Echo history based through SAR motion error compensation imaging method and device

Abstract

The application relates to a through SAR motion error compensation imaging method and device based on an echo history. One-dimensional range images and a preliminary target image are obtained by processing original echo data of a target. An initial position of the target and an actual distance between a radar actual position and a wall are obtained according to the preliminary target image and the one-dimensional range images respectively. Then, the distances between the radar at different positions and the target are extracted in the one-dimensional range images according to the initial position of the target, so that the echo history of the target is generated. The actual position of the target and the actual motion path of the radar are taken as unknown variables to be solved. A mathematical model is constructed based on the relationship among the unknown variables, the actual distance between the radar actual position and the wall and the echo history of the target. The mathematical model is solved by adopting a Gauss-Newton optimization algorithm. Finally, the motion error of the radar itself is corrected by using the result obtained by solving, and a final target imaging result is obtained. The motion error of the radar itself can be compensated by using echo information obtained by the radar itself.

Description

Technical Field

[0001] This application relates to the fields of radar signal processing and imaging technology, and in particular to a method and apparatus for penetrating SAR motion error compensation imaging based on echo history. Background Technology

[0002] Through-wall radar, due to the low-frequency characteristics of its electromagnetic waves, can penetrate materials such as wood and brick walls, enabling the location of targets inside buildings. Vehicle-mounted (or airborne) through-wall radars using synthetic aperture radar (SAR) technology orbit around the exterior of buildings, emitting low-frequency ultra-wideband (UWB) signals to ensure high range resolution of the echoes, and achieving high azimuth resolution through aperture accumulation. This allows for imaging of the building's internal structure, and its application has become increasingly widespread in recent years with the maturity of unmanned aerial vehicle (UAV) technology.

[0003] Unmanned aerial vehicle (UAV) platforms equipped with radar typically operate along fixed, pre-set trajectories and acquire their own position information using an inertial measurement unit (IMU) and a global positioning system (GPS). However, in practical applications, interference from factors such as the UAV's own vibration characteristics when hovering or flying close to walls, environmental factors, and human operation prevents the UAV from strictly adhering to the pre-set trajectory. Furthermore, the real-time position acquired by the UAV's own positioning system in complex urban environments contains a significant positioning error compared to the radar's actual position. The IMU, limited by hardware constraints and noise, cannot accurately capture this positioning error information, and the trajectory calculated based on inertial navigation itself suffers from cumulative drift, making it unsuitable for recording the actual platform position information within the time required for synthetic aperture radar (SAR). In environments close to buildings, GPS satellite communication is frequently interrupted, and the positioning system may also be unstable. Therefore, these practical conditions limit the means to eliminate platform motion errors from a hardware perspective.

[0004] Therefore, we consider eliminating motion errors from an algorithmic perspective. Classical motion error compensation methods such as Map Drift (MD), Phase Gradient Autofocus (PGA), and the more recent Multiple Aperture Map Drift (MAM) all rely on the assumption that the slant range error caused by motion errors does not exceed one range cell. Therefore, they are unsuitable for UWB radars facing significant positioning errors in flight trajectories. Other methods for autofocus compensation of UAV flight positioning errors typically rely on a far-field assumption, under which the slant range error caused by positioning errors can be ignored. However, the effective range of through-wall radar is usually within 20 meters, and the imaging environment is complex with numerous walls, making it unable to detect distant targets. Therefore, the far-field assumption is not met. Summary of the Invention

[0005] Therefore, it is necessary to provide a method and apparatus for penetrating SAR motion error compensation imaging based on echo history, which can compensate for the radar's own motion error and generate clear imaging results, in order to address the above-mentioned technical problems.

[0006] A penetration SAR motion error compensation imaging method based on echo history, the method comprising:

[0007] Acquire raw echo data of the target, which is obtained by a moving radar detecting the target behind a wall;

[0008] The original echo data is pulse-compressed to obtain a corresponding one-dimensional range image, and a preliminary target image is generated based on the one-dimensional range image;

[0009] The target is detected in the preliminary target image to obtain the initial position of the target;

[0010] The echo history of the direct wave to the wall surface is extracted based on the one-dimensional distance image;

[0011] Based on the initial position of the target, the distance between the radar and the target at different positions is extracted from the one-dimensional range image to generate the echo history of the target;

[0012] The actual position of the target and the actual movement path of the radar are taken as unknown variables to be solved, and a mathematical model is constructed based on the relationship between each unknown variable, the echo history of the wall direct wave and the echo history of the target.

