Structural perception based through-probing radar target detection method and system

Abstract

The application provides a structure perception-based penetrating detection radar target detection method and system, which comprises: an interface position estimation stage, wherein according to three-dimensional detection data acquired by a through-wall life detection radar, the spatial position information of at least one physical structure interface in a detection space is automatically estimated and determined; a layered target detection stage, wherein according to the spatial position information of the physical structure interface, the three-dimensional detection data is adaptively divided into multiple independent sub-data blocks corresponding to different longitudinal physical spaces along the distance dimension; and target detection processing is respectively performed in each independent sub-data block to obtain multi-target detection results distinguishing different depth positions. The application effectively solves the technical problem of confusion and ambiguity of different depth targets in a longitudinal detection scene of a through-wall life detection radar, and significantly improves the multi-target layered recognition capability.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and in particular to a method and system for penetrating radar target detection based on structure perception. Background Technology

[0002] Through-wall life detection radar technology has significant application value in disaster relief, law enforcement, and military reconnaissance due to its ability to penetrate common non-metallic obstacles (such as walls, floors, and ruins) to detect concealed targets. This technology achieves imaging and target detection in concealed environments by emitting electromagnetic waves and receiving echo signals from behind obstacles.

[0003] However, existing through-wall radar target detection methods typically assume the detection area is a single, homogeneous medium. Such methods do not adequately consider the impact of multi-layered structures (such as floors, load-bearing walls, and gaps between layers in ruins) commonly found in real-world detection scenarios on electromagnetic wave propagation characteristics. In practical applications, electromagnetic waves undergo multiple reflections when penetrating multi-layered media. Strong reflection clutter generated at structural interfaces severely interferes with signals from nearby weak life-bearing targets, leading to the following technical problems: 1. Strong clutter masking problem: Strong reflected echoes generated by structural interfaces form high-energy peaks on the range image, which can easily mask weak target signals located near or behind the same range cell, leading to missed detection.

[0004] 2. Problem of ambiguous depth positioning: Existing methods have difficulty distinguishing multiple targets located at different longitudinal depths (e.g., different floors or different burial depths of ruins). Due to the lack of prior knowledge of the spatial structure interface, the echoes of targets at different depths are coupled and superimposed in the distance dimension, resulting in defects such as ambiguous positioning information, confusion of target numbers, and increased false alarm rate in the detection results.

[0005] Therefore, traditional processing models for single uniform spaces are no longer sufficient to meet the high-precision detection requirements in complex scenarios involving multiple obstacles. Summary of the Invention

[0006] This invention addresses the technical problems existing in the prior art by providing a structure-aware penetrating radar target detection method and system. By automatically estimating the location of the main physical interfaces inside the obstacle and adaptively segmenting the detection space based on the prior information of the structure, fine target detection is then performed in each independent sub-region, thereby significantly improving the multi-target resolution capability in complex multi-layered scenes.

[0007] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows: A structure-aware penetration radar target detection method includes: In the interface location estimation stage, based on the three-dimensional detection data obtained by the through-wall life detection radar, the spatial location information of at least one physical structure interface inside the detection space is automatically estimated and determined. In the layered target detection stage, based on the spatial location information of the physical structure interface, the three-dimensional detection data is adaptively divided into multiple independent sub-data blocks corresponding to different longitudinal physical spaces along the distance dimension; target detection processing is performed in each of the independent sub-data blocks to obtain multi-target detection results that distinguish different depth positions.

[0008] As a further improvement to the method of the present invention, the interface position estimation stage further includes: The three-dimensional detection data is incoherently accumulated along the azimuth and elevation directions and compressed to form a one-dimensional range image feature curve; Peak detection processing is performed on the one-dimensional distance image feature curve to extract the maximum point of the one-dimensional distance image feature curve to determine the distance cell position of the physical structure interface.

[0009] As a further improvement to the method of the present invention, the peak detection processing adopts a spectral peak search algorithm or a one-dimensional constant false alarm rate detection algorithm, and eliminates false peak points by setting a minimum peak height threshold and a minimum peak spacing constraint.

