Arrangement method and device of SAR corner reflector for high mountain and valley area

Abstract

The application belongs to the technical field of synthetic aperture radar, and discloses a SAR corner reflector layout method for high mountain and valley areas; the application introduces an MDDG model and an R index evaluation index, combines GNSS high-precision positioning and R-D equation iterative optimization, realizes accurate positioning of the SAR corner reflector and optimization of the installation angle, and guarantees high spatial precision and long-term stability of InSAR monitoring; after the SAR corner reflector is installed, the application performs RCS time sequence analysis and UAV image optimization on the installation angle; the method of the application can improve installation stability, signal reflection quality and observation precision of the SAR reflector, especially under complex terrain and environmental conditions, can optimize layout and positioning of the reflector, and solves problems such as signal attenuation, shielding, positioning error and signal aliasing of the reflector in the prior art.

Description

Technical Field

[0001] This invention belongs to the field of synthetic aperture radar technology, specifically relating to a method for deploying SAR corner reflectors in high mountain and canyon areas, and also to a computer device. Background Technology

[0002] Synthetic Aperture Radar (SAR) is an all-weather, all-time, active microwave imaging radar with high resolution and strong penetration capability. In SAR applications, the geometric positioning and radiometric consistency of images are highly dependent on reliable ground reference targets to ensure that images under different acquisition conditions are comparable and traceable. Among them, corner reflectors (CRs), as controllable artificial strong scatterers, are widely used for geometric / radiometric calibration, positioning accuracy evaluation, and long-term consistency verification of SAR systems due to their stable RCS, clear phase center, and ability to be precisely deployed and remeasured.

[0003] In complex terrain areas such as high mountains and canyons, the site selection, installation, and precise positioning of SAR corner reflectors face unique challenges. Traditional deployments are often affected by terrain undulations, slope changes, vegetation obstruction, and signal attenuation, easily leading to insufficient line-of-sight and geometric distortions (compression, overlay, shadows), thus limiting positioning / calibration accuracy and effective coverage. In such areas, reflector site selection and installation must not only overcome large undulations and complex slopes / incident angles, but also solve the problems of base stability and signal interference / obstruction in extreme environments such as weathering, rainfall, snow accumulation, and water accumulation. Therefore, improving the site selection, installation, and positioning methods of SAR corner reflectors in high mountain and canyon areas has become a key issue in improving the geometric positioning accuracy, radiometric consistency, and long-term stability of SAR images. Currently, related practices mostly rely on experience and simple shape interpretation, lacking systematic methods such as quantitative assessment of line-of-sight for complex terrains, RCS accessibility prediction, attitude / azimuth optimization, and engineering accessibility evaluation. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the prior art by providing a method for deploying SAR corner reflectors in high mountain and canyon areas, and also to provide a computer device.

[0005] The above-mentioned objectives of the present invention are achieved by the following technical means:

[0006] The deployment method for SAR corner reflectors in high mountain and canyon areas includes the following steps:

[0007] Step 1: Select the deployment area of ​​SAR corner reflectors based on the continuous time period SAR image dataset and topographic elevation data DEM of the study area;

[0008] Step 2: Install the target number of SAR corner reflectors within the deployment area, and optimize the position of the SAR corner reflectors by using the positioning error of the GNSS positioning coordinates of each SAR corner reflector and the planar error of the initial SAR coordinates.

[0009] Step 3: Calculate and determine the initial installation angle of the SAR corner reflector based on the SAR image dataset of continuous time periods in the study area;

[0010] Step 4: Optimize the initial installation angle of each SAR corner reflector based on RCS time series data through RCS analysis;

[0011] Step 5: For SAR corner reflectors that do not meet the RCS threshold in Step 4, perform interference source analysis based on UAV imagery, reselect the location within the deployment area, and return to Step 2 for re-deployment.

[0012] Step 6: Continuously monitor the signal strength and stability of the SAR corner reflector and develop a regular maintenance plan.

[0013] As mentioned above, the deployment area for SAR corner reflectors is selected in the following way:

[0014] First, based on the topographic elevation data (DEM) of the study area, areas with slopes less than a set value within the study area are selected as candidate deployment areas.

