Dual error synchronous compensation through-the-radar imaging method, device, equipment and medium

Abstract

The application relates to a double-error synchronous compensation penetration radar imaging method, device, equipment and medium, a one-dimensional range image is generated based on target radar echo data, echo histories of direct waves before and after a wall are extracted, a history time difference value of the two is calculated, then a position model representing an actual position of a penetration radar is constructed based on an antenna layout radius of a to-be-solved parameter, meanwhile, a measurement model containing relationships among the antenna layout radius of the to-be-solved parameter, a wall thickness and a wall dielectric constant is constructed based on a relationship between the history time difference value and an environment parameter, the position model and the measurement model are solved by adopting a Gauss-Newton algorithm, and after error compensation is performed on the radar position and the environment parameter according to estimated values of the antenna layout radius of the to-be-solved parameter, the wall thickness and the wall dielectric constant obtained by solving, the target is imaged. The target imaging result obtained by adopting the method can clearly and accurately reflect target information or information in the wall.

Description

Technical Field

[0001] This application relates to the fields of radar signal processing and imaging technology, and in particular to a penetrating radar imaging method, apparatus, device, and medium with dual error synchronous compensation. Background Technology

[0002] Penetrating radar, due to the low-frequency characteristics of its electromagnetic waves, can penetrate materials such as wood and brick walls to locate targets behind them and image the environment behind them, thus it is widely used. To simultaneously achieve good penetration performance and high resolution, low-frequency ultra-wideband multiple-input multiple-output (MIMO) radar is typically chosen for penetration imaging. MIMO radar offers advantages such as high repeatability, dense sampling, safety and reliability, flexible operation, and good cost-effectiveness. For enclosed building spaces, deploying MIMO radar in a circular pattern around the building's perimeter can comprehensively acquire information about the building's internal structure and targets, greatly improving the ability to detect concealed spaces.

[0003] The most significant difference between penetrating radar and traditional radar is that the refraction effect of walls causes the propagation path of electromagnetic waves to be not a simple straight line, but a broken line related to environmental parameters and antenna position. Therefore, errors in environmental parameters and antenna positioning caused by motion errors during the imaging process can have a very serious impact on the imaging effect. To enable penetrating-wall imaging technology to adapt to various complex environments, it is usually necessary to consider and compensate for the influence of both environmental parameters and the radar's own position on image quality.

[0004] However, current technologies can only compensate for radar positioning errors or environmental parameter estimation errors individually. In practical applications, environmental parameters and radar positioning errors usually coexist and are coupled, jointly affecting the echo history and imaging results. If only one type of error is compensated, it will not only cause errors in the estimation of environmental or radar positioning parameters, but the imaging results will also fail to clearly and accurately reflect the internal information of buildings. Summary of the Invention

[0005] Therefore, it is necessary to provide a penetrating radar imaging method, device, equipment, and medium that can simultaneously compensate for radar self-positioning error and environmental parameter estimation error, addressing the aforementioned technical problems.

[0006] A penetrating radar imaging method with dual error synchronous compensation, the method comprising:

[0007] Acquire radar echo data of the target to be imaged, and generate a corresponding one-dimensional range image based on the radar echo data;

[0008] Based on the one-dimensional distance image, the echo history of the direct wave at the leading edge of the wall and the echo history of the direct wave at the trailing edge of the wall are extracted, and the time difference between the two is calculated.

[0009] Using the antenna deployment radius, wall thickness, and wall dielectric constant of the penetrating radar as unknown parameters, a position model representing the actual position of the penetrating radar is constructed based on the antenna deployment radius. Simultaneously, based on the relationship between the time difference of the history and environmental parameters, a measurement model including the antenna deployment radius, wall thickness, and wall dielectric constant is constructed. The position model and the measurement model are combined to obtain the model to be solved.

[0010] The model to be solved is solved using the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm to obtain the estimated values ​​of each of the unknown parameters.

[0011] After compensating for the positioning error and refraction effect based on the estimated values ​​of antenna deployment radius, wall thickness, and wall dielectric constant, the imaging result of the target is obtained.

[0012] In one embodiment, the penetrating radar is a MIMO radar.