[0013] The actual position of the target and the actual movement path of the radar are obtained by solving the mathematical model using the Gauss-Newton optimization algorithm.

[0014] The radar's own motion error is corrected using the solution, and the refraction effect is compensated based on the optimized wall parameters to obtain the final target imaging result.

[0015] In one embodiment, when detecting the target in the preliminary target image, the CFAR method is used to extract isolated strong features from the preliminary target image, and the position coordinates of the strong features are used as the initial position of the target.

[0016] In one embodiment, the extraction of the echo history of the wall direct wave based on the one-dimensional distance image includes:

[0017] In the one-dimensional range image, the distance between the leading edge of the wall and the radar detected by the radar when the radar moves to different positions constitutes the echo history of the wall direct wave.

[0018] In one embodiment, when generating the echo history of the target:

[0019] Calculate the error position distance between the initial position of the target and the error position of the radar, wherein the error position is obtained by the radar's own positioning system;

[0020] Centered on the error location distance, peak detection is performed on the range image of the radar at all locations in the one-dimensional range image within a preset detection range to extract the distance between the target and the radar, thus forming the echo history of the target.

[0021] In one embodiment, the mathematical model constructed based on the relationship between the unknown variables, the echo history of the direct wave from the wall, and the echo history of the target includes:

[0022] The first observation matrix is ​​constructed based on the radar's initial position, actual position, and the echo history of the direct wave from the wall.

[0023] The second observation is constructed based on the target's echo history being equal to the straight-line distance between the radar and the target plus the refraction effect compensation.

[0024] The mathematical model is obtained by combining the first observation matrix and the second observation matrix.

[0025] In one embodiment, the first observation matrix is ​​represented as:

[0026] y wall -y m =r f (m)

[0027] Among them, y wall =y0,m (1)+R y

[0028] In the above formula, y wall Indicates the position of the wall at a distance upwards, y m This indicates the actual position of the radar in the range direction, r. f (m) represents the echo history of the leading edge direct wave, y 0,m (1) indicates the initial position of the radar in the range direction, R y This indicates the straight-line distance between the radar and the wall.

[0029] In one embodiment, the second observation matrix is ​​represented as:

[0030]

[0031] In the above formula, x m y m and x k y k The actual movement path of the radar and the actual position of the target are respectively represented, d represents the thickness of the wall, and ε represents the actual movement path of the radar and the actual position of the target. r θ represents the dielectric constant of the wall. mk Let r represent the angle of incidence between the radar and the k-th target at time m. k (m) represents the echo history of the target.

[0032] In one embodiment, when solving the mathematical model;

[0033] The initial position of the target and the error path obtained by the radar's own positioning system are used as the initial values ​​of the unknown variables, and these values ​​are substituted into the data model. The Gauss-Newton optimization algorithm is then used to optimize each of the unknown variables until the actual position of the target and the actual motion path of the radar are obtained.

[0034] This application also provides a penetration SAR motion error compensation imaging device based on echo history, the device comprising:

[0035] The raw echo data acquisition module is used to acquire the raw echo data of the target, which is obtained by a moving radar detecting the target behind a wall.

[0036] The preliminary target image generation module is used to perform pulse compression on the original echo data to obtain the corresponding one-dimensional range image, and generate a preliminary target image based on the one-dimensional range image;

[0037] The initial position of the target is obtained by a module for detecting the target in the preliminary target image and obtaining the initial position of the target.

[0038] The module for obtaining the actual distance between the radar and the wall is used to extract the echo history of the direct wave from the wall based on the one-dimensional distance image.

[0039] The target echo history generation module is used to extract the distance between the radar and the target at different positions in the one-dimensional range image based on the initial position of the target, and generate the echo history of the target.

[0040] The mathematical model construction module is used to take the actual position of the target and the actual movement path of the radar as unknown variables to be solved, and to construct a mathematical model based on the relationship between each unknown variable, the actual distance between the actual position of the radar and the wall, and the echo history of the target.

