SAR tomography method based on single-vision iteration MUSIC and self-cancellation sequence GLRT

Abstract

The invention relates to an SAR (Synthetic Aperture Radar) tomography method based on single-vision iteration MUSIC (Multi-phase Iterative Clustering) and self-cancellation sequence GLRT (Graphic Language The method comprises the following steps: selecting PS candidate points from a chromatography data set according to an amplitude deviation index, carrying out sub-region division according to the number of the PS candidate points, carrying out sub-network generation on each divided sub-region by utilizing an ant colony search method and the PS candidate points, giving a search starting point, connecting the starting point with all the PS candidate points in a set distance range, and carrying out sub-network generation on each sub-region by utilizing an ant colony search method. Three-dimensional imaging and scatterer detection are carried out on each connecting edge, only the connecting edge containing one scatterer is reserved according to the three-dimensional imaging and scatterer detection results, and after parameter estimation of the connecting edges of all the sub-regions is completed, duplicate removal and maximum connection network search are carried out on all the connecting edges; and performing network adjustment estimation on the maximum connection network by using weighted least squares to obtain the absolute height of each remaining PS point. The method has the advantages of high resolution and low sidelobe, and can accurately detect the layover scatterer.

Description

Technical Field

[0001] The present application relates to the field of satellite remote sensing imaging technology, and in particular to a SAR tomography method based on single-view iterative MUSIC and self-cancelling sequential GLRT. Background Art

[0002] The three-dimensional information of urban buildings is of great significance in urban planning and management, environmental impact assessment, and smart city construction. Traditional field measurements are time-consuming, labor-intensive, and extremely inefficient. Although optical photogrammetry can obtain a relatively realistic three-dimensional model, its sensors are generally mounted on drones or airborne platforms, making it difficult to obtain building information on a large scale, and the cost is high. Spaceborne SAR technology has been favored by many researchers in recent years for its wide coverage, revisitability, all-day, and all-weather advantages. Among them, multi-phase multi-flight InSAR technology represented by PSI is the most classic and mature. However, since PSI technology assumes that there is at most one strong scattering element target in a single pixel, PSI technology can only identify single scattering element targets, while ignoring double / multi-scattering elements overlapping in the same pixel.

[0003] To solve this problem, TomoSAR technology came into being. It provides a third-dimensional resolution capability in addition to the two-dimensional plane of azimuth-range by forming a second synthetic aperture in the altitude direction. Compared with traditional PSI technology, SAR tomography technology can identify overlapping PS points due to its altitude resolution, thus significantly increasing the spatial distribution density of PS points and inverting finer target details. In addition, because SAR tomography uses both amplitude and phase information, the PS point estimation accuracy is higher.

[0004] After more than 20 years of continuous development, TomoSAR technology has achieved good research results in urban areas. Building tomography inversion includes two key steps: three-dimensional imaging and model selection. First, its imaging methods can be roughly divided into three categories: non-parametric spectrum estimation method: no need for manual input parameters, high computational efficiency, but poor resolution, classic methods include Beamforming, Capon; parameter spectrum estimation method: requires manual input parameters (such as the number of scatterers), represented by the MUSIC method; sparse spectrum estimation method: based on the sparsity of the signal, the compressed sensing technology is introduced into tomography, with ultra-high resolution, but the computational complexity is high and time-consuming. Secondly, scatterer detection is mainly oriented to PS points. The PS point detection problem is actually a model selection (MS) problem, the purpose of which is to determine the number of scatterers and the height position of each scatterer. To this end, many scholars have conducted a lot of research and proposed a variety of PS point detectors. The two most common types of detectors are: Generalized Likelihood Ratio Test (GLRT) detectors and Information Theoretic Criteria (ITC) detectors. SGLRTC is the most representative GLRT detector. Because its judgment process implicitly includes the beamforming method, this method is also called BF-SGLRTC. Although this method is simple, easy to operate and highly efficient, the BF method has a low resolution and is easily affected by side lobes, resulting in energy leakage. Therefore, it is very necessary to develop a tomography method with high resolution, low side lobes and the ability to accurately detect overlapping scatterers. Summary of the invention

[0005] Based on this, it is necessary to provide a SAR tomography method based on single-look iterative MUSIC and self-cancellation sequential GLRT with high resolution, low sidelobe and accurate detection of overlapping scatterers to address the above technical problems.