[0010] As a further improvement to the method of the present invention, in the layered target detection stage, the three-dimensional detection data is adaptively divided into multiple independent sub-data blocks corresponding to different longitudinal physical spaces along the distance dimension, including: Based on the spatial location information of the physical structure interface, the apparent distance of the physical structure interface in the radar line of sight is obtained. For each angle unit determined by the azimuth and elevation angles, the theoretical projection distance of the physical structure interface in the corresponding direction of each angle unit is calculated based on the apparent distance and the electromagnetic wave slant range propagation geometric model. Based on the theoretical projection distance corresponding to each angle unit, a dynamic segmentation interface lookup table is constructed; The three-dimensional detection data is segmented along the distance dimension by the dynamic segmentation interface lookup table, so that echo data that are on the same physical plane but at different detection angles are divided into the same independent sub-data block.

[0011] As a further improvement to the method of the present invention, the theoretical projection distance is calculated using the following formula:

[0012] in, Azimuth and pitch angle The corresponding theoretical projection distance, Indicates the first The apparent distance of each structural interface. , This represents the estimated total number of physical structure interfaces.

[0013] As a further improvement to the method of the present invention, the method further includes converting the theoretical projected distance into a distance cell index using the following formula:

[0014] in, For distance cell index, For distance resolution, This is a function for downward adjustment.

[0015] As a further improvement to the method of the present invention, the hierarchical target detection stage performs target detection processing separately within each independent sub-data block, including: Each independent sub-data block is subjected to distance-dimensional energy compression to generate corresponding layered two-dimensional azimuth-elevation plane images; The constant false alarm rate (CFAR) detection algorithm is applied independently to each of the two-dimensional azimuth-elevation plane images to obtain the target detection results in each longitudinal physical structure space.

[0016] As a further improvement to the method of the present invention, the three-dimensional detection data is the original echo data cube, or the strong reflector imaging data cube processed by the back projection imaging algorithm or the digital beamforming algorithm.

[0017] The present invention also provides a structure-aware penetrating radar target detection system, comprising a microprocessor and a memory interconnected thereto, wherein the microprocessor is programmed or configured to execute the structure-aware penetrating radar target detection method.

[0018] The present invention also provides a computer-readable storage medium storing a computer program / instructions that are programmed or configured to execute the structure-aware penetration radar target detection method by a processor.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention employs a two-stage processing framework of "structural estimation first, then zone detection." By automatically estimating and determining the spatial location information of the physical structure interfaces within the detection space, it provides crucial prior knowledge of the physical scene for subsequent target detection. Compared to traditional methods that treat the detection area as a single uniform space, this invention can effectively perceive the spatial distribution characteristics of internal structures such as walls and floors.

[0020] 2. Based on the estimated physical structure interface location information, this invention adaptively divides the 3D detection data into multiple independent sub-data blocks corresponding to different longitudinal physical spaces along the distance dimension, and performs target detection processing within each sub-data block. This hierarchical processing mechanism effectively isolates the mutual coupling and interference of target echo signals at different depths, solving the technical problem of traditional methods' difficulty in distinguishing targets located at different longitudinal depths, which leads to blurred positioning information. This significantly improves the multi-target resolution and detection accuracy in multi-layer obstacle scenarios.

[0021] 3. This invention adaptively segments the probe data based on the actual estimated physical structure interface location, rather than using a preset fixed distance threshold. This adaptive segmentation method ensures that the segmentation boundary is consistent with the structural distribution in the actual physical scene, thus ensuring the physical rationality and scene adaptability of the layered processing. Attached Figure Description

[0022] Figure 1 This is a flowchart of the structure-aware penetration radar target detection method in an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of the two-dimensional CFAR target detection results for each floor in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0025] like Figure 1 As shown, this embodiment provides a structure-aware penetration radar target detection method, including: Step 1: Interface position estimation stage. Based on the three-dimensional detection data obtained by the through-wall life detection radar, the spatial position information of at least one physical structure interface inside the detection space is automatically estimated and determined. Step 2: In the layered target detection stage, based on the spatial location information of the physical structure interface, the 3D detection data is adaptively divided into multiple independent sub-data blocks corresponding to different longitudinal physical spaces along the distance dimension; target detection processing is performed in each independent sub-data block to obtain multi-target detection results that distinguish different depth positions.