[0041] The mathematical model solving module is used to solve the mathematical model by employing the Gauss-Newton optimization algorithm to obtain the actual position of the target and the actual movement path of the radar;

[0042] The target imaging result acquisition module is used to correct the radar's own motion error using the solution obtained, and then compensate for the refraction effect based on the optimized wall parameters to obtain the final target imaging result.

[0043] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0044] Acquire raw echo data of the target, which is obtained by a moving radar detecting the target behind a wall;

[0045] The original echo data is pulse-compressed to obtain a corresponding one-dimensional range image, and a preliminary target image is generated based on the one-dimensional range image;

[0046] The target is detected in the preliminary target image to obtain the initial position of the target;

[0047] The echo history of the direct wave to the wall surface is extracted based on the one-dimensional distance image;

[0048] Based on the initial position of the target, the distance between the radar and the target at different positions is extracted from the one-dimensional range image to generate the echo history of the target;

[0049] The actual position of the target and the actual movement path of the radar are taken as unknown variables to be solved, and a mathematical model is constructed based on the relationship between each unknown variable, the echo history of the wall direct wave and the echo history of the target.

[0050] The actual position of the target and the actual movement path of the radar are obtained by solving the mathematical model using the Gauss-Newton optimization algorithm.

[0051] The radar's own motion error is corrected using the solution, and the refraction effect is compensated based on the optimized wall parameters to obtain the final target imaging result.

[0052] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0053] Acquire raw echo data of the target, which is obtained by a moving radar detecting the target behind a wall;

[0054] The original echo data is pulse-compressed to obtain a corresponding one-dimensional range image, and a preliminary target image is generated based on the one-dimensional range image;

[0055] The target is detected in the preliminary target image to obtain the initial position of the target;

[0056] The echo history of the direct wave to the wall surface is extracted based on the one-dimensional distance image;

[0057] Based on the initial position of the target, the distance between the radar and the target at different positions is extracted from the one-dimensional range image to generate the echo history of the target;

[0058] The actual position of the target and the actual movement path of the radar are taken as unknown variables to be solved, and a mathematical model is constructed based on the relationship between each unknown variable, the echo history of the wall direct wave and the echo history of the target.

[0059] The actual position of the target and the actual movement path of the radar are obtained by solving the mathematical model using the Gauss-Newton optimization algorithm.

[0060] The radar's own motion error is corrected using the solution, and the refraction effect is compensated based on the optimized wall parameters to obtain the final target imaging result.

[0061] The aforementioned imaging method and apparatus for penetrating SAR motion error compensation based on echo history processes the raw echo data of the target to obtain a corresponding one-dimensional range image, thereby generating a preliminary target image. The echo history of the direct wave from the wall is obtained from the one-dimensional range image. Then, based on the target's initial position, the distance between the radar and the target at different positions is extracted from the one-dimensional range image to generate the target's echo history. Next, the actual position of the target and the actual motion path of the radar are used as unknown variables to be solved. A mathematical model is constructed based on the relationships between these unknown variables, the actual distance between the radar's actual position and the wall, and the target's echo history. The mathematical model is solved using a Gauss-Newton optimization algorithm to obtain the actual position of the target and the actual motion path of the radar. Finally, the solution is used to correct the radar's own motion error. After compensating for the refraction effect based on the optimized wall parameters, the final target imaging result is obtained. This method can compensate for its own motion error using the echo information acquired by the radar itself, significantly reducing the impact of the radar's own position error on the imaging result, thus clearly reflecting the external structure of the building and the distribution of internal targets. Attached Figure Description

[0062] Figure 1 This is a flowchart illustrating a penetration SAR motion error compensation imaging method based on echo history in one embodiment.

[0063] Figure 2 This is a flowchart illustrating the wall refraction effect in one embodiment;

[0064] Figure 3 This is a schematic diagram illustrating the optimization process for conducting experimental measurements using the method presented in this paper.

[0065] Figure 4 This is a schematic diagram showing the comparison of the results before and after optimization in the experimental test using this method;

[0066] Figure 5 This is a structural block diagram of a penetrating SAR motion error compensation imaging device based on echo history in one embodiment.