[0006] A SAR tomography method based on single-view iterative MUSIC and self-cancellation sequential GLRT, the method comprising: Acquire a multi-phase data set; pre-process the multi-phase data set to obtain a tomographic data set; According to the amplitude deviation index, PS candidate points are selected from the tomographic data set, and sub-regions are divided according to the number of PS candidate points. The ant colony search method and PS candidate points are used to generate subnets for each divided sub-region. Given a search starting point, the starting point is connected to all PS candidate points within a set distance range, and three-dimensional imaging and scatterer detection are performed on each connection edge. According to the three-dimensional imaging and scatterer detection results, only the connection edges containing a scatterer are retained. At the same time, the imaging quality of the connection edge is judged. If the imaging quality is higher than the set threshold, the connection edge is retained. The end point of the valid connection edge is used as the starting point of the next ant colony search, and candidate point connection, three-dimensional imaging and scatterer detection are continued. After the connection edges of all sub-regions have completed parameter estimation, all connection edges are deduplicated and the maximum connection network search is performed to remove duplicate connection edges and isolated connection edges to ensure that the maximum connection network is connected and non-repetitive. The weighted least squares method is used to adjust the maximum connection network, a PS point is selected as the height reference point, and then the absolute height of each remaining PS point is estimated by the relative height difference of each connection edge.

[0007] The above-mentioned SAR tomography method based on single-view iterative MUSIC and self-cancellation sequential GLRT, the present application uses a quadtree segmentation method to divide the target area into several sub-areas and simultaneously construct a network, and uses a tomography method based on single-view iterative MUSIC and sequential self-cancellation GLRT to test the number of scatterers and estimate their heights. By iteratively eliminating the energy of the noise signal and utilizing the characteristics of the SGLRTC sequential estimation, the information of one scatterer is estimated each time, so that the sidelobe information in the obtained tomography spectrum is less, and at the same time, the overlapping scatterers at a closer distance can be better distinguished, which provides an effective guarantee for the high-precision inversion of the height information of urban buildings and improves the accuracy of SAR tomography. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of a flow chart of a SAR tomography method based on single-view iterative MUSIC and self-cancellation sequential GLRT in one embodiment; Figure 2 A block diagram of a SAR tomography method based on single-look iterative MUSIC and self-cancelling sequential GLRT in one embodiment; Figure 3 An example diagram of quadtree segmentation in one embodiment; Figure 4 A schematic diagram of the result of performing quadtree segmentation using measured data in another embodiment; Figure 5 A result graph of network construction for one embodiment; Figure 6 This is a diagram of the height inversion results of all PS points in a region in an embodiment. DETAILED DESCRIPTION

[0009] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0010] In one embodiment, Figure 1 and Figure 2 As shown, a SAR tomography method based on single-view iterative MUSIC and self-cancellation sequential GLRT is provided, comprising the following steps: Step 102, obtaining a multi-temporal data set; preprocessing the multi-temporal data set to obtain a tomographic data set.

[0011] Preprocess the multi-temporal data set; this data set is obtained by multiple spaceborne SAR satellites observing the same target multiple times at different altitudes with similar incident angles; the obtained data set needs to be preprocessed before tomography, including: registration, de-skewing and other operations, where the main purpose of registration is to ensure that the same pixel coordinates of different images correspond to the same target object, and the accuracy of registration needs to reach the sub-pixel level, which can be completed using different software; de-skewing is to facilitate the focusing process in the altitude direction, and the angular frequency support domain in the altitude direction needs to be corrected to near zero frequency, which can be done using slant range de-skewing or reference terrain de-skewing. After completing the above two steps, a tomographic data set that can be used for tomographic processing can be obtained. .

[0012] Step 104, select PS candidate points from the tomographic data set according to the amplitude deviation index, divide the sub-regions according to the number of PS candidate points, use the ant colony search method and the PS candidate points to generate a subnet for each divided sub-region, give a search starting point, connect the starting point with all PS candidate points within a set distance range, perform three-dimensional imaging and scatterer detection on each connection edge, and only retain the connection edge containing a scatterer according to the three-dimensional imaging and scatterer detection results. At the same time, the connection edge imaging quality judgment is also performed. If the imaging quality is higher than the set threshold, the connection edge is retained, and the end point of the valid connection edge is used as the starting point of the next ant colony search. Candidate point connection, three-dimensional imaging and scatterer detection are continued. After the connection edges of all sub-regions complete parameter estimation, all connection edges are deduplicated and the maximum connection network search is performed to remove duplicate connection edges and isolated connection edges to ensure that the maximum connection network is connected and non-duplicate.