[0013] In one embodiment, the location model is represented as:

[0014]

[0015] In the above formula, θ tm and θ rn Let x represent the circumferential angles of the m-th transmitting antenna and the n-th receiving antenna, respectively. tm y tm and x rn y rn Let r represent the coordinates of the m-th transmitting antenna and the n-th receiving antenna, respectively. c This indicates the antenna deployment radius of the penetrating radar.

[0016] In one embodiment, the measurement model is represented as:

[0017]

[0018] In the above formula, c represents the speed of light, and L... mn This indicates the distance between the transmitting and receiving antennas. R represents the tilt angle of the transmitting and receiving antennas relative to the wall. m ε represents the vertical distance between the m-th transmitting antenna and the wall, d represents the thickness of the wall, and ε represents the vertical distance between the m-th transmitting antenna and the wall. r This represents the dielectric constant of the wall. t represents the tilt angle of the transmitting and receiving antennas relative to the wall. d(m,n) represents the time difference of the process.

[0019] In one embodiment, solving the model to be solved based on the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm includes:

[0020] The estimation problem of solving for each unknown parameter in the model to be solved is transformed into a least squares problem. The solution is obtained by optimizing the solution using the Gauss-Newton algorithm based on the preset initial values ​​of each unknown parameter.

[0021] In one embodiment, noise is introduced into the model to be solved before solving the model.

[0022] This application also provides a penetrating radar imaging device with dual error synchronous compensation, the device comprising:

[0023] A one-dimensional range image generation module is used to acquire radar echo data of the target to be imaged and generate a corresponding one-dimensional range image based on the radar echo data.

[0024] The history time difference calculation module is used to extract the echo history of the direct wave at the leading edge of the wall and the echo history of the direct wave at the trailing edge of the wall based on the one-dimensional distance image, and to calculate the history time difference between the two.

[0025] The solution model construction module takes the antenna deployment radius, wall thickness, and wall dielectric constant of the penetrating radar as unknown parameters, and constructs a position model representing the actual position of the penetrating radar based on the antenna deployment radius. At the same time, based on the relationship between the time difference and environmental parameters, a measurement model including the antenna deployment radius, wall thickness, and wall dielectric constant is constructed. The position model and the measurement model are combined to obtain the model to be solved.

[0026] The unknown parameter solving module is used to solve the model to be solved based on the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm to obtain the estimated values ​​of each of the unknown parameters;

[0027] The error-compensated imaging module is used to compensate for the positioning error and refraction effect based on the estimated values ​​of the antenna deployment radius, wall thickness, and wall dielectric constant, respectively, and then obtain the imaging result of the target.

[0028] 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:

[0029] Acquire radar echo data of the target to be imaged, and generate a corresponding one-dimensional range image based on the radar echo data;

[0030] Based on the one-dimensional distance image, the echo history of the direct wave at the leading edge of the wall and the echo history of the direct wave at the trailing edge of the wall are extracted, and the time difference between the two is calculated.

[0031] Using the antenna deployment radius, wall thickness, and wall dielectric constant of the penetrating radar as unknown parameters, a position model representing the actual position of the penetrating radar is constructed based on the antenna deployment radius. Simultaneously, based on the relationship between the time difference of the history and environmental parameters, a measurement model including the antenna deployment radius, wall thickness, and wall dielectric constant is constructed. The position model and the measurement model are combined to obtain the model to be solved.

[0032] The model to be solved is solved using the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm to obtain the estimated values ​​of each of the unknown parameters.

[0033] After compensating for the positioning error and refraction effect based on the estimated values ​​of antenna deployment radius, wall thickness, and wall dielectric constant, the imaging result of the target is obtained.

[0034] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0035] Acquire radar echo data of the target to be imaged, and generate a corresponding one-dimensional range image based on the radar echo data;

[0036] Based on the one-dimensional distance image, the echo history of the direct wave at the leading edge of the wall and the echo history of the direct wave at the trailing edge of the wall are extracted, and the time difference between the two is calculated.