[0015] Secondly, based on the continuous time period SAR image dataset and topographic elevation data DEM of the study area, the MDDG model and R index of each candidate deployment area are calculated. Among all candidate deployment areas, the area with an MDDG model greater than or equal to the set MDDG threshold and an R index value greater than or equal to the set R index threshold is selected as the deployment area.

[0016] As described above, step 2 specifically includes the following steps:

[0017] Step 2.1: Install SAR corner reflectors within the deployment area;

[0018] Step 2.2: Measure the GNSS positioning coordinates of each SAR corner reflector. The GNSS positioning coordinates include latitude, longitude, and elevation.

[0019] Step 2.3: Using the SAR image dataset, the RD equation is used to locate each SAR corner reflector, and the initial SAR coordinates of each SAR corner reflector in the SAR image coordinate system are obtained.

[0020] Step 2.4: Measure the positioning error of the GNSS positioning coordinates of each SAR corner reflector. For SAR corner reflectors with positioning errors greater than the set GNSS positioning error threshold, re-deploy and install them in the deployment area until the positioning errors of the GNSS positioning coordinates of all SAR corner reflectors are within the set GNSS positioning error threshold, and re-measure the GNSS positioning coordinates of each SAR corner reflector.

[0021] Step 2.5: Measure the plane error of the initial SAR coordinates based on the GNSS positioning coordinates, set the acceptance threshold for the plane error, and re-deploy the SAR corner reflectors corresponding to the initial SAR coordinates that exceed the set acceptance threshold for the plane error in the deployment area, and return to step 2.4 until all the corresponding SAR corner reflectors meet the set GNSS positioning error threshold and the set acceptance threshold for the plane error.

[0022] As mentioned above, the initial installation angle of the SAR corner reflector is determined in the following way:

[0023] Step 3.1: Obtain the satellite's precise orbital state vector and imaging time. ;

[0024] Step 3.2: Convert the initial SAR coordinates of the SAR corner reflector to geocentric coordinates. ;

[0025] Step 3.3, in Real-time interpolation yields satellite position Calculate the slant distance vector ;

[0026] Step 3.4: Transform the slant range vector R to the local northeast-sky coordinate system with the corresponding SAR corner reflector as the origin to obtain the local incident angle. and azimuth ,in, , , These are the slant range vectors. The celestial component, eastern component, and northern component in the local northeast celestial coordinate system;

[0027] Step 3.5: Obtain the elevation angle of the SAR corner reflector. and azimuth This causes the SAR corner reflector opening to face directly towards the radar;

[0028] Step 3.6: Perform grid occlusion analysis based on the terrain elevation data DEM. If the SAR corner reflector... , If the direction is obstructed, the system will rotate around the vertical axis within a set range to find the optimal path until the obstruction area is less than the set obstruction area threshold. The angle at which the index value is still greater than or equal to the set R index threshold is the initial installation angle.

[0029] As described above, step 4 specifically includes the following steps:

[0030] Step 4.1: Calculate the RCS value and RCS time-series stability of the SAR reflector using the peak method based on the RCS time-series data, and compare them with the RCS acceptance threshold and the RCS time-series stability threshold respectively.

[0031] Step 4.2: For cases where the two thresholds in Step 4.1 cannot be met simultaneously, the installation angle of the SAR corner reflector is finely adjusted by rotating it within the set range of the elevation angle or the set range of the azimuth angle, and the image is re-imaged until the two thresholds in Step 4.1 are met simultaneously.

[0032] As described above, step 5 specifically includes the following steps: based on UAV image analysis, analyze the micro-topography and ground object distribution around the corner reflector to identify local interference sources that cause multiple scattering or obstruction of radar signals; based on the identification results, reselect the location to eliminate local interference sources within the deployment area and return to step 2 for re-deployment until the RCS value and its temporal stability simultaneously meet the threshold requirements.

[0033] As mentioned above, SAR image datasets are acquired and processed in the following ways:

[0034] First, multiple initial SAR image data from satellites over a continuous period are directly acquired to construct an initial SAR image dataset, and one of the initial SAR image data is selected as the reference image data.

[0035] Secondly, the initial SAR image dataset is registered: the other SAR image data in the initial SAR image dataset, except for the reference image data, are registered to the reference image data to obtain the SAR image dataset.