[0013] In a specific embodiment, based on the results of three-dimensional imaging and scatterer detection, only the connection edges containing a scatterer are retained, and the imaging quality of the connection edges is also judged. If the imaging quality is higher than the set threshold, the connection edge is retained, otherwise it is discarded. The end point of the valid connection edge is used as the starting point of the next ant colony search, and the above search, three-dimensional imaging, and scatterer detection operations are continued. According to the sub-network constructed in each sub-region, all sub-networks are deduplicated for connection edges, and finally the SPS network and the scatterer estimation parameters of each SPS edge are obtained.

[0014] PS candidate points are selected according to the amplitude deviation index, and sub-regions are divided according to their number. This method can adaptively and reasonably subdivide the complex target area according to the data characteristics, so that subsequent processing can be more targeted in each sub-region, avoiding the complexity and inaccuracy that may occur when the entire large area is processed uniformly, and improving processing efficiency and accuracy. Ant colony search method and PS candidate points are used to generate subnets, and the processing process of connecting edges is expanded from a given starting point. The ant colony search algorithm has strong global search capabilities and adaptability, and can quickly find a better path or connection method in a complex solution space. Combined with PS candidate points, a network structure that reflects the characteristics of the target area can be efficiently constructed, providing a reasonable framework for subsequent imaging and detection. In addition, the connection edge processing process is continuously optimized, and the connection edges that do not meet the requirements are gradually eliminated, further improving the accuracy of data processing.

[0015] Perform 3D imaging and scatterer detection on each connection edge, and only retain the connection edges containing a scatterer based on the results. This step can effectively filter out the valid information that truly represents the building target and remove the false scatterer information that may be generated by noise or other interference factors, so that the subsequent processing based on these valid connection edges can more accurately reflect the actual situation of the building. At the same time, the judgment of the imaging quality of the connection edge also ensures that the data used has a high reliability and avoids errors caused by low-quality imaging data.

[0016] The number of scatterers and height estimation are performed by using a tomography method based on single-view iterative MUSIC and sequential self-cancellation GLRT. The advantage of this method is that it can iteratively eliminate the energy of noise signals and reduce the interference of noise on scatterer detection and height estimation. By using the characteristics of SGLRTC sequential estimation, only one scatterer's information is estimated each time. This gradually refined estimation method can more accurately distinguish the characteristics of each scatterer, resulting in less sidelobe information in the obtained tomography spectrum, improving the resolution of scatterers, especially those overlapping scatterers at a close distance, thus providing strong support for high-precision inversion of building height information.

[0017] Step 106, using weighted least squares to perform network adjustment on the maximum connection network, select a PS point as a height reference point, and then estimate the absolute height of each remaining PS point through the relative height difference of each connection edge.

[0018] After the parameters of all the connecting edges of the sub-areas are estimated, all the connecting edges are deduplicated and the maximum connecting network is searched to remove duplicate and isolated connecting edges to ensure that the maximum connecting network is connected and non-duplicate. The maximum connecting network is adjusted using weighted least squares. First, a PS point is selected as the height reference point, and then the absolute height of each remaining PS point is estimated by the relative height difference of each connecting edge.

[0019] The target area is divided into several sub-areas using the quadtree segmentation method and the network is constructed at the same time. Quadtree segmentation can efficiently divide the spatial area into layers, which is convenient for data management and processing at different levels. The simultaneous network construction can better integrate the information of each sub-area to form a complete network structure that accurately reflects the overall picture of the target area. Finally, the maximum connection network is adjusted using weighted least squares, and a PS point is selected as the height reference point. The absolute height of each remaining PS point is estimated by the relative height difference of each connection edge. This adjustment method can effectively eliminate the systematic error and random error generated during the measurement process, further improve the accuracy of height information inversion, and make the final urban building height information more accurate and reliable.

[0020] The above-mentioned SAR tomography method based on single-view iterative MUSIC and self-cancellation sequential GLRT, the present application uses a quadtree segmentation method to divide the target area into several sub-areas and simultaneously construct a network, and uses a tomography method based on single-view iterative MUSIC and sequential self-cancellation GLRT to test the number of scatterers and estimate their heights. By iteratively eliminating the energy of the noise signal and utilizing the characteristics of the SGLRTC sequential estimation, the information of one scatterer is estimated each time, so that the sidelobe information in the obtained tomography spectrum is less, and at the same time, the overlapping scatterers at a closer distance can be better distinguished, which provides an effective guarantee for the high-precision inversion of the height information of urban buildings and improves the accuracy of SAR tomography.