[0067] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0069] In existing technologies, radar positioning information acquired through hardware lacks reference value or is subject to drift, and through-wall SAR operates under approach conditions. Therefore, in this embodiment, as... Figure 1 As shown, a penetration SAR motion error compensation imaging method based on echo history is provided, including the following steps:

[0070] Step S100: Obtain the raw echo data of the target. The raw echo data is obtained by the moving radar detecting the target behind the wall.

[0071] Step S110: Pulse compression is performed on the original echo data to obtain the corresponding one-dimensional range image, and a preliminary target image is generated based on the one-dimensional range image.

[0072] Step S120: Detect the target in the preliminary target image to obtain the initial position of the target.

[0073] Step S130: Extract the echo history of the direct wave from the wall surface based on the one-dimensional distance image.

[0074] Step S140: Based on the initial position of the target, extract the distance between the radar and the target at different positions in the one-dimensional range image to generate the target's echo history.

[0075] In step S150, the actual position of the target and the actual movement path of the radar are taken as unknown variables to be solved, and a mathematical model is constructed based on the relationship between each unknown variable, the actual distance between the actual position of the radar and the wall, and the echo history of the target.

[0076] Step S160: Solve the mathematical model using the Gauss-Newton optimization algorithm to obtain the actual position of the target and the actual movement path of the radar.

[0077] Step S170: The radar's motion error is corrected using the solution, and the refraction effect is compensated based on the optimized wall parameters to obtain the final target imaging result.

[0078] In this embodiment, by extracting information about prominent points and walls from the echo history and coarse imaging results obtained from the radar, the true radar trajectory is estimated and motion errors are compensated for, thereby obtaining better autofocus imaging results.

[0079] In step S100, the target being detected can be an object behind a wall, or the environment behind the wall. The SAR radar, mounted on a moving device, detects the target during movement.

[0080] In step S110, the original echo pulse is compressed to obtain a one-dimensional range image, and a coarse imaging result, i.e., a preliminary target image, is generated. Since the signal mode is frequency-modulated continuous wave, pulse compression is performed using a matched filtering method. Furthermore, because the radar's own position has errors at this point, a coarse imaging result is obtained.

[0081] In step S120, when detecting the target in the preliminary target image, the CFAR method is used to extract isolated strong feature points from the preliminary target image, and the position coordinates of the strong feature points are used as the initial position (x) of the target. 0,k ,y 0,k ).

[0082] Here, to make the steps in this method clearer, the description of the target and radar positions can be aided by... Figure 2 The diagram showing the wall refraction effect should be noted, as the subsequent mathematical model is also based on this wall refraction effect.

[0083] like Figure 2 As shown, a coordinate system is established along the east-west direction of the building wall. The horizontal axis represents the azimuth direction in meters, and the vertical axis represents the altitude direction in meters. The radar moves horizontally parallel to the front edge of the wall. The solid black line represents the actual movement path of the radar, the triangles represent the locations of each radar transmission and reception, and the dotted line represents the radar movement trajectory recorded by GPS. Target K is located behind the wall. During radar detection, the same target can be detected multiple times by the radar from different locations.

[0084] In this embodiment, it is assumed that Figure 2 The triangle in the diagram represents the radar's initial position, and the gray triangle represents the antenna position where target K can be observed. Then, regarding the distance r between target K and the radar... k (m) can be extracted from radar echoes at observable locations. The distance information at different locations together form the echo history of the target K, thereby generating a series of equations related to the radar's own position and environmental parameters based on the echo history.

[0085] In addition to the target echo, Figure 2 It also demonstrates the relationship between the direct waves along the front and rear edges of the wall and the unknown parameters. Compared to point targets, the advantages of direct waves are high echo intensity, easy extraction, and a naturally zero angle of incidence, eliminating the need for separate estimation of the angle of incidence.

[0086] Next, in step S130, the echo history of the wall direct wave is extracted based on the one-dimensional range image: in the one-dimensional range image, the distance between the front edge of the wall and the radar detected when the radar moves to different positions is used to form the echo history of the wall direct wave.

