Single-channel moving target detection method and device for terahertz video SAR

Abstract

The application relates to a single-channel moving target detection method and device of a terahertz video SAR. The method comprises the following steps: using the symmetry of the Doppler spectrum of the stationary and moving targets in an imaging scene about a Doppler center zero point, dividing a range-compressed and migration-corrected radar signal after desquaring into left and right sub-apertures by taking the Doppler center as a boundary, obtaining a sub-aperture image through azimuth Fourier transform, matching, Gaussian smoothing, amplitude inversion and binary processing of the sub-aperture image, and obtaining positive and negative images through incoherent subtraction; dividing and recording marks through an open-close operation, circumscribing a region with two marks after synthesizing an image, and detecting the position and direction of the moving target according to the region position and the upper and lower relationship of the marks.

Description

Technical Field

[0001] This application relates to the field of synthetic aperture radar moving target detection technology, and in particular to a single-channel moving target detection method and apparatus for terahertz video SAR. Background Technology

[0002] Terahertz video SAR has the capability to image ground scenes around the clock and in all weather conditions. It also has the ability to detect and track moving targets on the ground. During imaging, when radar illuminates a moving target, the target's motion causes a Doppler shift in the imaged position. This results in a light spot forming at the azimuth shift point, while the target's true location, blocked by the radar's illumination, has low reflected energy, forming a shadow. Video SAR moving target detection methods are often based on the shadows or light spots of moving targets. However, shadow-based video SAR moving target detection suffers from several drawbacks. The formation of shadows is related to the size of the moving target, its motion state, the imaging scene, and surrounding ground clutter. Furthermore, the shadows cast by moving targets, as well as those cast by weak reflective areas (such as shallow water) and shadows cast by high-altitude targets blocking radar illumination (such as trees), are all caused by energy differences between the weak reflective area and its surroundings. Essentially, they lack intensity differences, making them prone to false alarms and missed detections.

[0003] In video SAR (especially terahertz video SAR) moving target detection scenarios, the core problems of existing technologies are concentrated in three major aspects: difficulty in target discrimination, incomplete noise suppression, and limited detection accuracy. Specifically, these problems can be summarized as: difficulty in distinguishing moving targets from interference objects, poor background modeling and differential denoising effects, a lot of residual noise after sub-aperture image cancellation, and the inherent limitations of traditional detection methods. Among these, traditional detection methods that rely on background modeling (such as background difference methods) are easily affected by factors such as slight disturbances in the scene, changes in illumination, and movement of fixed objects, leading to inaccurate background models and reduced detection accuracy. On the other hand, some methods (such as optical flow methods) have huge computational requirements, high hardware requirements, and are sensitive to background noise, making them prone to false detections. Summary of the Invention

[0004] Based on this, it is necessary to provide a single-channel moving target detection method and apparatus for terahertz video SAR that can solve the problem of noise residue in the image cancellation process caused by different radar illumination areas in sub-view images or sub-aperture images.

[0005] A single-channel moving target detection method for terahertz video SAR, the method comprising: The raw data is obtained by receiving radar echo signal data of the target area by terahertz video SAR through a single receiving channel, and the echo signal is obtained by preprocessing the raw data. The echo signal is subjected to range-to-Fourier transform to obtain a two-dimensional signal in the range frequency domain and azimuth time domain. Based on the two-dimensional signal, the Doppler phase zero point is shifted to the Doppler phase center. Then, the two-dimensional signal is divided into left and right sub-apertures with the Doppler phase center as the boundary, and the left and right sub-aperture images are obtained accordingly. The left and right sub-aperture images are registered using feature point pairs, and the amount of translation during registration is recorded. Gaussian smoothing is applied to the left and right sub-aperture images. The amplitude of the Gaussian smoothed images is inverted. Then, according to a preset threshold, the two inverted images are binarized to obtain the corresponding two binary images. Based on the registered translation amount, the two binary images are incoherently subtracted to obtain a canceled image; After performing forward and reverse decomposition on the canceled image, image opening and closing operations are performed on the obtained forward and reverse images respectively. Then, the DBSCAN clustering algorithm is used to divide the image after the opening and closing operations, and the division identifier is recorded at the same time. The divided positive and negative images are synthesized. In the synthesized image, the regions marked by the two types of positive and negative images are delineated. The shadow area and spot area of ​​the moving target are determined according to the relationship between the delineated area and the radar illumination area. At the same time, the direction of movement of the moving target is determined according to the vertical position relationship between the positive and negative images within the delineated area, so as to realize the detection of moving targets.