[0021] In one embodiment, the sub-regions are divided according to the number of PS candidate points, including: Quadtree segmentation is performed according to the set upper limit threshold of the number of PS candidate points contained in the sub-region. If the total number of PS candidate points in the current sub-region is higher than the set threshold, the sub-region will continue to be segmented until the total number of PS candidate points in each sub-region is lower than the set threshold; the final range of each sub-region is widened to a certain width toward the surrounding effective area based on the original area.

[0022] In a specific embodiment, for the global PS candidate points, quadtree segmentation is used to divide the sub-regions, and the number of PS points in each target area (initial area or sub-region obtained by segmentation) is determined:

[0023] When the number of PS in the target area Less than threshold (here set to 5000), the area will perform the operation : Stop segmentation, otherwise execute the operation : Continue segmentation. For each sub-region finally obtained, although their spatial sizes are different, the number of PS points in the region is lower than the set threshold. When constructing the ant colony network, each sub-network extends the distance to the surrounding area. . It is the upper limit threshold of the connection edge distance, which is set to 80m here.

[0024] In one embodiment, performing three-dimensional imaging and scatterer detection on each connection edge includes: For each connection edge, the initial observation signals of the two endpoints are differentiated. First, SLI-MUSIC three-dimensional imaging is performed, and the beamforming method is used to obtain a preliminary backscattered energy spectrum estimate. Then, the covariance matrix is ​​estimated, and then the covariance matrix is ​​eigenvalue decomposed to obtain the eigenvalues ​​and eigenvectors. The eigenvalue and the eigenvector are used to perform backscattering profile estimation and noise energy estimation to obtain the backscattering profile and noise energy; then an iterative threshold judgment is performed, and if the number of iterations is less than a given threshold, the method returns to the covariance matrix estimation; if the number of iterations is not less than the given threshold, the loop is exited, and the height estimation value of the first scatterer is obtained by using the backscattering profile estimation obtained last time; SLI-MUSIC 3D imaging needs to be performed twice. The first time is to use the initial observation signal to obtain the height estimate of the first scatterer. The estimated value of the height of the first scatterer is used to remove the signal of the scatterer from the observation value, obtain the remaining observation signal and perform SLI-MUSIC 3D imaging again to obtain the height estimate of the second scatterer. For scatterer detection, a double scatterer hypothesis test is first performed. If it is greater than the set threshold, the hypothesis is accepted and the height estimates of the two scatterers are obtained. Otherwise, a single scatterer hypothesis test is performed. If it is greater than the set threshold, the hypothesis is accepted and the estimated height of the main scatterer is obtained. Otherwise, it means that the connection edge differential signal does not contain a scatterer.

[0025] In one embodiment, a preliminary backscattered energy spectrum estimation is obtained using a beamforming method, including: Use the beamforming method to obtain a preliminary backscattered energy spectrum estimate:

[0026] in, is the guiding matrix, is the observation vector, represents the initial backscattering profile.

[0027] In one embodiment, the process of estimating the covariance matrix then includes: Estimation of the covariance matrix:

[0028] in, Indicates The estimated covariance matrix is For the The estimated backscatter profile is For the The estimated noise energy is express The identity matrix of is the number of images.

[0029] In one embodiment, performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors includes: Perform eigenvalue decomposition on the covariance matrix and obtain the eigenvalues ​​and eigenvectors as follows:

[0030] in, , , Represents the feature vector The corresponding eigenvalues; represents the eigenvector in the signal subspace; represents the eigenvector in the noise subspace.

[0031] In one embodiment, using the eigenvalue and the eigenvector to estimate the backscattering profile and the noise energy to obtain the backscattering profile and the noise energy includes: set up , that is, only one scatterer signal is detected from the observation value each time to estimate the backscattering profile:

[0032] in, express Middle elements, for Middle column vector, represents the eigenvector in the noise subspace, For the The backscatter profile obtained by this estimation.

[0033] In one embodiment, the noise energy is estimated:

[0034] in, represents the matrix trace, Indicates The noise energy estimated is is the sample covariance matrix, Indicates The covariance matrix obtained by this estimation.

[0035] In a specific embodiment, is the sample covariance matrix:

[0036] in, Indicates the number of multiple views, set here That is, only the current pixel is selected to estimate the sample covariance matrix.