[0036] As mentioned above, the initial SAR image dataset includes the same number of ascending and descending orbit image data; the registration method uses the amplitude cross-correlation method.

[0037] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement steps 1, 3, 4, 5, and 6 of the SAR corner reflector deployment method for high mountain and canyon areas as described above.

[0038] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements steps 1, 3, 4, 5, and 6 of the method for deploying SAR corner reflectors in mountainous and canyon areas as described above.

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] (1) Strong adaptability: This invention proposes an optimization method for the location and installation angle of SAR corner reflectors based on multi-source data fusion in complex terrain environments such as high mountains and canyons. It comprehensively considers factors such as slope, vegetation cover, signal obstruction and radar observation angle, and can flexibly select locations in complex terrain, significantly improving the rationality and applicability of SAR corner reflector deployment.

[0041] (2) High precision: By introducing the MDDG model and R index evaluation index, and combining GNSS high-precision positioning and RD equation iterative optimization, this invention achieves accurate positioning and installation angle optimization of SAR corner reflectors, ensuring high spatial accuracy and long-term stability of InSAR monitoring.

[0042] (3) Good stability: After the SAR corner reflector is installed, the present invention tracks the reflection intensity and stability of the SAR corner reflector signal through RCS time sequence analysis and SAR monitoring, and establishes a maintenance and adjustment mechanism to effectively resist the signal attenuation and equipment loss caused by the harsh environment in high mountain and canyon areas, and ensure long-term reliable operation.

[0043] (4) Scalability: The SAR corner reflector deployment and positioning optimization process proposed in this invention is universal and can be extended to different SAR systems (C-band, L-band, etc.) and different observation modes (ascending orbit, descending orbit), providing extensive application value for geological disaster monitoring and precision deformation measurement.

[0044] In summary, the method of the present invention can improve the installation stability, signal reflection quality and observation accuracy of SAR reflectors. Especially under complex terrain and environmental conditions, it can optimize the layout and positioning of reflectors and solve problems such as reflector signal attenuation, obstruction, positioning error and signal aliasing that exist in the prior art. Attached Figure Description

[0045] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0046] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to embodiments. The embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0047] Example 1:

[0048] like Figure 1 As shown, the deployment method of SAR corner reflectors for high mountain and canyon areas includes the following steps:

[0049] Step 1: Select the deployment area for SAR corner reflectors based on the continuous time-period SAR image dataset and topographic elevation data (DEM) of the study area. This includes the following steps:

[0050] Step 1.1: Acquire SAR image data and perform registration, specifically including the following steps:

[0051] Step 1.1.1 First, acquire multiple SAR image data over a continuous period to construct an initial SAR image dataset. The initial SAR image dataset includes the same number of ascending SAR image data and descending SAR image data, and select reference image data from the initial SAR image dataset.

[0052] In this embodiment, 24 SAR images from the Sentinel-1 satellite between March and July 2024 are selected to construct the initial SAR image dataset. The spatial resolution of the images is 5m×20m. The 24 SAR images include 12 ascending orbit SAR images and 12 descending orbit SAR images. The SAR image data from May 1, 2024, in the middle period is selected as the reference image data.

[0053] Step 1.1.2: Register the initial SAR image dataset: Register all SAR image data in the initial SAR image dataset except for the reference image data to the reference image data to obtain the registered SAR image data. All registered SAR image data are used to construct a SAR image dataset, which ensures the accurate alignment of SAR image data at different time points and provides stable basic data for subsequent positioning analysis.

[0054] As one possible implementation, the amplitude cross-correlation method is used to perform sub-pixel-level registration of other SAR image data besides the reference image data to the reference image data, wherein the average registration error of all SAR image data is 0.32 pixels.

[0055] Step 1.2: Based on the 30m resolution terrain elevation data DEM from SRTM (Space Shuttle Radar Terrain Mapping Mission), analyze the terrain features of the study area, obtain the slope information within the target area, and select areas with slopes less than the set value as candidate deployment areas for SAR corner reflectors.