[0006] In one embodiment, preprocessing the raw data to obtain the echo signal includes: After performing motion compensation and distance compression on the raw data, the dechirp echo signal is obtained; The echo signal is obtained by performing residual video phase compensation and Keystone transform correction on the dechirp echo signal.

[0007] In one embodiment, the left and right sub-aperture images are registered using the SAR-SIFT algorithm.

[0008] In one embodiment, performing forward and reverse data decomposition on the canceled image includes: dividing the canceled image into data images greater than 0 and less than 0, wherein the data image greater than 0 is the forward image and the data image less than 0 is the reverse image.

[0009] In one embodiment, amplitude inversion of a Gaussian-smoothed image means changing the maximum amplitude of the image to the minimum amplitude, the minimum amplitude of the image to the maximum amplitude, and transforming other amplitudes proportionally.

[0010] In one embodiment, determining a moving target based on a demarcated area and a radar illumination area includes: If both the radar illumination area and other parallel radar illumination areas have delineated areas, then these two delineated areas are determined to be moving targets. After identifying the moving target, the area within the radar illumination zone is the shadow of the moving target, and the area parallel to it is the spot of light of the moving target.

[0011] In one embodiment, determining the shadow area and spot area of ​​the moving target based on the relationship between the demarcated area and the radar illumination area includes: If the defined area is located within the actual radar illumination area, then the area is determined to be the shadow area of ​​the moving target; If the defined area is located parallel to the radar illumination area but outside the actual radar illumination area, then the area is determined to be the spot area of ​​a moving target.

[0012] This application also provides a single-channel moving target detection device for terahertz video SAR, the device comprising: The raw data acquisition module is used to acquire raw data, which is radar echo signal data of the target area received by terahertz video SAR through a single receiving channel, and to preprocess the raw data to obtain the echo signal. The left and right sub-aperture image acquisition module is used to perform range-to-Fourier transform on the echo signal to obtain a two-dimensional signal in the range frequency domain and azimuth time domain. Based on the two-dimensional signal, the Doppler phase zero point is shifted to the Doppler phase center, and then the two-dimensional signal is divided into left and right sub-apertures with the Doppler phase center as the boundary, and the left and right sub-aperture images are obtained accordingly. The registration module is used to register the left and right sub-aperture images using feature point pairs and record the translation amount of the registration. The binary image acquisition module is used to perform Gaussian smoothing on the left and right sub-aperture images, invert the amplitude of the Gaussian smoothed images, and then perform binary processing on the two inverted images according to a preset threshold to obtain the corresponding two binary images. The cancel image acquisition module is used to perform incoherent subtraction of two binary images according to the registered translation amount to obtain a cancel image; The positive and negative image processing module is used to perform positive and negative data decomposition on the canceled image, perform image opening and closing operations on the obtained positive and negative images respectively, and then use the DBSCAN clustering algorithm to divide the image after the opening and closing operation, while recording the division identifier. The moving target detection module is used to synthesize the divided positive and negative images. In the synthesized image, the area marked by both positive and negative images is delineated, and the shadow area and spot area of ​​the moving target are determined according to the relationship between the delineated area and the radar illumination area. At the same time, the direction of movement of the moving target is determined according to the vertical position relationship between the positive and negative images within the delineated area, so as to realize the detection of moving targets.

[0013] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above-described terahertz video SAR single-channel moving target detection method.

[0014] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described terahertz video SAR single-channel moving target detection method.