[0037] Then the number of iterations is judged, if it is less than or equal to the iteration threshold , the above operation is repeated, otherwise the loop is exited and the backscattering profile obtained last time is used to estimate the parameters to be estimated. Here, the height position corresponding to the maximum peak in the backscattering profile is selected as the height estimation result.

[0038] Use the estimated result of the first scatterer to remove the signal of the scatterer from the observed value to obtain the remaining observed signal . And use SLI-MUSIC three-dimensional imaging is performed again to obtain the estimated height of the second scatterer.

[0039] In one embodiment, for scatterer detection, a double scatterer hypothesis test is first performed, and if it is greater than a set threshold, the hypothesis is accepted to obtain the height estimation values ​​of the two scatterers; otherwise, a single scatterer hypothesis test is performed, and if it is greater than the set threshold, the hypothesis is accepted to obtain the estimated height of the main scatterer; otherwise, it means that the connection edge differential signal does not contain a scatterer, including: For scatterer detection, we first assume : Contains two scatterers - does not contain two scatterers; judgment:

[0040] in, and They represent the projection of the observed signal onto and The energy corresponding to the component on ; If satisfied Assuming that , it means that the observed signal contains two scatterers, and their corresponding heights are the positions estimated by the previous two three-dimensional imaging; Next, make the assumption : contains a scatterer - does not contain a scatterer); judge:

[0041] in, Represents the projection of the observed signal onto The energy corresponding to the component on ; If satisfied Assume that, it means that the observed signal contains a scatterer, and the corresponding height is the position estimated by the first three-dimensional imaging; if it satisfies Assume that , then the observed signal does not contain scatterers.

[0042] In a specific embodiment, Figure 3 As shown in the figure, it is an example of quadtree segmentation. When the sub-region is inside the initial region (such as Figure 3 blue area), the sub-area needs to expand the search range to the surrounding areas; when the sub-area is at the edge of the initial area (such as Figure 3 The sub-regions are only expanded inwards to the adjacent sub-regions. By widening the search area, it is ensured that each sub-region can be connected. Figure 3 The red dotted box) will remove duplicate connection edges after the parallel network construction of each sub-region is completed. Since the above operation ensures sufficient redundancy, the connection between sub-regions can still be achieved after the duplicate edges are removed.

[0043] like Figure 4 As shown in FIG. 1 , it is the result of quadtree segmentation using measured data, which is composed of 15 images obtained by observing city B from satellite A.

[0044] like Figure 5 As shown in Figure 1, it is the result of network construction. It can be seen that the network covers most of the area, and the redundancy of the connection edges of each PS point is also guaranteed.

[0045] like Figure 6As shown in the figure, the height inversion results of all PS points in the area are three-dimensional. It can be clearly seen that the inversion results are consistent with objective reality, and the detailed information of most buildings can also be well estimated.

[0046] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0047] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A SAR tomography method based on single-look iterative MUSIC and self-cancelling sequential GLRT, characterized in that: The method comprises: Acquire a multi-phase data set; pre-process the multi-phase data set to obtain a tomographic data set; Select PS candidate points from the tomographic data set according to the amplitude deviation index, divide the sub-regions according to the number of PS candidate points, generate subnets for each divided sub-region using the ant colony search method and the PS candidate points, give a search starting point, connect the starting point with all PS candidate points within a set distance range, perform three-dimensional imaging and scatterer detection on each connection edge, and only retain the connection edge containing a scatterer according to the three-dimensional imaging and scatterer detection results. At the same time, the connection edge imaging quality judgment is also performed. If the imaging quality is higher than the set threshold, the connection edge is retained, and the end point of the effective connection edge is used as the starting point of the next ant colony search. Candidate point connection, three-dimensional imaging and scatterer detection are continued. After the connection edges of all sub-regions complete parameter estimation, all connection edges are deduplicated and the maximum connection network search is performed to remove duplicate connection edges and isolated connection edges to ensure that the maximum connection network is connected and non-repetitive; The weighted least squares method is used to adjust the maximum connection network, a PS point is selected as the height reference point, and then the absolute height of each remaining PS point is estimated by the relative height difference of each connection edge.

2. The method according to claim 1, characterized in that Sub-areas are divided according to the number of PS candidate points, including: Quadtree segmentation is performed according to the set upper limit threshold of the number of PS candidate points contained in the sub-region. If the total number of PS candidate points in the current sub-region is higher than the set threshold, the sub-region will continue to be segmented until the total number of PS candidate points in each sub-region is lower than the set threshold; the final range of each sub-region is widened to a certain width toward the surrounding effective area based on the original area.