[0056] The study area in this embodiment is a mountainous canyon region. The maximum slope in the target area is 58° and the minimum slope is 12°. It is covered by 35% vegetation. The slopes in the center of the target area and on both sides of the canyon are relatively large. The areas suitable for SAR corner reflector deployment are mainly concentrated in flat areas with slopes of less than 30°. Therefore, areas with slopes of less than 30° are selected as candidate deployment areas for SAR corner reflectors.

[0057] Step 1.3: Evaluate the candidate deployment areas using the MDDG model and R-index, and select the areas with better SAR signal propagation performance from all candidate deployment areas as the deployment areas. This specifically includes the following process:

[0058] Combining the long-span registered SAR image data obtained in step 1.1 with the 30m resolution terrain elevation data DEM from SRTM in step 1.2, visibility analysis is performed using the MDDG model (maximum detectable deformation gradient) and the R-index. The MDDG value and R-index value of each candidate deployment area in step 1.2 are calculated. The area in the candidate deployment area where (MDDG value ≥ first set MDDG threshold) ∩ (R-index value ≥ first set R-index) is selected as the deployment area of ​​the SAR corner reflector.

[0059] In this embodiment, the area that simultaneously satisfies the MDDG value ≥ 1.5 mm / 100m and R-Index ≥ 0.75 is selected as the deployment area for the SAR corner reflector.

[0060] Step 2: Install the target number of SAR corner reflectors within the deployment area. Optimize the position of the SAR corner reflectors using the positioning error of the GNSS positioning coordinates and the planar error of the initial SAR coordinates of each SAR corner reflector. This specifically includes the following steps:

[0061] Step 2.1: Install SAR corner reflectors within the deployment area selected in Step 1.

[0062] Step 2.2: Use a high-precision GNSS receiver to measure the reference points of each SAR corner reflector installed in Step 2.1 to obtain its precise GNSS positioning coordinates (including latitude, longitude, and elevation). The GNSS positioning coordinates will be used as the reference true values ​​for subsequent positioning error analysis.

[0063] Step 2.3: Using the registered SAR image dataset, the RD equation is used to locate each SAR corner reflector, and the initial SAR coordinates of each SAR corner reflector in the SAR image coordinate system are obtained.

[0064] Step 2.4: Measure the positioning error of the GNSS positioning coordinates of each SAR corner reflector. For SAR corner reflectors with positioning errors greater than the set GNSS positioning error threshold (e.g., 0.5 meters), re-deploy and install them in the deployment area. Finally, ensure that the positioning errors of the GNSS positioning coordinates of all SAR corner reflectors are within the set GNSS positioning error threshold, and re-measure the GNSS positioning coordinates of each SAR corner reflector.

[0065] Step 2.5: Measure the plane error of the initial SAR coordinates based on the obtained millimeter-level GNSS positioning coordinates, set the acceptance threshold for the plane error, and re-deploy the SAR corner reflectors corresponding to the initial SAR coordinates that exceed the set acceptance threshold for the plane error in the deployment area, and return to step 2.4, until all the corresponding SAR corner reflectors meet the set GNSS positioning error threshold and the set acceptance threshold for the plane error.

[0066] In this embodiment, using 24 registered SAR image data, the 10 SAR corner reflectors installed in the deployment area were located using the RD equation, and the GNSS positioning coordinates and initial SAR coordinates of these 10 SAR corner reflectors were obtained, providing a data basis for subsequent plane error measurement and fine-tuning optimization.

[0067] This invention introduces the MDDG model and the R-index evaluation index, and combines GNSS high-precision positioning with RD equation iterative optimization to achieve precise positioning and installation angle optimization of SAR corner reflectors, thus ensuring high spatial accuracy and long-term stability of InSAR monitoring.

[0068] Step 3: Calculate and determine the initial installation angle of the SAR corner reflector based on the SAR image dataset of continuous time periods in the study area. The initial installation angle of the SAR corner reflector is determined through the following steps:

[0069] Step 3.1: Read the satellite's precise orbital state vector and imaging time. .

[0070] Step 3.2: Convert the initial SAR coordinates (Lat, Lon, H) of the SAR corner reflector to geocentric and geofixed coordinates (ECEF). .

[0071] Step 3.3, in Real-time interpolation yields satellite position Calculate the slant distance vector .