[0015] The aforementioned terahertz video SAR single-channel moving target detection method and apparatus obtains a two-dimensional signal in the range-frequency domain and azimuth-time domain by performing a range-to-Fourier transform on the echo signal. Based on the two-dimensional signal, the Doppler phase zero point is shifted to the Doppler phase center, and then the two-dimensional signal is divided into left and right sub-apertures with the Doppler phase center as the boundary, resulting in two corresponding left and right sub-aperture images. The left and right sub-aperture images are registered using feature point pairs, and the shift amount of the registration is recorded. The left and right sub-aperture images are Gaussian smoothed, and the amplitude of the Gaussian smoothed images is inverted. Then, according to a preset threshold, the two amplitude-inverted images are binarized to obtain two corresponding binary images. Based on the registration translation, two binary images are incoherently subtracted to obtain a canceled image. After data decomposition into positive and negative values, image opening and closing operations are performed on the resulting positive and negative images. The images after opening and closing operations are then segmented using the DBSCAN clustering algorithm, with segmentation markers recorded. The segmented positive and negative images are then synthesized. Regions containing both positive and negative image markers are delineated in the synthesized image. The shadow and spot regions of moving targets are determined based on the relationship between the delineated regions and the radar illumination area. Furthermore, the vertical positional relationship between the positive and negative images within the delineated regions determines the direction of motion of the moving target, thus achieving moving target detection. This method can detect moving targets in complex imaging scenes, simultaneously determining the shadows and spots of moving targets, as well as their direction of motion. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the imaging geometry of a moving target in one embodiment; Figure 2 This is a flowchart illustrating a single-channel moving target detection method for terahertz video SAR in one embodiment. Figure 3 This is a schematic diagram illustrating the specific processing flow of a single-channel moving target detection method for terahertz video SAR in one embodiment; Figure 4 This is a schematic diagram of the left and right sub-aperture imaging results in an experiment, where, Figure 4 (a) shows a schematic diagram of the left aperture. Figure 4 (b) shows a schematic diagram of the right sub-aperture; Figure 5 This is a schematic diagram of the sub-aperture destructive results in an experiment; Figure 6 This is a schematic diagram of the composite data after clustering in an experiment. Figure 7 This is a schematic diagram of the labeled test results in an experiment; Figure 8 This is a structural block diagram of a single-channel moving target detection device for terahertz video SAR in one embodiment; Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0017] 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.

[0018] First, this article will introduce the relevant theoretical aspects.

[0019] like Figure 1 The diagram shown is a geometrical representation of an imaging motion target, where OA represents the flight direction of the aircraft and its velocity is... , At any given moment, the location of the carrier platform is A. M is the location of the moving target, and the target's velocity in the distance direction is... The acceleration in the distance direction is azimuth velocity is azimuth acceleration is The shortest distance from the moving target to the flight path is , The instantaneous slant range from the moving target to the carrier radar at any given time is ,Right now: (1) Considering that azimuth acceleration has little effect on the state of a moving target, it is ignored. For terms that are more than three times, only consider those that are three times or more. The quadratic term, then formula (1) can be expressed as: (2) In formula (2), The equivalent azimuth flight speed is related to the azimuth velocity of the moving target, the range acceleration, and the slant distance from the flight platform to the moving target.

[0020] The shortest distance from a stationary target S in the imaging scene to the flight path is... , The instantaneous slant range from the stationary target to the radar at any given time is ,Right now: (3) In deskewing mode, the radar's difference frequency output is: (4) In formula (4), , This represents the amplitude of the echo from the scattering point. Indicates direction and time. Indicates distance to time. Indicates the radar center frequency, Indicates frequency modulation. Indicates the distance to the pulse width. Indicates the distance from the point target to the radar. Indicates the reference distance. Represents the speed of light. .

[0021] Furthermore, performing a Fourier transform on the distance direction of formula (4), the expression in the difference frequency domain after deskewing is as follows: (5) In formula (5), This indicates the difference frequency.

[0022] Neglecting the constant term, the echo signal of point target S is: (6) Performing an azimuth-to-Fourier transform on formula (6), we obtain: (7) The azimuth modulation frequency of a stationary target can be deduced using formula (7). It can be represented as , The azimuth frequency of a stationary target.

[0023] Neglecting constant terms, the echo signal of the moving target M is: (8) Performing an azimuth-to-Fourier transform on formula (8) yields: (9) In formula (9), ,in, and Let represent frequency and wavelength respectively. Furthermore, the azimuth modulation frequency of the moving target can be deduced from formula (9). It can be represented as .