[0087] Specifically, in penetration imaging, the wall-leading direct wave provides rich measurement information related to unknown parameters. Without loss of generality, the peak with the largest amplitude in the one-dimensional range image is the wall-leading direct wave. Assuming there is no target between the radar and a section of wall, the position of the wall-leading direct wave is closest to the radar. The position r of the leading-edge direct wave detected by the radar at different times is then considered. f (m) The echo path of the direct wave r f And take r f The average value is used as the distance R between the actual radar position and the wall. f :

[0088]

[0089] In step S140, when generating the echo history of the target: the error position distance between the initial position of the target and the error position of the radar is calculated, wherein the error position is obtained by the radar's own positioning system. Then, with the error position distance as the center, peak detection is performed on the range image of the radar at all positions in the one-dimensional range image within a preset detection range to extract the distance between the target and the radar and form the echo history of the target.

[0090] Specifically, based on the target's initial position (x) 0,k ,y 0,k ) and radar position with errors (x) m ,y m ), calculate the distance between the two:

[0091]

[0092] by Centered on the target, in this embodiment, the tolerances of 20m to the left and right are preset ranges. The tolerance should not be too small, otherwise the true peak value of the target will not be detected; nor should it be too large, otherwise other targets may be falsely detected.

[0093] Within a preset range, peak detection is performed on the range profiles of the radar at all locations to extract the distance r between the target and the radar. k (m), the echo history r of target k k .

[0094] In step S150, when setting it as a variable, the actual position of the target (x) is set. k ,y k ), where x k Let y be the azimuth position corresponding to the minimum value in the echo history of target k. Assuming the echo history is correctly extracted, this parameter is unaffected by radar motion errors and can therefore be considered a known quantity. kTherefore, due to the influence of the radar's own position, it is considered an unknown parameter to be optimized, along with the unknown actual movement path of the radar (x). m ,y m Together they form This represents the unknown variable to be solved.

[0095] In this embodiment, the mathematical model constructed based on the relationship between various unknown variables, the actual distance between the radar's actual position and the wall, and the target's echo history includes: constructing a first observation matrix based on the radar's initial position, actual position, and the actual distance between the radar's actual position and the wall; constructing a second observation matrix based on the target's echo history being equal to the straight-line distance between the radar and the target plus a refraction compensation amount; and finally, combining the first and second observation matrices to obtain the mathematical model.

[0096] In this embodiment, when constructing the first observation matrix, based on experience, the initial position of the radar motion is measurable, and the initial value of the radar's own positioning system has high reliability. Therefore, based on the radar's own initial position y... 0,m (1) and R y Calculate the position of the wall at the upward distance:

[0097] y wall =y 0,m (1)+R y (3)

[0098] Based on the obtained leading-edge direct wave and the position r detected by the radar at different times... f (m), then we can obtain:

[0099] y wall -y m =r f (m) (4)

[0100] In the above formula, y wall Indicates the position of the wall at a distance upwards, y m This represents the actual position of the radar in the range direction, which is the position that needs to be solved for, r. f (m) represents the echo history of the leading edge direct wave, y 0,m (1) Represents the initial position of the radar in the range direction, R y This indicates the straight-line distance between the actual location of the radar and the wall.

[0101] In this embodiment, when constructing the second observation matrix, the distance between the target and the radar is equal to the straight-line distance between them plus the refraction effect compensation.

[0102]

[0103] In formula (5), x m y m and x k y k These represent the actual movement path of the radar and the actual position of the target, respectively; d represents the wall thickness; ε r θ represents the dielectric constant of the wall. mk Let r represent the angle of incidence between the radar position at time m and the target at time k. k (m) represents the echo history of the target.

[0104] In this embodiment, for θ mk When making the estimation, the formula atan(|x) is used. m -x k | / |y m -y k |) Perform the calculation.

[0105] In this embodiment, formulas (4) and (5) represent the ideal situation. However, in practical applications, the obtained measurement values ​​are affected by various noises. The model proposed above can only be as accurate as possible. Therefore, noise is introduced below to consider how to estimate unknown parameters in noisy data. After introducing the influence of noise, and combining the first observation matrix and the second observation matrix, the resulting mathematical model is expressed as follows:

[0106]

[0107] Given a known mathematical model, estimating the optimal solution for the unknown variables is equivalent to finding... The maximum a posteriori probability estimate. Because If the prior estimate is unknown, then the maximum a posteriori estimation problem is transformed into maximum likelihood estimation. The problem to be solved is ultimately transformed into:

[0108]

[0109] final, The estimation problem is transformed into a least squares problem, which can be solved using the Gauss-Newton method.

