Target identification method suitable for space rendezvous and docking

Abstract

The invention discloses a target identification method suitable for space rendezvous and docking, and solves the problems that an existing space rendezvous and docking target identification method is not high in identification speed and not accurate in identification result. According to the method, the data driving capability of the light-weight neural network is utilized to obtain the image of which the main body is an elliptical cross configuration target, Gaussian filtering is utilized to remove noise of the image, then LSD algorithm line segment detection is utilized, the line segments are grouped by utilizing the angle difference between the line segments, and ellipses with overlarge errors are effectively eliminated by utilizing the light-weight neural network. The calculation amount is reduced, the initial ellipse set is obtained by adopting the elliptic arc support algorithm, the optimal candidate ellipse set is generated after the initial ellipse set is clustered and fitted, the method has the advantages of being concise, efficient and good in repeatability, the light-weight neural network based on data driving is fused, the calculation complexity is obviously reduced, and the method is suitable for large-scale popularization and application. The ellipse detection accuracy is improved, and the target recognition speed and accuracy are correspondingly improved.

Description

Technical Field

[0001] The invention relates to a target recognition method for space rendezvous and docking, and in particular to a target recognition method suitable for space rendezvous and docking. Background Art

[0002] Identifying space targets through a monocular camera is very common in rendezvous and docking tasks. Fast and accurate target recognition plays a key role in the success of rendezvous and docking. In existing space rendezvous and docking target recognition methods, the cooperative target is usually designed as an elliptical cross target. Taking the Hough transform ellipse detection algorithm as an example, the algorithm can effectively detect the ellipse by checking all the feature point pairs on the main axis and using the Hough transform to obtain the minor axis.

[0003] However, since this type of algorithm needs to use feature points as calculation units and calculate the distance between feature point pairs individually, it consumes a lot of memory, making the space rendezvous and docking target recognition slow and the recognition results inaccurate. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the existing space rendezvous and docking target recognition methods have slow recognition speed and inaccurate recognition results, and to provide a target recognition method suitable for space rendezvous and docking.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: The present invention provides a target recognition method suitable for space rendezvous and docking, which is special in that it comprises the following steps: Step 1, designing an elliptical cross configuration target and installing it at a relative position of the docking mechanism, imaging the elliptical cross configuration target through a camera, and obtaining an image of the elliptical cross configuration target; Step 2: Using a trained lightweight neural network to detect the initial position frame of the elliptical cross configuration target in the elliptical cross configuration target image, and obtaining an image to be detected whose main body is the elliptical cross configuration target and whose size is twice that of the initial position frame; Step 3: extract edge information from the image to be detected to obtain an edge image composed of multiple line segments; Step 4: Use the LSD algorithm to perform line segment detection on the edge image to obtain a line segment set, group the line segments based on their angle differences, and output the line segment sets of each group; Step 5, traverse each line segment in the line segment set of each group, and extract an initial ellipse set from the line segment set of each group in combination with the ellipse arc support algorithm; Step 6: Use the trained lightweight neural network to detect the image to be detected, obtain the real-time position frame of the elliptical cross configuration target, obtain the inscribed ellipse according to the real-time position frame, remove the ellipse with a large deviation from the inscribed ellipse in the initial ellipse set, and obtain the updated initial ellipse set; Step 7: average the parameters of the real-time position frame and the ellipse in the updated initial ellipse set to obtain the final candidate ellipse, and use the final candidate ellipse to identify the elliptical cross configuration target of the docking mechanism to complete the target identification of the space rendezvous and docking.

[0006] Furthermore, the shape of the elliptical cross configuration target in step 1 is elliptical, the main body color is black, and a plurality of white square grids are arranged at intervals on the outer edge, which can make the detection effect more significant, and the middle part is a white cross shape.

[0007] Furthermore, the lightweight neural network in step 2 is a YOLO_X or SSD lightweight neural network.

[0008] Furthermore, the trained lightweight neural network described in step 2 is: using an elliptical cross configuration target image containing no less than 500 images, and using the initial position box in each elliptical cross configuration target image as a label, using the elliptical cross configuration target image and the corresponding label as training samples, making a sample training set, and training the lightweight neural network to obtain a lightweight neural network.

[0009] Furthermore, step 3 is specifically as follows: using Gaussian filtering to filter out noise from the image to be detected, performing gradient calculation and non-maximum suppression on the image, and then using the Sobel operator or the Canny operator to extract edge information to obtain an edge image composed of multiple line segments.

[0010] Furthermore, the specific process of step 4 is as follows: Use the LSD algorithm to detect line segments in the edge image, obtain the endpoint coordinate set of the line segments, calculate the direction angle of each line segment relative to the horizontal line, and normalize it to between 0 and 180 degrees. According to the normalized angle, use the full-link method of hierarchical clustering, group them with X degrees as the threshold, and the angle difference between any two line segments in the same group does not exceed X degrees. Output the set and number of line segments in each group.

[0011] Furthermore, in step 4, the value of X is 14-18, and the full-link method using hierarchical clustering is specifically grouped with X degree as the threshold: Step a: The direction angle of each line segment in the edge image relative to the horizontal line is ,like If is regular, the value remains unchanged. If it is a negative value, add 180 degrees, based on the direction from the starting point to the end point of the line segment, and number each line segment 1, 2, 3, ..., N , N The value is 7-10; Step b: Calculate the angle difference between all line segments ,in Indicates i The direction angle of the line segment relative to the horizontal line, Indicates j The direction angle of each line segment relative to the horizontal line; i and j The value range is 1, 2, 3, ..., N ; Step c: Treat each line segment as an independent cluster C k ={Line segment k}, k is the number of the line segment, k =1,2,3,..., N , forming the initial cluster set ; Step d: Perform full-link hierarchical merging of independent clusters in the initial cluster set C with degree X as the threshold to obtain a new cluster set; Step e: Output each new cluster in the new cluster set as a set of line segments for each group.