[0072] Step 3.4: Transform the slant range vector R to the local northeast-sky coordinate system (ENU) with the corresponding SAR corner reflector as the origin to obtain the local incident angle. and azimuth ,in, , , These are the slant range vectors. The celestial component, eastern component, and northern component in the local northeast celestial coordinate system.

[0073] Step 3.5, Elevation angle of SAR corner reflector azimuth (Make the corner reflector opening face the radar).

[0074] Step 3.6: Perform occlusion analysis on a 5 m grid based on the terrain elevation data DEM. If the SAR corner reflector... , If the direction is obstructed by the terrain, rotate around the vertical axis within a range of ±10° to find the optimal angle until the obstruction area is less than 5% and the R-index value is still greater than or equal to 0.75. This angle is then used as the initial installation angle.

[0075] Step 4: Optimize the initial installation angle of each SAR corner reflector based on RCS time-series data through RCS analysis. This includes the following steps:

[0076] Step 4.1: Perform RCS (Radar Cross Section) analysis based on RCS time-series data. Calculate the RCS value and RCS time-series stability of the SAR reflector using the peak method, and compare them with the RCS acceptance threshold and the RCS time-series stability threshold, respectively. In this embodiment, a preset RCS acceptance threshold is used. = +10 dBsm, RCS timing stability threshold ≤±2 dB.

[0077] Step 4.2: For cases where the two thresholds in Step 4.1 cannot be met simultaneously, the installation angle of the SAR corner reflector is finely adjusted by rotating it within the set range of the elevation angle or the set range of the azimuth angle, and the image is re-imaged until the two thresholds in Step 4.1 are met simultaneously.

[0078] Step 5: For SAR corner reflectors that do not meet the RCS threshold in Step 4, perform interference source analysis based on UAV imagery, reselect locations within the deployment area, and return to Step 2 for re-deployment. This process includes the following steps:

[0079] If both thresholds cannot be met simultaneously after step 4.2, a fine-tuning of the location based on UAV imagery is performed: high-resolution UAV imagery of the SAR corner reflector installation location is acquired, and the deployment scheme is iteratively optimized in combination with RCS time-series data; the specific process is as follows: based on UAV imagery, the micro-topography and ground object distribution around the corner reflector are analyzed to identify local interference sources that may cause multiple scattering or obstruction of radar signals. Local interference sources include isolated man-made structures (such as utility poles, small buildings), large rocks, nearby steep slopes or rock walls, tall vegetation, micro-topographic pits or gullies, and seasonal water or snow accumulation areas, etc.; based on the identification results, the location to eliminate local interference sources is reselected within the deployment area and the process returns to step 2 for re-deployment until the RCS value and its time-series stability simultaneously meet the threshold requirements.

[0080] In this embodiment, the analysis was performed using 0.5 m resolution UAV images and the time-series standard deviation of RCS as input. It was found that the original position of the SAR corner reflector CR3 (slope direction NE35°, 3 m from the edge of the steep slope) resulted in an RCS standard deviation of ±6 dB due to multiple scattering from the side slope. Based on the analysis of the micro-topography and ground object distribution around the corner reflector using UAV images, the interference source was identified. The SAR corner reflector CR3 was then moved 5 m along the slope direction to a micro-platform (slope direction NE32°, 8 m from the edge of the steep slope, elevation reduced by 1.2 m, pitch angle adjusted from 18° to 20°, azimuth angle maintained at 270°). After reinstallation, imaging and measurement were performed again, and the RCS standard deviation decreased to ±2 dB, meeting the stability threshold, thus completing the fine-tuning optimization.

[0081] Traditional reflector deployment methods are often affected by factors such as terrain undulations, vegetation obstruction, and signal attenuation, resulting in insufficient monitoring accuracy and coverage. Especially in complex terrain environments such as high mountains and canyons, the positioning and installation of corner reflectors not only need to overcome the challenges posed by the complex terrain but also need to address the perspective contraction effect and its impact on the signal, thereby effectively avoiding signal aliasing and ensuring clear, stable, and high-quality reflector signals. The method of this invention fully considers the complexity of terrain and environmental changes in high mountain and canyon areas, employing multi-source data and algorithms to optimize the site selection and installation angle of SAR reflectors. This ensures the stability and long-term reliability of SAR reflectors in practical applications, improves the installation stability, signal reflection quality, and observation accuracy of SAR reflectors, and, particularly under complex terrain and environmental conditions, optimizes the deployment and positioning of reflectors, solving problems such as reflector signal attenuation, obstruction, positioning errors, and signal aliasing existing in current technologies.