[0110] In this embodiment, when using the Gauss-Newton method to solve the problem, the initial position of the target and the error path obtained by the radar's own positioning system are used as the initial values ​​of the unknown variables and substituted into the data model. The Gauss-Newton optimization algorithm is then used to optimize each unknown variable until the actual position of the target and the actual motion path of the radar are obtained.

[0111] It should be noted here that in this article, both the error position obtained by the radar's own positioning system and the actual radar position that needs to be calculated are represented by x.m y m The reason for using the same letters to represent the actual position and the error position is that when solving for the actual position of the radar, the error position is used as the initial value and then continuously optimized by the algorithm.

[0112] like Figure 3 The diagram shows the optimization process for conducting experimental tests using the method described in this paper. The horizontal axis represents the number of iterations, and the vertical axis represents the increment during the optimization process. Figure 3 As shown, after 10 iterations, the parameter increment decreases from 8 to 10. -6 This indicates that the algorithm can converge to a stable value.

[0113] The signal has a starting frequency of 2.7 GHz, a bandwidth of 500 MHz, a pulse width of 440 μs, and a pulse repetition frequency of 1923 Hz. A multi-rotor UAV flies along the south side of the building at a speed of approximately 0.4727 m / s, flying uniformly along the wall from east to west. On-site inspection revealed that the wall thickness is approximately 0.3 m and its dielectric constant is approximately 6. Due to the UAV's own jitter and the electromagnetic interference affecting its navigation system, the onboard platform's positioning information exhibits cumulative offsets and periodic fluctuations, resulting in positioning errors. This method can estimate and compensate for motion errors caused by the radar's own positioning system and autofocus to generate imaging results with better focusing effects.

[0114] like Figure 4 As shown, this is a comparison of the imaging results before and after optimization in the experimental results presented in this paper. Figure 4 (a) represents the unoptimized coarse imaging result. Figure 4 (b) shows the compensated imaging result. The horizontal axis represents the east-west azimuth in meters, and the vertical axis represents the north-south azimuth in meters. The comparison shows that the target behind the wall can be focused as a point after compensation, and the front and rear edges of the wall can be distinguished, resulting in a significant improvement in imaging quality. Since the outer side of the building used for imaging in this experiment is not a continuous plane, with the middle section of the wall recessed and the side walls protruding, the imaging results also reflect this phenomenon. This demonstrates that the present invention can compensate for the radar's own motion error under near-field SAR conditions, improving the imaging quality of penetrating radar in practical applications.

[0115] The aforementioned echo history-based penetration SAR motion error compensation imaging method extracts information on prominent points and walls from the echo history and coarse imaging results to estimate the true radar trajectory and compensate for motion errors, thereby obtaining better autofocus imaging results. This method compensates for its own motion errors using echo information acquired by the radar itself, significantly reducing the impact of radar position errors on the imaging results and clearly reflecting the external structure of buildings and the distribution of internal targets. Furthermore, this method is applicable to UWB-SAR, achieving good results even under the motion error-sensitive conditions of UWB systems. It is not limited by far-field approximation conditions and also achieves good results under near-field conditions, making it more suitable for penetration radar, especially for vehicle-mounted and airborne penetration radar applications in complex environments.

[0116] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed 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 some of the sub-steps or stages of other steps.

[0117] In one embodiment, such as Figure 5 As shown, a penetration SAR motion error compensation imaging device based on echo history is provided, comprising: a raw echo data acquisition module 200, a preliminary target image generation module 210, a target initial position acquisition module 220, an actual distance acquisition module between the radar and the wall module 230, a target echo history generation module 240, a mathematical model construction module 250, a mathematical model solving module 260, and a target imaging result acquisition module 270, wherein:

[0118] The raw echo data acquisition module 200 is used to acquire the raw echo data of the target, which is obtained by a moving radar detecting the target behind a wall.

[0119] The preliminary target image generation module 210 is used to perform pulse compression on the original echo data to obtain the corresponding one-dimensional range image, and generate a preliminary target image based on the one-dimensional range image.

[0120] The initial position of the target is obtained by module 220, which is used to detect the target in the preliminary target image and obtain the initial position of the target.