[0026] To address the shortcomings of existing through-wall life detection radar target detection methods, such as difficulty in effectively distinguishing targets at different depths, susceptibility to ambiguous positioning information, and insufficient detection accuracy, this embodiment provides a robust and highly accurate multi-layer structure multi-target layered detection method, following the core logic of "first sensing the structure, then detecting by region." Specifically, it first automatically identifies and estimates the specific spatial location information of the main physical structural interfaces (such as floors, walls, and rubble layers) within the detection space from the imaging data of strong reflectors in the through-wall life detection radar. These physical structural interfaces refer to the interfaces formed between different media (such as air and concrete, brick, soil, etc.) or different building layers (such as floors, walls, and rubble accumulation layers) in the detection scene, possessing continuous geometrical extension and significant electromagnetic wave reflection characteristics. Its typical characteristics are the generation of a stable, broad-peaked energy distribution in the radar echo; then, based on the position of the structural interface, the three-dimensional detection data space is adaptively divided into different longitudinal hierarchical units along the distance dimension, so that each longitudinal hierarchical unit corresponds to an independent sub-data block in the longitudinal physical space; finally, target detection processing is carried out independently in each hierarchical unit, thereby effectively solving the problem of target confusion at different depths and improving the accuracy and reliability of target detection.

[0027] In this embodiment, the three-dimensional detection data is either the original echo data cube or a strong reflector imaging data cube processed by a back projection imaging algorithm or a digital beamforming algorithm. The dimensions of the data cube include range × azimuth × pitch.

[0028] In this embodiment, the interface position estimation stage further includes: Step 101: Angular Domain Energy Compression: The three-dimensional detection data is compressed along the azimuth and elevation directions to form a one-dimensional range profile feature curve. This processing can effectively concentrate the scattered energy of the structural interface and highlight its distribution characteristics in the range dimension. The compression method can employ maximum value compression, summation compression, or mean value compression, with maximum value compression being preferred to enhance the scattering characteristics. To facilitate subsequent processing, the compressed one-dimensional range profile is normalized to its maximum and minimum values.

[0029] Step 102: Perform peak detection processing on the one-dimensional distance image feature curve, and extract the maximum point of the one-dimensional distance image feature curve to determine the location of the distance cell at the physical structure interface.

[0030] In this embodiment, the peak detection processing adopts a spectral peak search algorithm or a one-dimensional constant false alarm rate detection algorithm, and false peak points are eliminated by setting a minimum peak height threshold and a minimum peak spacing constraint.

[0031] Specifically, an extreme value analysis of the one-dimensional range image feature curve is performed using a spectral peak search algorithm to automatically filter out candidate range units representing structural interfaces. To improve the robustness of interface recognition, this embodiment introduces a dual constraint mechanism: on the one hand, a minimum peak height threshold (preferably 0.3) is set to filter out weak background clutter interference; on the other hand, a minimum peak spacing constraint (preferably 1 range unit) is set to merge adjacent sidelobe oscillations. The above processing effectively eliminates false spectral peaks, ensuring accurate estimation of the distance positions of major physical structural interfaces such as floors and walls. It is understood that in practical applications, those skilled in the art can adaptively adjust the above-mentioned preferred parameters according to factors such as radar system parameters, detection scene characteristics, and target type, which also fall within the protection scope of this invention.

[0032] It should be noted that, in addition to the peak search algorithm described above, this embodiment can also use a one-dimensional constant false alarm rate (CFAR) detection algorithm to automatically extract the interface position. However, given the advantages of the peak search algorithm in terms of its intuitive implementation and flexible parameter adjustment, it is considered the preferred embodiment of this invention.

[0033] In the layered target detection stage of this embodiment, the three-dimensional detection data is adaptively divided into multiple independent sub-data blocks corresponding to different longitudinal physical spaces along the distance dimension, including: Step 201: Based on the spatial location information of the physical structure interface, obtain the apparent distance of the physical structure interface in the radar line-of-sight direction. The apparent distance refers to the distance value corresponding to the one-way or two-way path length of the electromagnetic wave actually measured by the radar from its emission to its return to the receiving antenna after reflection from the target.