[0082] Step 6: Continuously monitor the signal strength and stability of the SAR corner reflector and develop a regular maintenance plan, which includes the following steps:

[0083] Step 6.1 SAR corner reflector signal stability monitoring: After the SAR corner reflector is installed, the reflected signal strength of the SAR corner reflector is monitored. The reflected signal strength of the SAR corner reflector remains within the fluctuation range of ±0.8 dB within 6 months after installation, which meets the expected performance requirements.

[0084] Step 6.2, SAR positioning and precise measurement: SAR positioning of SAR corner reflectors is performed using SAR image datasets, and precise measurement is performed based on the RD equation. By analyzing the SAR coordinates of SAR corner reflectors and the SAR positioning results of multiple SAR corner reflector points, the root mean square error (RMSE) was finally obtained as 2.1 meters.

[0085] Step 6.3, Long-term monitoring and signal stability verification: The SAR corner reflector is monitored over a long period of time through the SAR system to ensure that the SAR corner reflector can maintain signal stability during long-term operation. In the first year after installation, the signal strength change of the SAR corner reflector remains within ±1.0 dB, indicating that the performance of the SAR corner reflector remains stable in the high mountain and canyon environment.

[0086] Step 6.4 Regular Maintenance Plan: Based on the operating status of the SAR corner reflector, a quarterly inspection plan was developed, and routine maintenance was carried out on the SAR corner reflector. Considering the impact of weathering and precipitation in the region, regular maintenance ensured that the SAR corner reflector was always in optimal working condition.

[0087] Step 6.5, Performance Change Monitoring: Regularly monitor the performance changes of the SAR corner reflector during operation using SAR imagery.

[0088] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement steps 1, 3, 4, 5, and 6 in the above method embodiments.

[0089] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements steps 1, 3, 4, 5, and 6 in the above method embodiments.

[0090] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements steps 1, 3, 4, 5, and 6 in the above method embodiments.

[0091] It should be noted that the embodiments described in this invention are merely illustrative examples of the spirit of the invention. Those skilled in the art to which this invention pertains can make various modifications or additions to the described embodiments or use similar methods to substitute them, without departing from the spirit of the invention.

Claims

1. A method for deploying SAR corner reflectors in high mountain and canyon areas, characterized in that, Includes the following steps: Step 1: Select the deployment area of ​​SAR corner reflectors based on the continuous time period SAR image dataset and topographic elevation data DEM of the study area; Step 2: Install the target number of SAR corner reflectors within the deployment area, and optimize the position of the SAR corner reflectors by using the positioning error of the GNSS positioning coordinates of each SAR corner reflector and the planar error of the initial SAR coordinates. Step 3: Calculate and determine the initial installation angle of the SAR corner reflector based on the SAR image dataset of continuous time periods in the study area; Step 4: Optimize the initial installation angle of each SAR corner reflector based on RCS time series data through RCS analysis; Step 5: For SAR corner reflectors that do not meet the RCS threshold in Step 4, perform interference source analysis based on UAV imagery, reselect the location within the deployment area, and return to Step 2 for re-deployment. Step 6: Continuously monitor the signal strength and stability of the SAR corner reflector and develop a regular maintenance plan. The deployment area of ​​the SAR corner reflector is selected in the following way: First, based on the topographic elevation data (DEM) of the study area, areas with slopes less than a set value within the study area are selected as candidate deployment areas. Secondly, based on the continuous time-series SAR image dataset and topographic elevation data (DEM) of the study area, the MDDG model and R index of each candidate deployment area are calculated. Among all candidate deployment areas, the areas with an MDDG model greater than or equal to the set MDDG threshold and an R index value greater than or equal to the set R index threshold are selected as deployment areas. Step 5 specifically includes the following steps: based on UAV image analysis, analyze the micro-topography and ground object distribution around the corner reflector to identify local interference sources that cause multiple scattering or obstruction of radar signals; according to the identification results, reselect the location to eliminate local interference sources in the deployment area and return to step 2 for re-deployment until the RCS value and its temporal stability simultaneously meet the threshold requirements.