[0037] Using the antenna deployment radius, wall thickness, and wall dielectric constant of the penetrating radar as unknown parameters, a position model representing the actual position of the penetrating radar is constructed based on the antenna deployment radius. Simultaneously, based on the relationship between the time difference of the history and environmental parameters, a measurement model including the antenna deployment radius, wall thickness, and wall dielectric constant is constructed. The position model and the measurement model are combined to obtain the model to be solved.

[0038] The model to be solved is solved using the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm to obtain the estimated values ​​of each of the unknown parameters.

[0039] After compensating for the positioning error and refraction effect based on the estimated values ​​of antenna deployment radius, wall thickness, and wall dielectric constant, the imaging result of the target is obtained.

[0040] The aforementioned dual-error synchronous compensation method, apparatus, equipment, and medium for penetrating radar imaging extracts the echo history of the direct wave along the front and rear edges of the wall from a one-dimensional range image generated based on the target radar echo data, and calculates the time difference between the two histories. Then, a position model representing the actual position of the penetrating radar is constructed based on the antenna deployment radius, a parameter to be solved. Simultaneously, based on the relationship between the time difference and environmental parameters, a measurement model is constructed that includes the antenna deployment radius, wall thickness, and wall dielectric constant, parameters to be solved. The position model and measurement model are solved using the Gauss-Newton algorithm. Based on the estimated values ​​of the antenna deployment radius, wall thickness, and wall dielectric constant obtained from the solution, errors are compensated for in the radar position and environmental parameters before imagery of the target is performed. The target imaging results obtained using this method can clearly and accurately reflect target information or information within the wall. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating a penetrating radar imaging method with dual error synchronous compensation in one embodiment.

[0042] Figure 2 This is a schematic diagram of the wall refraction effect in one embodiment;

[0043] Figure 3 This is a schematic diagram of an experimental scenario in a simulation experiment;

[0044] Figure 4 This is a schematic diagram showing the results of each optimization step during the process of solving for unknown parameters in a simulation experiment.

[0045] Figure 5 This is a schematic diagram comparing the imaging results before and after optimization using this method in a simulation experiment;

[0046] Figure 6 This is a structural block diagram of a penetrating radar imaging device with dual error synchronous compensation in one embodiment;

[0047] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] 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.

[0049] Existing penetrating radar imaging methods can only compensate for either the radar's own positioning error or the environmental parameter estimation error. However, in practical applications, both errors coexist, causing existing imaging methods to fail to clearly and accurately reflect the internal information of buildings. In this embodiment, as... Figure 1 As shown, a penetrating radar imaging method with dual error synchronous compensation is provided, including the following steps:

[0050] Step S100: Obtain radar echo data of the target to be imaged, and generate a corresponding one-dimensional range image based on the radar echo data.

[0051] Step S110: Extract the echo history of the direct wave at the leading edge of the wall and the echo history of the direct wave at the trailing edge of the wall based on the one-dimensional distance image, and calculate the time difference between the two.

[0052] In step S120, the antenna deployment radius, wall thickness, and wall dielectric constant of the penetrating radar are used as unknown parameters. A position model representing the actual position of the penetrating radar is constructed based on the antenna deployment radius. At the same time, based on the relationship between the time difference and environmental parameters, a measurement model including the antenna deployment radius, wall thickness, and wall dielectric constant is constructed. The position model and the measurement model are combined to obtain the model to be solved.

[0053] Step S130: Solve the model to be solved based on the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm to obtain the estimated values ​​of each unknown parameter.

[0054] Step S140: After compensating for the positioning error and refraction effect based on the estimated values ​​of antenna deployment radius, wall thickness and wall dielectric constant, the imaging result of the target is obtained.

[0055] In this embodiment, a synchronous compensation method for self-localization and refraction effects of penetrating radar based on echo history extraction is proposed. This method extracts the history delay difference from the echo history to simultaneously estimate the true position of the radar antenna and the wall parameter information, thereby compensating for positioning errors and refraction effects and obtaining better autofocusing imaging results.

[0056] In step S100, the target refers to an object located behind or inside a wall, or it can refer to a structure inside the wall, or it can refer to a space behind the wall.

[0057] In penetration imaging, the direct waves along the front and rear edges of the wall provide 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's direct wave.