[0034] Step 202: For each angle unit determined by the azimuth and elevation angles, calculate the theoretical projection distance of the physical structure interface in the corresponding direction of each angle unit based on the apparent distance and the electromagnetic wave slant range propagation geometric model. Step 203: Construct a dynamic segmentation interface lookup table based on the theoretical projection distance corresponding to each angle unit; Step 204: The three-dimensional detection data is segmented along the distance dimension by looking up the table through the dynamic segmentation interface, so that the echo data that are on the same physical plane but at different detection angles are divided into the same independent sub-data block.

[0035] The following section provides a further explanation of the dynamic segmentation process described above, with a focus on specific implementation details.

[0036] In this embodiment, based on the estimated structural interface position, the original 3D imaging data cube is adaptively segmented along the distance dimension to form a series of sub-data blocks corresponding to different longitudinal layered spaces. The data cube segmentation does not employ a fixed distance threshold, but rather uses an electromagnetic wave geometric propagation model to dynamically calculate the corresponding layered interface distance unit for each azimuth-elevation angle unit; that is, a dynamic interface segmentation method based on geometric projection.

[0037] Specifically, based on the peak search results Apparent distance of a physical structure interface With known distance resolution (This range resolution is a theoretical value determined by the radar system bandwidth or by the imaging algorithm), calculate the first... The apparent distance of each structural interface Different angle units Theoretical projected distance : (1) in, Azimuth and pitch angle The corresponding theoretical projection distance, Indicates the first The apparent distance of each structural interface. , This represents the estimated total number of physical structure interfaces.

[0038] It should be noted that the applicable condition for the above formula (1) is that the physical structure interface is assumed to be a strong reflective surface that is relatively horizontal to the radar detection surface. Such interfaces can generate stable and energy-concentrated specular reflection echoes. Relatively inclined structural surfaces (such as collapsed inclined walls, stairs, and slopes) are not used as the main reference interface for estimation in this invention because their reflected echo energy is limited compared to the horizontal surface.

[0039] In this embodiment, the azimuth angle and pitch angle The reference coordinate system is defined as follows: a spatial rectangular coordinate system is established with the geometric center of the radar antenna array as the origin; azimuth angle Defined as the angle between the target's projection onto the horizontal plane and the radar normal, with clockwise being positive and counterclockwise being negative, and its value range being... 90° to 90°; pitch angle Defined as the angle between the line connecting the target and the origin and its projection onto the horizontal plane, positive for upward and negative for downward, with a range of values ​​of [value missing]. 90° to 90°.

[0040] After calculating the theoretical projection distance, the theoretical projection distance is converted into a distance cell index. This forms a dynamic segmentation interface lookup table. The formula for calculating the distance cell index is as follows: (2) in, For distance cell index, For distance resolution, This is a function for downward adjustment.

[0041] (3) The physical basis for using the floor function here is that if floor function or rounding were used, the resulting sub-data blocks might contain echo signals from targets on the lower interface. For example, suppose the actual distance between a certain structural interface and the radar is 3.5m, and the range resolution... If the distance is 1m, and rounded up, the distance cell index is 4. The corresponding segmentation boundary will be located behind the interface, causing the echo signal of the target on the lower interface to be incorrectly included in the target detection sub-data block, resulting in target energy confusion and thus causing target missed detection or location distortion. Therefore, this embodiment uses a rounding function to ensure that the segmentation boundary is located before the structural interface, so that the echo signal of the target on the lower interface is excluded from the target detection sub-data block, and only the potential target area behind the interface is included in the detection range, thereby ensuring the accuracy and reliability of layered target detection.

[0042] In actual segmentation, for each pair of angles The method determines the unique range-dimensional segmentation interval based on a dynamic segmentation interface lookup table, and then performs range-dimensional energy compression on the data within that interval to generate the final two-dimensional azimuth-elevation image. This method aligns the segmentation process with the physical propagation characteristics of electromagnetic waves, effectively overcoming the geometric distortion caused by slant-range projection.