[0024] From the side view, it can be seen from formula (7) that the Doppler spectrum of the stationary target S is symmetrical about the Doppler null point. From formula (9), it can be seen that the Doppler spectrum of the moving target M has a frequency shift related to its motion state and is asymmetrical about the Doppler null point. Similar to the stationary target, the clutter spectrum of the stationary target is symmetrical about the Doppler null point. By dividing the radar signal into two sub-apertures with the Doppler null point as the boundary in the azimuth time domain, imaging them separately, and then performing incoherent image subtraction, the moving target can be preserved, clutter and noise can be suppressed, the signal-to-noise ratio can be improved, and the detection capability of the moving target can be enhanced.

[0025] Based on the above theories, such as Figure 2 The present invention proposes a single-channel moving target detection method for terahertz video SAR, which specifically includes the following steps: Step S100: Obtain raw data. The raw data is radar echo signal data of the target area received by terahertz video SAR through a single receiving channel, and the raw data is preprocessed to obtain the echo signal.

[0026] Step S110: Perform range-to-Fourier transform on the echo signal to obtain a two-dimensional signal in the range frequency domain and azimuth time domain. Based on the two-dimensional signal, shift the Doppler phase zero point to the Doppler phase center. Then, using the Doppler phase center as the boundary, divide the two-dimensional signal into left and right sub-apertures and obtain the corresponding left and right sub-aperture images.

[0027] Step S120: Register the left and right sub-aperture images using feature point pairs and record the translation amount of the registration.

[0028] Step S130: Gaussian smoothing is applied to the left and right sub-aperture images. The amplitude of the Gaussian smoothed images is inverted. Then, according to a preset threshold, the two inverted amplitude images are binarized to obtain the corresponding two binary images.

[0029] Step S140: Based on the registered translation amount, the two binary images are incoherently subtracted to obtain a canceled image.

[0030] Step S150: After performing forward and reverse decomposition on the canceled image, perform image opening and closing operations on the obtained forward and reverse images respectively, and then use the DBSCAN clustering algorithm to divide the image after the opening and closing operations, while recording the division identifier.

[0031] Step S160: The divided positive and negative images are synthesized. In the synthesized image, the area marked by the two types of positive and negative images is delineated. The shadow area and spot area of ​​the moving target are determined according to the relationship between the delineated area and the radar illumination area. At the same time, the movement direction of the moving target is determined according to the vertical position relationship between the positive and negative images in the delineated area, so as to realize the detection of the moving target.

[0032] The above method addresses the problem in existing technologies that assume the average energy of pixels in a static scene is equal, treat moving targets as only causing amplitude changes in the two sub-images of the pixel, and do not consider the influence of radar illumination on the sub-image pixels. As a result, in the detection of moving targets in terahertz video SAR, it is difficult to obtain a true background image, and after image cancellation, it is also difficult to obtain a clean cancellation image.

[0033] This method aims to overcome the inconsistency of radar illumination on the pixel amplitude of sub-view images. Based on the symmetry problem of moving targets and stationary scenes in the Doppler spectrum, a single-channel moving target detection method for terahertz video SAR is provided. Through processing from imaging to detection, the pixel amplitude of the stationary scene is canceled out. Through a series of image domain processing, the shadows and light spots of the moving target are detected, and the direction of motion of the moving target is determined.

[0034] In step S100, the preprocessing of the raw data to obtain the echo signal includes: performing motion compensation and distance compression on the raw data to obtain the dechirp echo signal, then performing residual video phase compensation on the dechirp echo signal, and correcting the distance movement using Keystone transform to obtain the echo signal.

[0035] In step S110, because single-channel echoes are susceptible to platform motion errors, it is necessary to shift the Doppler phase zero point to the true Doppler phase center using phase gradient autofocus (PGA) or peak detection methods to ensure symmetrical sub-aperture division and avoid image distortion. Then, using the Doppler phase center as the boundary, the two-dimensional signal is divided into left and right sub-apertures along the azimuth frequency domain direction. Azimuth Fourier transform and inverse range Fourier transform are performed on the two sub-apertures respectively to obtain focused left and right sub-aperture images, laying the foundation for subsequent detection of moving targets using differences in target Doppler characteristics.

[0036] In step S120, the left and right sub-aperture images are registered using the SAR-SIFT algorithm.

[0037] In step S140, inverting the amplitude of the Gaussian-smoothed image means changing the maximum amplitude of the image to the minimum amplitude, the minimum amplitude of the image to the maximum amplitude, and transforming other amplitudes proportionally.