[0058] Assuming there is no target between the radar and a section of wall, the direct wave from the wall is closest to the radar. Furthermore, the front and rear edges of the wall form two boundaries with different dielectric constants with the air, which are reflected in the echo, including two adjacent direct waves from the front and rear edges of the wall.

[0059] Specifically, the wall refraction effect, such as 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. T represents a transmitting antenna in a circular MIMO, and R represents a receiving antenna. The two antennas are at different vertical distances from the wall. L represents the tilt angle of the transmitting and receiving antenna relative to the wall. mn This indicates the distance between the transmitting and receiving antennas. Figure 2 The thick solid line represents the propagation path of the electromagnetic wave, which is reflected at the front edge of the wall and received by the receiving antenna; this is the direct wave at the front edge of the wall. The thin solid line represents the propagation path of the electromagnetic wave, which is reflected at the rear edge of the wall and received by the receiving antenna; this is the direct wave at the rear edge of the wall.

[0060] The time delay t detected by radar at different times using the leading and trailing edge direct waves. f (m,n) and t b The difference between (m,n) yields:

[0061] t d (m,n)=t b (m,n)-t f (m,n) (1)

[0062] In formula (1), t f (m,n) represents the time delay of the leading edge direct wave detected by the m-th transmitting antenna and the n-th receiving antenna. t b (m,n) represents the time delay of the trailing edge direct wave detected by the m-th transmitting antenna and the n-th receiving antenna.

[0063] Specifically, the time delay of both the leading edge direct wave and the trailing edge direct wave can be directly obtained on a one-dimensional range image.

[0064] In this embodiment, the penetrating radar is a millimeter-wave sparse multiple-input multiple-output synthetic aperture radar (MIMO) radar.

[0065] In one embodiment, the antenna for penetrating the radar is arranged in a circular pattern.

[0066] In step S120, the antenna deployment radius, wall thickness, and wall dielectric constant required for positioning error and environmental error compensation are taken as unknown parameters, which are the parameters that need to be solved later.

[0067] In this embodiment, a position model representing the actual location of the penetrating radar is constructed based on the antenna deployment radius. First, the position of the radar antenna is obtained based on the radius information of the circular MIMO:

[0068]

[0069] In formula (2), θ tm and θ rn Let x represent the circumferential angles of the m-th transmitting antenna and the n-th receiving antenna, respectively. tm y tm and x rn y rn Let r represent the coordinates of the m-th transmitting antenna and the n-th receiving antenna, respectively. c This represents the antenna deployment radius that penetrates the radar. Equation (2) is then rearranged as follows:

[0070] X TR =A TR R (3)

[0071] In this embodiment, the unknown environmental parameters are represented by the wall thickness and the wall dielectric constant. Based on the relationship between the direct wave delay difference along the front and rear edges of the wall and the environmental parameters, a measurement model is constructed, expressed as follows:

[0072]

[0073] In formula (4), c represents the speed of light, and L... mn This indicates the distance between the transmitting and receiving antennas. R represents the tilt angle of the transmitting and receiving antennas relative to the wall. m ε represents the vertical distance between the m-th transmitting antenna and the wall, d represents the wall thickness, and ε represents the wall thickness. r Indicates the dielectric constant of the wall. t represents the tilt angle of the transmitting and receiving antennas relative to the wall. d (m,n) represents the time difference of the process.

[0074] Furthermore, adopt The distance between the transmitting and receiving antennas is calculated using... The tilt angle of the transmitting and receiving antennas relative to the wall is calculated.

[0075] In this embodiment, formulas (3) and (4) represent the rational scenario. However, in practical applications, the obtained measurements are affected by various noises, and the models represented by formulas (3) and (4) cannot be as accurate as possible when noise is included. Therefore, we need to consider how to estimate the unknown parameters in data with introduced noise. After introducing the influence of noise, the equation, i.e., the model to be solved, can be uniformly written as:

[0076]

[0077] Given that the model represented by Equation (5) is known, estimating the optimal solution for the unknown variables is equivalent to finding the best solution for the unknown variables. 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:

[0078]

[0079] In this embodiment, The estimation problem is transformed into a least squares problem, which can be solved using the Gauss-Newton method.