[0121] The module 230, which obtains the actual distance between the radar and the wall, is used to extract the echo history of the direct wave from the wall based on the one-dimensional distance image.

[0122] The target echo history generation module 240 is used to extract the distance between the radar and the target at different positions in the one-dimensional range image based on the initial position of the target, and generate the echo history of the target.

[0123] The mathematical model construction module 250 is used to take the actual position of the target and the actual movement path of the radar as unknown variables to be solved, and to construct a mathematical model based on the relationship between each unknown variable, the actual distance between the actual position of the radar and the wall, and the echo history of the target.

[0124] The mathematical model solving module 260 is used to solve the mathematical model by employing the Gauss-Newton optimization algorithm to obtain the actual position of the target and the actual movement path of the radar.

[0125] The target imaging result acquisition module 270 is used to correct the radar's own motion error using the solution obtained, and then to compensate for the refraction effect based on the optimized wall parameters to obtain the final target imaging result.

[0126] Specific limitations regarding the echo history-based penetrating SAR motion error compensation imaging device can be found in the limitations of the echo history-based penetrating SAR motion error compensation imaging method described above, and will not be repeated here. Each module in the aforementioned echo history-based penetrating SAR motion error compensation imaging device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0127] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a penetration SAR motion error compensation imaging method based on echo history. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0128] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0129] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0130] Acquire raw echo data of the target, which is obtained by a moving radar detecting the target behind a wall;

[0131] The original echo data is pulse-compressed to obtain a corresponding one-dimensional range image, and a preliminary target image is generated based on the one-dimensional range image;

[0132] The target is detected in the preliminary target image to obtain the initial position of the target;

[0133] The echo history of the direct wave to the wall surface is extracted based on the one-dimensional distance image;

[0134] Based on the initial position of the target, the distance between the radar and the target at different positions is extracted from the one-dimensional range image to generate the echo history of the target;

[0135] The actual position of the target and the actual movement path of the radar are taken as unknown variables to be solved, and a mathematical model is constructed based on the relationship between each unknown variable, the echo history of the wall direct wave and the echo history of the target.

[0136] The actual position of the target and the actual movement path of the radar are obtained by solving the mathematical model using the Gauss-Newton optimization algorithm.

[0137] The radar's own motion error is corrected using the solution, and the refraction effect is compensated based on the optimized wall parameters to obtain the final target imaging result.

[0138] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0139] Acquire raw echo data of the target, which is obtained by a moving radar detecting the target behind a wall;

[0140] The original echo data is pulse-compressed to obtain a corresponding one-dimensional range image, and a preliminary target image is generated based on the one-dimensional range image;

[0141] The target is detected in the preliminary target image to obtain the initial position of the target;

[0142] The echo history of the direct wave to the wall surface is extracted based on the one-dimensional distance image;

[0143] Based on the initial position of the target, the distance between the radar and the target at different positions is extracted from the one-dimensional range image to generate the echo history of the target;

[0144] The actual position of the target and the actual movement path of the radar are taken as unknown variables to be solved, and a mathematical model is constructed based on the relationship between each unknown variable, the echo history of the wall direct wave and the echo history of the target.

[0145] The actual position of the target and the actual movement path of the radar are obtained by solving the mathematical model using the Gauss-Newton optimization algorithm.

[0146] The radar's own motion error is corrected using the solution, and the refraction effect is compensated based on the optimized wall parameters to obtain the final target imaging result.

[0147] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual 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.

[0148] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0149] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for penetrating SAR motion error compensation imaging based on echo history, characterized in that, The method includes: Acquire raw echo data of the target, which is obtained by a moving radar detecting the target behind a wall; The original echo data is pulse-compressed to obtain a corresponding one-dimensional range image, and a preliminary target image is generated based on the one-dimensional range image; The target is detected in the preliminary target image to obtain the initial position of the target; The echo history of the direct wave to the wall surface is extracted based on the one-dimensional distance image; Based on the initial position of the target, the distance between the radar and the target at different positions is extracted from the one-dimensional range image to generate the echo history of the target; The actual position of the target and the actual movement path of the radar are taken as unknown variables to be solved. A mathematical model is constructed based on the relationship between the unknown variables, the echo history of the wall-direct wave, and the echo history of the target. Specifically, a first observation matrix is ​​constructed based on the initial position and actual position of the radar and the echo history of the wall-direct wave. A second observation matrix is ​​constructed based on the fact that the echo history of the target is equal to the straight-line distance between the radar and the target plus the refraction effect compensation. The mathematical model is obtained by combining the first observation matrix and the second observation matrix. The actual position of the target and the actual movement path of the radar are obtained by solving the mathematical model using the Gauss-Newton optimization algorithm. The radar's own motion error is corrected using the solution, and the refraction effect is compensated based on the optimized wall parameters to obtain the final target imaging result.