[0038] In step S150, the forward and reverse data decomposition of the cancellation image includes: dividing the cancellation image into data images greater than 0 and less than 0, wherein the data images greater than 0 are forward images, and the data images less than 0 are reverse images. Simultaneously, the images after opening and closing operations are further divided using the DBSCAN clustering algorithm, and a partitioning identifier is used to clearly identify whether the partitioned data comes from a forward image or a reverse image.

[0039] In step S160, a moving target is determined based on the delineated area and the radar illumination area. This includes the radar illumination area and other radar illumination areas parallel to it. If both of these areas have delineated areas, then these two delineated areas are determined to be moving targets. After determining the moving target, the delineated area in the radar illumination area is the shadow of the moving target, and the delineated area parallel to it is the light spot of the moving target.

[0040] In this embodiment, determining the shadow area and spot area of ​​a moving target based on the relationship between the demarcated area and the radar illumination area includes: if the demarcated area is located within the actual radar illumination area, then the area is determined to be the shadow area of ​​the moving target. If the demarcated area is located in an area parallel to the radar illumination area but outside the actual radar illumination area, then the area is determined to be the spot area of ​​the moving target.

[0041] In this paper, the effectiveness of the proposed method is also demonstrated through experiments. The main parameters of the imaging scene are shown in Table 1.

[0042] Table 1. Main parameters of terahertz video SAR imaging scene

[0043] The experimental verification data comes from the terahertz video SAR data collected during a flight test at a certain location, as shown in Table 1. Figure 1 This is a geometric schematic diagram of the imaging of a moving target, which uses a frontal side-view imaging mode.

[0044] The procedure flow verified in this experiment follows... Figure 3 The process is shown below.

[0045] Figure 4In the experimental verification, the azimuth spectrum data was divided into left and right sub-apertures with the Doppler center null point as the boundary, and then the images were generated by azimuth-to-Fourier transform. The stationary scene in the images is basically in the same position, but the radar-illuminated areas of the left and right sub-images are different, the brightness of the same pixel areas in the left and right sub-images is different, the brightness variation is inconsistent, and the average energy of the left and right sub-images is different. Outside the radar-illuminated area on the left side of the image, the light spots of two moving targets can be clearly seen.

[0046] Figure 5 The result is the cancellation of the two sub-aperture images by subtracting the left aperture from the right sub-aperture. In the radar illumination area, regions marked S1, S2, and S3 represent the shadow areas of moving targets. These regions contain black and white spots, and the arrows indicate the direction of the moving target's movement. In S1, the white spot is at the bottom and the black spot is at the top, indicating the moving target is moving from top to bottom. In S2 and S3, the white spot is at the top and the black spot is at the bottom, indicating the moving target is moving from bottom to top. To the right of the radar illumination area, region L1 is marked. This region contains black and white spots, with the vertical position of the spots reversed compared to region S1. Region L1 is the illuminated area of ​​region S1, representing the image of the moving target. In L1, the white spot is at the top and the black spot is at the bottom, indicating the target is moving from top to bottom. To the left of the radar illumination area, regions L2 and L3 are marked with black and white spots. The vertical position of these spots is the opposite of that in regions S2 and S3. Regions L2 and L3 are the bright spots of regions S2 and S3, representing the image of a moving target. In L2 and L3, the white spots are at the bottom and the black spots are at the top, indicating that the target is moving from bottom to top. If the sub-aperture image cancellation is calculated by subtracting the right sub-aperture from the left sub-aperture, the vertical positions of the white and black spots in the shadow and bright spot areas will be reversed. Other stationary bright spots with higher energy or stationary dark areas with lower energy are not displayed through the processing of this invention.

[0047] Figure 6 The result of image synthesis after DBSCAN clustering partitioning, and... Figure 5 The results were consistent; both the shadow area and the spot area contained markers from both the front and back images. The positions of these markers in the shadow area and the spot area were used to determine the direction of motion of the moving target.

[0048] Figure 7 The detection results are labeled and mapped onto the imaging image. In the original image, L1, which is difficult to distinguish, can be detected as a light spot of a moving target after processing by this invention; S3, which is difficult to distinguish with the naked eye, can be detected as a shadow of a moving target after processing by this invention. That is, this method can not only detect the shadow and light spot of a moving target, but also determine the direction of movement of the moving target.