2. The method for deploying SAR corner reflectors in mountainous and canyon areas according to claim 1, characterized in that, Step 2 specifically includes the following steps: Step 2.1: Install SAR corner reflectors within the deployment area; Step 2.2: Measure the GNSS positioning coordinates of each SAR corner reflector. The GNSS positioning coordinates include latitude, longitude, and elevation. Step 2.3: Using the SAR image dataset, the RD equation is used to locate each SAR corner reflector, and the initial SAR coordinates of each SAR corner reflector in the SAR image coordinate system are obtained. Step 2.4: Measure the positioning error of the GNSS positioning coordinates of each SAR corner reflector. For SAR corner reflectors with positioning errors greater than the set GNSS positioning error threshold, re-deploy and install them in the deployment area until the positioning errors of the GNSS positioning coordinates of all SAR corner reflectors are within the set GNSS positioning error threshold, and re-measure the GNSS positioning coordinates of each SAR corner reflector. Step 2.5: Measure the plane error of the initial SAR coordinates based on the GNSS positioning coordinates, set the acceptance threshold for the plane error, and re-deploy the SAR corner reflectors corresponding to the initial SAR coordinates that exceed the set acceptance threshold for the plane error in the deployment area, and return to step 2.4 until all the corresponding SAR corner reflectors meet the set GNSS positioning error threshold and the set acceptance threshold for the plane error.

3. The method for deploying SAR corner reflectors in mountainous and canyon areas according to claim 2, characterized in that, The initial installation angle of the SAR corner reflector is determined in the following way: Step 3.1: Obtain the satellite's precise orbital state vector and imaging time. ; Step 3.2: Convert the initial SAR coordinates of the SAR corner reflector to geocentric coordinates. ; Step 3.3, in Real-time interpolation yields satellite position Calculate the slant distance vector ; Step 3.4: Transform the slant range vector R to the local northeast-sky coordinate system with the corresponding SAR corner reflector as the origin to obtain the local incident angle. and azimuth ,in, , , These are the slant range vectors. The celestial component, eastern component, and northern component in the local northeast celestial coordinate system; Step 3.5: Obtain the elevation angle of the SAR corner reflector. and azimuth This causes the SAR corner reflector opening to face directly towards the radar; Step 3.6: Perform grid occlusion analysis based on the terrain elevation data DEM. If the SAR corner reflector... , If the direction is obstructed, the system will rotate around the vertical axis within a set range to find the optimal path until the obstruction area is less than the set obstruction area threshold. The angle at which the index value is still greater than or equal to the set R index threshold is the initial installation angle.

4. The method for deploying SAR corner reflectors in mountainous and canyon areas according to claim 3, characterized in that, Step 4 specifically includes the following steps: Step 4.1: Calculate the RCS value and RCS time-series stability of the SAR reflector using the peak method based on the RCS time-series data, and compare them with the RCS acceptance threshold and the RCS time-series stability threshold respectively. Step 4.2: For cases where the two thresholds in Step 4.1 cannot be met simultaneously, the installation angle of the SAR corner reflector is finely adjusted by rotating it within the set range of the elevation angle or the set range of the azimuth angle, and the image is re-imaged until the two thresholds in Step 4.1 are met simultaneously.

5. The method for deploying SAR corner reflectors in mountainous and canyon areas according to claim 1, characterized in that, The SAR image dataset was acquired and processed in the following ways: First, multiple initial SAR image data from satellites over a continuous period are directly acquired to construct an initial SAR image dataset, and one of the initial SAR image data is selected as the reference image data. Secondly, the initial SAR image dataset is registered: the other SAR image data in the initial SAR image dataset, except for the reference image data, are registered to the reference image data to obtain the SAR image dataset.

6. The method for deploying SAR corner reflectors in mountainous and canyon areas according to claim 5, characterized in that, The initial SAR image dataset includes the same number of ascending and descending orbit image data; the registration method uses the amplitude cross-correlation method.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the deployment method of SAR corner reflectors for high mountain and canyon areas as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the deployment method of SAR corner reflectors for high mountain and canyon areas as described in any one of claims 1 to 6.

Images