[0080] Specifically, in solving equation (6) using the Gauss-Newton method... When solving the problem, the initial values ​​of each unknown parameter are preset. After substituting the preset initial values ​​of each position parameter into formula (6), the Gauss-Newton method is used to continuously optimize each unknown parameter based on the preset initial values ​​until the estimated values ​​of each unknown parameter are obtained.

[0081] In one embodiment, the initial values ​​of each unknown parameter are set to parameters within a reasonable range. For example, the wall thickness is set to 10, the wall dielectric constant is set to 10, and the antenna deployment radius is set to 5.

[0082] Finally, in step S140, the radar position is corrected using the optimized radar position parameters, and the refraction effect is compensated for by imaging based on the optimized wall parameters.

[0083] In this paper, we also construct an experimental scenario and conduct simulation experiments based on the method described in this paper, and demonstrate the effectiveness of the proposed method through experimental results.

[0084] like Figure 3 The diagram shows a simulation scenario using this method. A total of 36 transmitting antennas and 180 receiving antennas are evenly distributed on a circle with a radius of 4 meters. The X-axis represents the east-west direction, and the Y-axis represents the north-south direction. The gray rectangles represent clay brick walls with a thickness of 0.24 meters and a dielectric constant of 5. A concrete load-bearing column in the lower right corner of the building can be considered the target.

[0085] like Figure 4 The diagram shows the optimization process for simulation experiments using this method. Among them, Figure 4 The horizontal axis of (a) and (b) represents the number of iterations, and the vertical axis represents the wall thickness estimation error (in meters) and the wall dielectric constant estimation error, respectively. Figure 4(c) The horizontal axis represents the number of iterations, and the vertical axis represents the increment size during the optimization process. For example... Figure 4 As shown, after 15 iterations, the estimation error of the wall thickness decreased from 10 to approximately 0.08, the estimation error of the wall dielectric constant decreased from 5 to approximately 0, and the parameter increment decreased from 5 to 4 × 10⁻⁶. -7 This indicates that the algorithm can converge to a stable value. Table 1 shows the specific results of the actual experiments of this invention in detail.

[0086] Table 1. Estimated results from the actual experiment.

[0087]

[0088] The signal has a center frequency of 2.5 GHz, a bandwidth of 1 GHz, and a pulse repetition frequency of 20 MHz. Assuming the radius parameters of the deployed circular MIMO antenna are unknown or contain errors, and that the thickness and dielectric constant of the walls within the building are also unknown, the imaging results will suffer from severe defocusing, making it impossible to detect information inside the building. The method proposed in this invention can estimate and compensate for motion errors caused by the radar's own positioning system and refraction errors caused by unknown environmental parameters, and self-focus to generate imaging results with better focusing effects.

[0089] like Figure 5 As shown, this is a comparison of the imaging results before and after optimization in a field experiment using this method. Figure 5 (a) represents the unoptimized coarse imaging result. Figure 5 (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 compensated target—the lower and right edges of the concrete load-bearing columns inside the building—can be focused, and the dihedrals formed by the corners of the building's internal walls can be clearly distinguished, resulting in a significant improvement in imaging quality. This demonstrates that this method can simultaneously compensate for both the radar's own positional error and wall parameter errors, improving the imaging quality of penetrating radar in practical applications.

[0090] In the aforementioned dual-error synchronous compensation method for penetrating radar imaging, the echo history of the direct wave along the front and rear edges of the wall is extracted from a one-dimensional range image generated based on the target radar echo data, and the time difference between the two histories is calculated. Then, a position model representing the actual position of the penetrating radar is constructed based on the antenna deployment radius, a parameter to be solved. Simultaneously, based on the relationship between the time difference and environmental parameters, a measurement model is constructed that includes the antenna deployment radius, wall thickness, and wall dielectric constant, parameters to be solved. The position model and measurement model are solved using the Gauss-Newton algorithm. Based on the estimated values ​​of the antenna deployment radius, wall thickness, and wall dielectric constant obtained from the solution, errors are compensated for in the radar position and environmental parameters before image acquisition of the target. This method can resolve the coupling between the radar's own positioning error and wall parameters in the echo history. Compared with traditional autofocus methods, it considers the influence of both types of errors simultaneously, making it more suitable for penetrating radar, especially for vehicle-mounted and airborne penetrating radar applications in complex environments. Meanwhile, this method does not rely on radio navigation positioning systems such as the Global Positioning System (GPS), and can significantly reduce the radar's own position error when the motion model is correct. The imaging results compensated by the error compensation method proposed in this invention are not affected by radar motion errors and can clearly reflect the external structure of buildings and the distribution of internal targets.