[0049] In the aforementioned single-channel moving target detection method for terahertz video SAR, based on the symmetry between the Doppler spectrum of stationary targets and the Doppler spectrum of moving targets about the Doppler center null point in the imaging scene, the de-skewed radar signal is subjected to range compression and range migration correction. The azimuth spectrum is divided into left and right sub-apertures with the Doppler center as the boundary. Sub-aperture images are obtained by azimuth Fourier transform. After image matching and Gaussian smoothing, the sub-aperture images are amplitude inverted and binary processed. The sub-aperture images are then incoherently subtracted. The image data after incoherent subtraction is divided into two parts: positive and negative, which are the positive and negative images, respectively. Opening and closing operations are performed on the positive and negative images. The DBSCAN algorithm is used to cluster the data in the positive and negative images after the opening and closing operations and record their labels. The clustered images are then synthesized. Regions with both positive and negative image labels in the synthesized image are delineated. Based on the location of the delineated region and the vertical positional relationship of the positive and negative image labels within the delineated region, the moving target is detected, and the position and direction of movement of the moving target are obtained.

[0050] In this method, the locations of shadows and light spots of moving targets can be accurately detected simultaneously based on cluster markers in the synthesized image. The direction of motion of the moving target is determined by the vertical relationship between the front and back image markers within the delineated area. This method offers distinct technical characteristics for moving target detection, allowing for intuitive judgment of the target's position and direction of motion from the image. This method can detect the position and direction of motion of a moving target using only one frame from a single channel of terahertz video SAR. It is theoretically simple, processing is not cumbersome, and the detection speed is fast, demonstrating good practicality and timeliness.

[0051] It should be understood that, although Figure 2 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.

[0052] In one embodiment, such as Figure 8As shown, a single-channel moving target detection device for terahertz video SAR is provided, comprising: a raw data acquisition module 200, a left and right sub-aperture image acquisition module 210, a registration module 220, a binary image acquisition module 230, a canceled image acquisition module 240, a front and back image processing module 250, and a moving target detection module 260, wherein: The raw data acquisition module 200 is used to acquire raw data, which is radar echo signal data of the target area received by terahertz video SAR through a single receiving channel, and to preprocess the raw data to obtain the echo signal. The left and right sub-aperture image acquisition module 210 is used to perform range-to-Fourier transform on the echo signal to obtain a two-dimensional signal in the range frequency domain and azimuth time domain. Based on the two-dimensional signal, the Doppler phase zero point is shifted to the Doppler phase center, and then the two-dimensional signal is divided into left and right sub-apertures with the Doppler phase center as the boundary, and the left and right sub-aperture images are obtained accordingly. The registration module 220 is used to register the left and right sub-aperture images using feature point pairs and record the translation amount of the registration. The binary image acquisition module 230 is used to perform Gaussian smoothing on the left and right sub-aperture images, invert the amplitude of the Gaussian smoothed images, and then perform binary processing on the two inverted images according to a preset threshold to obtain the corresponding two binary images. The cancel image acquisition module 240 is used to perform incoherent subtraction of two binary images according to the registered translation amount to obtain a cancel image; The positive and negative image processing module 250 is used to perform positive and negative data decomposition on the canceled image, perform image opening and closing operations on the obtained positive and negative images respectively, and then use the DBSCAN clustering algorithm to divide the image after the opening and closing operation, while recording the division identifier. The moving target detection module 260 is used to synthesize the divided positive and negative images. In the synthesized image, the area with both positive and negative image markers is delineated, and the shadow area and spot area of ​​the moving target are determined according to the relationship between the delineated area and the radar illumination area. At the same time, the direction of movement of the moving target is determined according to the vertical position relationship between the positive and negative images within the delineated area, so as to realize the detection of the moving target.

[0053] Specific limitations regarding the single-channel moving target detection device for terahertz video SAR can be found in the limitations of the single-channel moving target detection method for terahertz video SAR described above, and will not be repeated here. Each module in the aforementioned single-channel moving target detection device for terahertz video SAR 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.

[0054] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 9 As 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 in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a single-channel moving target detection method using terahertz video SAR. The display screen can be an LCD screen or an e-ink screen. 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.

[0055] Those skilled in the art will understand that Figure 9 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.