[0043] The reason for the need for dynamic projection correction lies in the fact that electromagnetic waves, when incident on a horizontal structural interface at different angles, have different echo paths (apparent slant ranges). Fixed-distance segmentation methods ignore this physical fact, easily leading to incorrect segmentation of targets located on the same physical plane but at different angles. This method, through dynamic projection correction, ensures that targets on the same physical plane are accurately divided into the same data sub-blocks, greatly improving the physical accuracy and reliability of segmentation.

[0044] In the hierarchical target detection stage of this embodiment, target detection processing is performed separately within each independent sub-data block, including: Step 211: Perform maximum range dimension energy compression on each independent sub-data block, and converge the energy of the sub-data block in the range dimension to the azimuth-elevation plane to generate a two-dimensional azimuth-elevation plane image that reflects the target scattering intensity distribution within the layer.

[0045] Step 212: Apply the constant false alarm rate detection algorithm independently to each two-dimensional azimuth-elevation plane image to obtain the target detection results in each longitudinal physical structure space.

[0046] The two-dimensional constant false alarm rate (CFAR) detection algorithm achieves robust detection and point output of potential targets within a corresponding hierarchical space by adaptively setting a detection threshold, while maintaining a constant false alarm probability. The point output is in the form of three-dimensional spatial coordinates. All units are meters. The horizontal axis... x and ordinate y The angular coordinates of the target in the two-dimensional azimuth-elevation plane image The distance information corresponding to the hierarchical space in which the target is located is obtained through coordinate transformation; depth location It is determined based on the vertical physical space layer to which the target belongs.

[0047] In practical applications, the specific type of constant false alarm rate (CFAR) detection algorithm can be selected based on the specific scenario requirements. For example, in scenarios with uniform background clutter, cell-average CFAR detection (CA-CFAR) can be used to maximize detection sensitivity, while in scenarios with dense multi-target activity, ordered statistical CFAR detection (OS-CFAR) can be used to enhance the ability to resist interference from nearby targets. Furthermore, deep learning-based target detection algorithms (such as the YOLO series) are also suitable for this stage; the specific choice can be made based on a trade-off between system real-time requirements, computing platform architecture, and processing performance.

[0048] It should be noted that, since the aforementioned dynamic projection segmentation step has precisely divided the detection data into multiple independent sub-data blocks according to the physical structure interface, the clutter background in each layer space is isolated from each other. This allows the subsequent constant false alarm rate detection for each layer to be performed in a more uniform clutter background, effectively reducing cross-contamination from clutter in other layers, thereby helping to achieve a higher detection probability and a lower false alarm probability.

[0049] like Figure 2As shown, after dynamic projection segmentation, the original 3D detection data is divided into five independent sub-data blocks corresponding to different physical floors (floors 1 to 5). Each sub-data block is compressed using the maximum energy value in the distance dimension to form a two-dimensional planar image with azimuth as the horizontal axis and elevation as the vertical axis. The grayscale value (or color depth) of each pixel in the image represents the target scattering energy amplitude in the corresponding azimuth-elevation angle unit within that layer—the higher the amplitude, the greater the probability of a strong scattering target existing at that location.

[0050] After independently executing the two-dimensional constant false alarm rate (CFAR) detection algorithm on each floor's two-dimensional image, potential targets within the space of each floor are automatically identified. Figure 2 It can be clearly seen that the detection results from layer 1 to layer 5 are arranged vertically, and the high-amplitude regions (i.e., the detected target points) in each layer are clearly distinguishable. Moreover, the target signals between different layers are independent and isolated from each other. This effectively avoids the problems of cross-layer target signal coupling, strong clutter masking weak targets, and blurred target localization at different depths caused by the traditional single uniform spatial processing method, which does not consider structural layering.

[0051] This schematic diagram visually verifies the technical effectiveness of the core concept of "structure estimation first, then partitioned detection" in this invention: by precisely dividing the detection space according to the physical structure interface, the multi-layered target echoes that originally overlapped in the range dimension are decoupled to different independent processing channels, ultimately achieving clear layered identification of multiple targets at different depths. This embodiment also provides a structure-aware penetrating radar target detection system, including an interconnected microprocessor and memory. The microprocessor is programmed or configured to execute a structure-aware penetrating radar target detection method.