2. The penetrating SAR motion error compensation imaging method according to claim 1, characterized in that, When detecting the target in the preliminary target image, the CFAR method is used to extract isolated strong features from the preliminary target image, and the position coordinates of the strong features are used as the initial position of the target.

3. The penetrating SAR motion error compensation imaging method according to claim 1, characterized in that, The echo history of the wall direct wave extracted based on the one-dimensional distance image includes: In the one-dimensional range image, the distance between the leading edge of the wall and the radar detected by the radar when the radar moves to different positions constitutes the echo history of the wall direct wave.

4. The penetrating SAR motion error compensation imaging method according to claim 1, characterized in that, When generating the echo history of the target: Calculate the error position distance between the initial position of the target and the error position of the radar, wherein the error position is obtained by the radar's own positioning system; Centered on the error location distance, peak detection is performed on the range image of the radar at all locations in the one-dimensional range image within a preset detection range to extract the distance between the target and the radar, thus forming the echo history of the target.

5. The penetrating SAR motion error compensation imaging method according to claim 4, characterized in that, The first observation matrix is ​​represented as: ； in, ; In the above formula, This indicates the position of the wall at a distance upwards. This indicates the actual position of the radar in the range direction. This indicates the echo path of the leading edge direct wave. This indicates the initial position of the radar in the range direction. This indicates the straight-line distance between the radar and the wall.

6. The penetrating SAR motion error compensation imaging method according to claim 5, characterized in that, The second observation matrix is ​​represented as follows: ； In the above formula, , as well as , These represent the actual movement path of the radar and the actual position of the target, respectively. Indicates the wall thickness. Indicates the dielectric constant of the wall. Indicates the first m At the moment, the radar and the first k The angle of incidence between the targets This indicates the echo history of the target.

7. The penetrating SAR motion error compensation imaging method according to claim 5, characterized in that, When solving the mathematical model; The initial position of the target and the error path obtained by the radar's own positioning system are used as the initial values ​​of the unknown variables, and these values ​​are substituted into the data model. The Gauss-Newton optimization algorithm is then used to optimize each of the unknown variables until the actual position of the target and the actual motion path of the radar are obtained.

8. A penetration SAR motion error compensation imaging device based on echo history, characterized in that, The device implements the echo history-based penetrating SAR motion error compensation imaging method according to any one of claims 1-7, including: The raw echo data acquisition module is used to acquire the raw echo data of the target, which is obtained by a moving radar detecting the target behind a wall. The preliminary target image generation module is used to perform pulse compression on the original echo data to obtain the corresponding one-dimensional range image, and generate a preliminary target image based on the one-dimensional range image; The initial position of the target is obtained by a module for detecting the target in the preliminary target image and obtaining the initial position of the target. The module for obtaining the actual distance between the radar and the wall is used to extract the echo history of the direct wave from the wall based on the one-dimensional distance image. The target echo history generation module is used to extract the distance between the radar and the target at different positions in the one-dimensional range image based on the initial position of the target, and generate the echo history of the target. The mathematical model construction module is used to take the actual position of the target and the actual movement path of the radar as unknown variables to be solved, and to construct a mathematical model based on the relationship between each unknown variable, the actual distance between the actual position of the radar and the wall, and the echo history of the target. The mathematical model solving module is used to solve the mathematical model by employing the Gauss-Newton optimization algorithm to obtain the actual position of the target and the actual movement path of the radar; The target imaging result acquisition module is used to correct the radar's own motion error using the solution obtained, and then compensate for the refraction effect based on the optimized wall parameters to obtain the final target imaging result.

Images