[0056] 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: The raw data is obtained by receiving radar echo signal data of the target area by terahertz video SAR through a single receiving channel, and the echo signal is obtained by preprocessing the raw data. The echo signal is subjected to range-to-Fourier transform to obtain a two-dimensional signal in the range frequency domain and azimuth time domain. Based on the two-dimensional signal, the Doppler phase zero point is shifted to the Doppler phase center. Then, the two-dimensional signal is divided into left and right sub-apertures with the Doppler phase center as the boundary, and the left and right sub-aperture images are obtained accordingly. The left and right sub-aperture images are registered using feature point pairs, and the amount of translation during registration is recorded. Gaussian smoothing is applied to the left and right sub-aperture images. The amplitude of the Gaussian smoothed images is inverted. Then, according to a preset threshold, the two inverted images are binarized to obtain the corresponding two binary images. Based on the registered translation amount, the two binary images are incoherently subtracted to obtain a canceled image; After performing forward and reverse decomposition on the canceled image, image opening and closing operations are performed on the obtained forward and reverse images respectively. Then, the DBSCAN clustering algorithm is used to divide the image after the opening and closing operations, and the division identifier is recorded at the same time. The divided positive and negative images are synthesized. In the synthesized image, the regions marked by the two types of positive and negative images are delineated. The shadow area and spot area of ​​the moving target are determined according to the relationship between the delineated area and the radar illumination area. At the same time, the direction of movement of the moving target is determined according to the vertical position relationship between the positive and negative images within the delineated area, so as to realize the detection of moving targets.

[0057] 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: The raw data is obtained by receiving radar echo signal data of the target area by terahertz video SAR through a single receiving channel, and the echo signal is obtained by preprocessing the raw data. The echo signal is subjected to range-to-Fourier transform to obtain a two-dimensional signal in the range frequency domain and azimuth time domain. Based on the two-dimensional signal, the Doppler phase zero point is shifted to the Doppler phase center. Then, the two-dimensional signal is divided into left and right sub-apertures with the Doppler phase center as the boundary, and the left and right sub-aperture images are obtained accordingly. The left and right sub-aperture images are registered using feature point pairs, and the amount of translation during registration is recorded. Gaussian smoothing is applied to the left and right sub-aperture images. The amplitude of the Gaussian smoothed images is inverted. Then, according to a preset threshold, the two inverted images are binarized to obtain the corresponding two binary images. Based on the registered translation amount, the two binary images are incoherently subtracted to obtain a canceled image; After performing forward and reverse decomposition on the canceled image, image opening and closing operations are performed on the obtained forward and reverse images respectively. Then, the DBSCAN clustering algorithm is used to divide the image after the opening and closing operations, and the division identifier is recorded at the same time. The divided positive and negative images are synthesized. In the synthesized image, the regions marked by the two types of positive and negative images are delineated. The shadow area and spot area of ​​the moving target are determined according to the relationship between the delineated area and the radar illumination area. At the same time, the direction of movement of the moving target is determined according to the vertical position relationship between the positive and negative images within the delineated area, so as to realize the detection of moving targets.

[0058] 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. When executed, the computer program 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 may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of 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.

[0059] 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.

[0060] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. 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 application should be determined by the appended claims.

Claims

1. A single-channel moving target detection method for terahertz video SAR, characterized in that, The method includes: The raw data is obtained by receiving radar echo signal data of the target area by terahertz video SAR through a single receiving channel, and the echo signal is obtained by preprocessing the raw data. The echo signal is subjected to range-to-Fourier transform to obtain a two-dimensional signal in the range frequency domain and azimuth time domain. Based on the two-dimensional signal, the Doppler phase zero point is shifted to the Doppler phase center. Then, the two-dimensional signal is divided into left and right sub-apertures with the Doppler phase center as the boundary, and the left and right sub-aperture images are obtained accordingly. The left and right sub-aperture images are registered using feature point pairs, and the amount of translation during registration is recorded. Gaussian smoothing is applied to the left and right sub-aperture images. The amplitude of the Gaussian smoothed images is inverted. Then, according to a preset threshold, the two inverted images are binarized to obtain the corresponding two binary images. Based on the registered translation amount, the two binary images are incoherently subtracted to obtain a canceled image; After performing forward and reverse decomposition on the canceled image, image opening and closing operations are performed on the obtained forward and reverse images respectively. Then, the DBSCAN clustering algorithm is used to divide the image after the opening and closing operations, and the division identifier is recorded at the same time. The divided positive and negative images are synthesized. In the synthesized image, the regions marked by the two types of positive and negative images are delineated. The shadow area and spot area of ​​the moving target are determined according to the relationship between the delineated area and the radar illumination area. At the same time, the direction of movement of the moving target is determined according to the vertical position relationship between the positive and negative images within the delineated area, so as to realize the detection of moving targets.

