase the number of iterations and let , repeatedly select the steering vector and update the residual until the convergence condition is reached.

6. The three-dimensional imaging method of ground-based synthetic aperture radar according to claim 1 or 2, characterized in that: Project the pitch estimation result of each target into the spatial rectangular coordinate system through coordinate transformation to obtain the corresponding height estimation result; Based on all the height estimation results, the 3D point cloud results of the imaging scene are obtained, including: Using the coordinate transformation formula, the pitch estimation result of each target is projected into the spatial rectangular coordinate system through coordinate transformation to obtain the corresponding height estimation result; Based on all the height estimation results, the three-dimensional point cloud result of the imaging scene is obtained.

7. The three-dimensional imaging method of ground-based synthetic aperture radar according to claim 6, characterized in that: The coordinate transformation formula is: Where, is the true height of the target, is the distance to the target, The direction of the target, is the pitch angle of the target.

8. The three-dimensional imaging method of ground-based synthetic aperture radar according to claim 1 or 2, characterized in that: A density-based spatial clustering algorithm is used to cluster the 3D point cloud results, and then noise reduction is performed to obtain a 3D point cloud image, including: Determine the parameters of density-based spatial clustering algorithm based on the echo of ground-based synthetic aperture radar; A density-based spatial clustering algorithm is used to cluster the 3D point cloud results, and the 3D point cloud results are divided into different clusters. For the three-dimensional point cloud results divided into different clusters, the outlier noise points marked as noise clusters are removed to obtain a three-dimensional point cloud image.

9. A three-dimensional imaging device for a ground-based synthetic aperture radar, characterized in that: include: The first module is used to obtain the echo of the ground-based synthetic aperture radar, project the echo to the same imaging area grid in each receiving channel, and obtain a two-dimensional radar image for each receiving channel; The second module is used to obtain the estimated range of the pitch angle and construct the pitch array flow pattern; All pixels in the same range-azimuth unit of the 2D radar image are combined into a pixel vector. The pitch array manifold and a pixel vector are used as inputs of the OMP algorithm to obtain multiple target elevation estimation results corresponding to different range-azimuth units. The third module is used to project the pitch estimation results of each target into a spatial rectangular coordinate system through coordinate transformation to obtain the corresponding height estimation results; based on all the height estimation results, a three-dimensional point cloud result of the imaging scene is obtained; The fourth module is used to cluster the three-dimensional point cloud results using a density-based spatial clustering algorithm, and then perform noise reduction processing to obtain a three-dimensional point cloud image.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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