[0091] 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.

[0092] In one embodiment, such as Figure 6 As shown, a penetrating radar imaging device with dual error synchronous compensation is provided, including: a one-dimensional range image generation module 200, a history time difference calculation module 210, a solution model construction module 220, an unknown parameter solution module 230, and an error compensation imaging module 240, wherein:

[0093] The one-dimensional range image generation module 200 is used to acquire radar echo data of the target to be imaged and generate a corresponding one-dimensional range image based on the radar echo data.

[0094] The history time difference calculation module 210 is used to extract the echo history of the direct wave at the leading edge of the wall and the echo history of the direct wave at the trailing edge of the wall based on the one-dimensional distance image, and to calculate the history time difference between the two.

[0095] The solution model construction module 220 takes the antenna deployment radius, wall thickness, and wall dielectric constant of the penetrating radar as unknown parameters, and constructs a position model representing the actual position of the penetrating radar based on the antenna deployment radius. At the same time, based on the relationship between the time difference and environmental parameters, it constructs a measurement model that includes the relationship between the antenna deployment radius, wall thickness, and wall dielectric constant. The solution model is obtained by combining the position model and the measurement model.

[0096] The unknown parameter solving module 230 is used to solve the model to be solved based on the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm to obtain the estimated values ​​of each of the unknown parameters.

[0097] The error compensation imaging module 240 is used to compensate for the positioning error and refraction effect based on the estimated values ​​of the antenna deployment radius, wall thickness and wall dielectric constant, respectively, and then obtain the imaging result of the target.

[0098] Specific limitations regarding the dual-error synchronous compensation penetrating radar imaging device can be found in the limitations of the dual-error synchronous compensation penetrating radar imaging method described above, and will not be repeated here. Each module in the aforementioned dual-error synchronous compensation penetrating radar 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 operations corresponding to each module.

[0099] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7As 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 penetrating radar imaging method with dual-error synchronous compensation. 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.

[0100] Those skilled in the art will understand that Figure 7 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.

[0101] 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:

[0102] Acquire radar echo data of the target to be imaged, and generate a corresponding one-dimensional range image based on the radar echo data;

[0103] Based on the one-dimensional distance image, the echo history of the direct wave at the leading edge of the wall and the echo history of the direct wave at the trailing edge of the wall are extracted, and the time difference between the two is calculated.

[0104] Using the antenna deployment radius, wall thickness, and wall dielectric constant of the penetrating radar as unknown parameters, a position model representing the actual position of the penetrating radar is constructed based on the antenna deployment radius. Simultaneously, based on the relationship between the time difference of the history and environmental parameters, a measurement model including the antenna deployment radius, wall thickness, and wall dielectric constant is constructed. The position model and the measurement model are combined to obtain the model to be solved.

[0105] The model to be solved is solved using the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm to obtain the estimated values ​​of each of the unknown parameters.

[0106] After compensating for the positioning error and refraction effect based on the estimated values ​​of antenna deployment radius, wall thickness, and wall dielectric constant, the imaging result of the target is obtained.

[0107] 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:

[0108] Acquire radar echo data of the target to be imaged, and generate a corresponding one-dimensional range image based on the radar echo data;

[0109] Based on the one-dimensional distance image, the echo history of the direct wave at the leading edge of the wall and the echo history of the direct wave at the trailing edge of the wall are extracted, and the time difference between the two is calculated.

[0110] Using the antenna deployment radius, wall thickness, and wall dielectric constant of the penetrating radar as unknown parameters, a position model representing the actual position of the penetrating radar is constructed based on the antenna deployment radius. Simultaneously, based on the relationship between the time difference of the history and environmental parameters, a measurement model including the antenna deployment radius, wall thickness, and wall dielectric constant is constructed. The position model and the measurement model are combined to obtain the model to be solved.