2. The single-channel moving target detection method of terahertz video SAR according to claim 1, characterized in that, Preprocessing the raw data to obtain the echo signal includes: After performing motion compensation and distance compression on the raw data, the dechirp echo signal is obtained; The echo signal is obtained by performing residual video phase compensation and Keystone transform correction on the dechirp echo signal.

3. The single-channel moving target detection method for terahertz video SAR according to claim 2, characterized in that, The two sub-aperture images were registered using the SAR-SIFT algorithm.

4. The single-channel moving target detection method for terahertz video SAR according to claim 3, characterized in that, The data forward and reverse decomposition of the canceled image includes: dividing the canceled image into data images greater than 0 and less than 0, wherein the data image greater than 0 is the forward image and the data image less than 0 is the reverse image.

5. The single-channel moving target detection method for terahertz video SAR according to claim 4, characterized in that, Amplitude inversion of a Gaussian-smoothed image means changing the maximum amplitude of the image to the minimum amplitude, the minimum amplitude to the maximum amplitude, and transforming other amplitudes proportionally.

6. The single-channel moving target detection method for terahertz video SAR according to any one of claims 1-5, characterized in that, Determining moving targets based on the demarcated area and the radar illumination area includes: If both the radar illumination area and other parallel radar illumination areas have delineated areas, then these two delineated areas are determined to be moving targets. After identifying the moving target, the area within the radar illumination zone is the shadow of the moving target, and the area parallel to it is the spot of light of the moving target.

7. The single-channel moving target detection method for terahertz video SAR according to claim 6, characterized in that, The process of determining the shadow and spot areas of a moving target based on the relationship between the demarcated area and the radar illumination area includes: If the delineated area is located within the actual radar illumination area, then the delineated area is determined to be the shadow area of ​​the moving target; If the defined area is located parallel to the radar illumination area but outside the actual radar illumination area, then this defined area is determined to be the spot area of ​​a moving target.

8. A single-channel moving target detection device for terahertz video SAR, characterized in that, The device includes: The raw data acquisition module is used to acquire raw data, which is radar echo signal data of the target area received by terahertz video SAR through a single receiving channel, and to preprocess the raw data to obtain the echo signal. The left and right sub-aperture image acquisition module is used to perform range-to-Fourier transform on the echo signal to obtain a two-dimensional signal in the range frequency domain and azimuth time domain. Based on the two-dimensional signal, the Doppler phase zero point is shifted to the Doppler phase center, and then the two-dimensional signal is divided into left and right sub-apertures with the Doppler phase center as the boundary, and the left and right sub-aperture images are obtained accordingly. The registration module is used to register the left and right sub-aperture images using feature point pairs and record the translation amount of the registration. The binary image acquisition module is used to perform Gaussian smoothing on the left and right sub-aperture images, invert the amplitude of the Gaussian smoothed images, and then perform binary processing on the two inverted images according to a preset threshold to obtain the corresponding two binary images. The cancel image acquisition module is used to perform incoherent subtraction of two binary images according to the registered translation amount to obtain a cancel image; The positive and negative image processing module is used to perform positive and negative data decomposition on the canceled image, perform image opening and closing operations on the obtained positive and negative images respectively, and then use the DBSCAN clustering algorithm to divide the image after the opening and closing operation, while recording the division identifier. The moving target detection module is used to synthesize the divided positive and negative images. In the synthesized image, the area marked by both positive and negative images is delineated, and the shadow area and spot area of ​​the moving target are determined according to the relationship between the delineated area and the radar illumination area. At the same time, the direction of movement of the moving target is determined according to the vertical position relationship between the positive and negative images within the delineated area, so as to realize the detection of moving targets.

Images