[0052] This embodiment also provides a computer-readable storage medium storing a computer program / instructions that are programmed or configured to execute a structure-aware penetration radar target detection method via a processor.

[0053] Those skilled in the art will understand that the above embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A target detection method for penetration radar based on structure perception, characterized in that, include: In the interface location estimation stage, based on the three-dimensional detection data obtained by the through-wall life detection radar, the spatial location information of at least one physical structure interface inside the detection space is automatically estimated and determined. In the layered target detection stage, based on the spatial location information of the physical structure interface, the three-dimensional detection data is adaptively divided into multiple independent sub-data blocks corresponding to different longitudinal physical spaces along the distance dimension; target detection processing is performed in each of the independent sub-data blocks to obtain multi-target detection results that distinguish different depth positions.

2. The structure-aware penetration radar target detection method according to claim 1, characterized in that, The interface position estimation stage also includes: The three-dimensional detection data is incoherently accumulated along the azimuth and elevation directions and compressed to form a one-dimensional range image feature curve; Peak detection processing is performed on the one-dimensional distance image feature curve to extract the maximum point of the one-dimensional distance image feature curve to determine the distance cell position of the physical structure interface.

3. The structure-aware penetration radar target detection method according to claim 2, characterized in that, The peak detection processing employs a spectral peak search algorithm or a one-dimensional constant false alarm rate detection algorithm, and eliminates false peak points by setting a minimum peak height threshold and a minimum peak spacing constraint.

4. The structure-aware penetration radar target detection method according to claim 2, characterized in that, In the layered target detection stage, the three-dimensional detection data is adaptively divided along the distance dimension into multiple independent sub-data blocks corresponding to different longitudinal physical spaces, including: Based on the spatial location information of the physical structure interface, the apparent distance of the physical structure interface in the radar line of sight is obtained. For each angle unit determined by the azimuth and elevation angles, the theoretical projection distance of the physical structure interface in the corresponding direction of each angle unit is calculated based on the apparent distance and the electromagnetic wave slant range propagation geometric model. Based on the theoretical projection distance corresponding to each angle unit, a dynamic segmentation interface lookup table is constructed; The three-dimensional detection data is segmented along the distance dimension by the dynamic segmentation interface lookup table, so that echo data that are on the same physical plane but at different detection angles are divided into the same independent sub-data block.

5. The structure-aware penetration radar target detection method according to claim 4, characterized in that, The theoretical projection distance is calculated using the following formula: in, Azimuth and pitch angle The corresponding theoretical projection distance, Indicates the first The apparent distance of each structural interface. , This represents the estimated total number of physical structure interfaces.

6. The structure-aware penetration radar target detection method according to claim 5, characterized in that, This also includes converting the theoretical projected distance into a distance cell index using the following formula: in, For distance cell index, For distance resolution, This is a function for downward adjustment.

7. The structure-aware penetration radar target detection method according to claim 1, characterized in that, The hierarchical target detection stage performs target detection processing within each independent sub-data block, including: Each independent sub-data block is subjected to distance-dimensional energy compression to generate corresponding layered two-dimensional azimuth-elevation plane images; The constant false alarm rate (CFAR) detection algorithm is applied independently to each of the two-dimensional azimuth-elevation plane images to obtain the target detection results in each longitudinal physical structure space.

8. The structure-aware penetration radar target detection method according to claim 1, characterized in that, The three-dimensional detection data is either the original echo data cube or the strong reflector imaging data cube processed by a back projection imaging algorithm or a digital beamforming algorithm.

9. A structure-aware through-wall life detection radar target detection system, comprising a microprocessor and a memory interconnected, characterized in that, The microprocessor is programmed or configured to execute the structure-aware penetration radar target detection method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program / instructions, characterized in that, The computer program / instructions are programmed or configured to execute, via a processor, the structure-aware penetration radar target detection method according to any one of claims 1 to 8.

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