[0111] The model to be solved is solved using the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm to obtain the estimated values ​​of each of the unknown parameters.

[0112] After compensating for the positioning error and refraction effect based on the estimated values ​​of antenna deployment radius, wall thickness, and wall dielectric constant, the imaging result of the target is obtained.

[0113] 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.

[0114] 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.

[0115] 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 penetrating radar imaging method with dual error synchronous compensation, characterized in that, The method includes: Acquire radar echo data of the target to be imaged, and generate a corresponding one-dimensional range image based on the radar echo data; Based on the one-dimensional distance image, the echo history of the direct wave at the leading edge of the wall and the echo history of the direct wave at the trailing edge of the wall are extracted, and the difference in the history delay between the two is calculated. Using the antenna deployment radius of the penetrating radar, the wall thickness, and the wall dielectric constant as unknown parameters, a location model representing the actual location of the penetrating radar is constructed based on the antenna deployment radius. The location model is expressed as follows: In the above formula, and They represent the first m The first transmitting antenna and the first n The circumference of each receiving antenna, , and , They represent the first m The first transmitting antenna and the first n The coordinates of each receiving antenna, This indicates the antenna deployment radius of the penetrating radar; Simultaneously, based on the relationship between the process delay difference and environmental parameters, a measurement model is constructed that includes the relationship between wall thickness and wall dielectric constant, wherein the measurement model is expressed as: In the above formula, Represents the speed of light. This indicates the distance between the transmitting and receiving antennas. This indicates the tilt angle of the transmitting and receiving antennas relative to the wall. Indicates the first m The vertical distance between the transmitting antenna and the wall. This indicates the thickness of the wall. This represents the dielectric constant of the wall. This indicates the tilt angle of the transmitting and receiving antennas relative to the wall. This represents the difference in process delay; The model to be solved is obtained by combining the location model and the measurement model. The model to be solved is solved using the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm to obtain the estimated values ​​of each of the unknown parameters. After compensating for the positioning error and refraction effect based on the estimated values ​​of antenna deployment radius, wall thickness, and wall dielectric constant, the imaging result of the target is obtained.

2. The penetrating radar imaging method according to claim 1, characterized in that, The penetrating radar is a MIMO radar.

3. The penetrating radar imaging method according to claim 1, characterized in that, The step of solving the model to be solved based on the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm includes: The estimation problem of solving for each unknown parameter in the model to be solved is transformed into a least squares problem. The solution is obtained by optimizing the solution using the Gauss-Newton algorithm based on the preset initial values ​​of each unknown parameter.

4. The penetrating radar imaging method according to claim 3, characterized in that, Before solving the model to be solved, noise is introduced into the model to be solved.

5. A penetrating radar imaging device with dual error synchronous compensation, characterized in that, The device implements the penetrating radar imaging method with dual error synchronous compensation as described in any one of claims 1-4, including: A one-dimensional range image generation module is used to acquire radar echo data of the target to be imaged and generate a corresponding one-dimensional range image based on the radar echo data. The history delay difference calculation module is used to extract the echo history of the direct wave at the leading edge of the wall and the echo history of the direct wave at the trailing edge of the wall based on the one-dimensional distance image, and to calculate the history delay difference between the two. The solution model construction module takes the antenna deployment radius, wall thickness, and wall dielectric constant of the penetrating radar as unknown parameters, and constructs a position model representing the actual position of the penetrating radar based on the antenna deployment radius. At the same time, based on the relationship between the history delay difference and environmental parameters, a measurement model including the antenna deployment radius, wall thickness, and wall dielectric constant is constructed. The position model and the measurement model are combined to obtain the model to be solved. The unknown parameter solving module is used to solve the model to be solved based on the preset initial values ​​of the unknown parameters and the Gauss-Newton algorithm to obtain the estimated values ​​of each of the unknown parameters; The error-compensated imaging module is used to compensate for the positioning error and refraction effect based on the estimated values ​​of the antenna deployment radius, wall thickness, and wall dielectric constant, respectively, and then obtain the imaging result of the target.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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