3. The method according to claim 1, characterized in that: 3D imaging and scatterer detection for each connection edge, including: For each connection edge, the initial observation signals of the two endpoints are differentiated. First, SLI-MUSIC three-dimensional imaging is performed, and the beamforming method is used to obtain a preliminary backscattered energy spectrum estimate. Then, the covariance matrix is ​​estimated, and then the covariance matrix is ​​eigenvalue decomposed to obtain the eigenvalues ​​and eigenvectors. The eigenvalue and the eigenvector are used to perform backscattering profile estimation and noise energy estimation to obtain the backscattering profile and noise energy; then an iterative threshold judgment is performed, and if the number of iterations is less than a given threshold, the method returns to the covariance matrix estimation; if the number of iterations is not less than the given threshold, the loop is exited, and the height estimation value of the first scatterer is obtained by using the backscattering profile estimation obtained last time; SLI-MUSIC 3D imaging needs to be performed twice. The first time is to use the initial observation value signal to obtain the height estimate of the first scatterer; the preliminary estimate of the height of the first scatterer is used to remove the signal of the scatterer from the observation value, obtain the remaining observation signal and perform SLI-MUSIC 3D imaging again to obtain the height estimate of the second scatterer; For scatterer detection, a double scatterer hypothesis test is first performed. If it is greater than the set threshold, the hypothesis is accepted and the height estimates of the two scatterers are obtained. Otherwise, a single scatterer hypothesis test is performed. If it is greater than the set threshold, the hypothesis is accepted and the estimated height of the main scatterer is obtained. Otherwise, it means that the connection edge differential signal does not contain a scatterer.

4. The method according to any one of claims 1 to 3, characterized in that: The beamforming method is used to obtain a preliminary backscattered energy spectrum estimate, including: Use the beamforming method to obtain a preliminary backscattered energy spectrum estimate: in, is the guiding matrix, represents the observation vector, represents the initial backscattering profile.

5. The method according to claim 1, characterized in that The process of estimating the covariance matrix then includes: Estimation of the covariance matrix: in, Indicates The estimated covariance matrix is For the The estimated backscatter profile is For the The estimated noise energy is express The identity matrix of is the number of images.

6. The method according to claim 1, characterized in that Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors, including: Perform eigenvalue decomposition on the covariance matrix and obtain the eigenvalues ​​and eigenvectors as follows: in, , , Represents the feature vector The corresponding eigenvalues; represents the eigenvector in the signal subspace; represents the eigenvector in the noise subspace.

7. The method according to claim 1, characterized in that The backscattering profile and noise energy are estimated by using the eigenvalue and the eigenvector to obtain the backscattering profile and the noise energy, including: set up , that is, only one scatterer signal is detected from the observation value each time to estimate the backscattering profile: in, express Middle elements, for Middle column vector, represents the eigenvector in the noise subspace, For the The backscatter profile is estimated.

8. The method according to claim 7, characterized in that The method further comprises: Estimate the noise energy: in, represents the matrix trace, Indicates The noise energy estimated is is the sample covariance matrix, Indicates The covariance matrix obtained by this estimation.

9. The method according to claim 1, characterized in that: For scatterer detection, a double scatterer hypothesis test is first performed. If the value is greater than the set threshold, the hypothesis is accepted to obtain the height estimation values ​​of the two scatterers. Otherwise, a single scatterer hypothesis test is performed. If the value is greater than the set threshold, the hypothesis is accepted to obtain the estimated height of the main scatterer. Otherwise, it means that the differential signal of the connection edge does not contain scatterers, including: For scatterer detection, we first assume : Contains two scatterers - does not contain two scatterers; judgment: in, and They represent the projection of the observed signal onto and The energy corresponding to the component on ; If satisfied Assuming that , it means that the observed signal contains two scatterers, and their corresponding heights are the positions estimated by the previous two three-dimensional imaging; Next, make the assumption : contains a scatterer - does not contain a scatterer; judge: in, Represents the projection of the observed signal onto The energy corresponding to the component on ; If satisfied Assume that, it means that the observed signal contains a scatterer, and the corresponding height is the position estimated by the first three-dimensional imaging; if it satisfies Assume that , then the observed signal does not contain scatterers.

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