[0012] Furthermore, the specific process of step d is: ① Traverse the current initial cluster set C and take any two clusters from the initial cluster set C C a and C b ; ② Define the inter-cluster distance as the maximum angle difference between the line segments formed by the line segments in the two clusters, and calculate the inter-cluster distance It is expressed as: ; ③ If satisfied ,merge and For new cluster , update the initial cluster set , return to ①; If not satisfied , select a cluster from the current initial cluster set C C c Replacing a Cluster C a and C b Any one of them; return to ② until no one is satisfied The cluster pairs can be merged to obtain a new cluster set.

[0013] Furthermore, the specific process of step 5 is as follows: Step 5.1, for each line segment in the group, extract the endpoints and sample several intermediate points at equal intervals along the line segment to form a sampling point coordinate set P of the group, and use the RANSAC strategy to perform robust fitting on the sampling point coordinate set P; Step 5.2: Randomly select 5 points from the fitting results of step 5.1 to generate candidate ellipses; calculate the number of points whose distances from the 5 points to the candidate ellipses are less than the threshold value τ; Step 5.3, return to step 5.2, randomly select 5 points to generate candidate ellipses again, and calculate the number of points whose distances from the 5 points to the candidate ellipse are less than the threshold τ. After 5-10 calculations, retain the candidate ellipse with the largest number of points less than the threshold τ; Step 5.4: Calculate the geometric distances from each line segment sampling point to the candidate ellipse retained in step 5.3. If the average geometric distance is less than the threshold value τ, it is considered supported, otherwise it is not supported. At the same time, calculate the angle between each line segment and the tangent line of the corresponding point of the candidate ellipse retained in step 5.2. If it is less than the angle threshold θ, it is considered supported, otherwise it is not supported. The line segment that satisfies both of the above supports is regarded as the enhanced support line segment in the group. Step 5.5: Count the number of enhanced support segments. If it exceeds the threshold N_min, it is considered valid and executes step 5.6. Otherwise, it is considered invalid and returns to step 5.2. Step 5.6, taking the candidate ellipses retained by each group in step 5.3 as the optimal candidate ellipse set; Step 5.7: Use the optimal candidate ellipse set as the initial ellipse set.

[0014] Furthermore, the elimination of ellipses with large deviations from the inscribed ellipse in the initial ellipse set in step 6 is specifically as follows: respectively calculating the distance between the center point of the inscribed ellipse and the center point of the ellipse in the initial ellipse set, and the angle formed by the line connecting the center point of the inscribed ellipse and the center point of the ellipse in the initial ellipse set and the horizontal direction, eliminating ellipses whose center point distance is greater than 20% of the major axis of the inscribed ellipse and ellipses whose angle is not less than 5 degrees, and updating the initial ellipse set.

[0015] Beneficial effects of the present invention: In a target recognition method suitable for space rendezvous and docking provided by the present invention, after designing, installing and imaging an elliptical cross configuration target, a fast and accurate ellipse detection algorithm is used to effectively identify the target, providing support for the smooth realization of rendezvous and docking.

[0016] In a target recognition method suitable for space rendezvous and docking provided by the present invention, an image of a target with an elliptical cross configuration is obtained, and after removing noise from the image using Gaussian filtering, the edge image can be effectively extracted; after detecting line segments using the LSD algorithm, the line segments are grouped using the angle differences between the line segments, and a lightweight neural network is used to effectively eliminate ellipses with excessive errors, reducing the amount of calculation for subsequent processing, and an elliptical arc support algorithm is used to obtain an initial ellipse set, and the initial ellipse set is clustered and fitted to generate an optimal candidate ellipse set, and the docking mechanism is identified using the final candidate ellipse. The method proposed by the present invention is concise, efficient, and has good repeatability. It integrates a lightweight neural network based on data drive. Compared with the traditional Hough transform-based ellipse detection algorithm, the computational complexity is significantly reduced, the accuracy of ellipse detection is improved, and the speed and accuracy of target recognition are correspondingly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of an embodiment of a target identification method applicable to space rendezvous and docking of the present invention; Figure 2 A schematic diagram of a target in a simulated elliptical cross configuration in an embodiment of a target identification method suitable for space rendezvous and docking of the present invention; Figure 3 This is a rendering of detecting a simulated elliptical cross configuration target in an embodiment of a target recognition method suitable for space rendezvous and docking of the present invention; Figure 4 It is a real elliptical cross configuration target image in an embodiment of a target recognition method suitable for space rendezvous and docking of the present invention; Figure 5 This is a rendering of the effect of detecting a real elliptical cross configuration target in an embodiment of a target recognition method suitable for space rendezvous and docking of the present invention; Figure 6 It is a schematic diagram of the results of detecting multiple ellipses in an embodiment of a target recognition method suitable for space rendezvous and docking of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] This embodiment provides a target recognition method suitable for space rendezvous and docking, such as Figure 1 As shown, the following steps are included: Step 1, designing an elliptical cross configuration target, the shape of the elliptical cross configuration target is elliptical, the main body color is black, a plurality of white square grids are arranged at intervals on the outer edge, and the middle part is a white cross shape; and installing the elliptical cross configuration target at a relative position of the docking mechanism, imaging the elliptical cross configuration target through a camera, and obtaining an image of the elliptical cross configuration target; Step 2: using an elliptical cross configuration target image containing no less than 500 images, and taking the initial position frame in each elliptical cross configuration target image as a label, taking the elliptical cross configuration target image and the corresponding label as a training sample, making a sample training set, training the YOLO_X or SSD lightweight neural network to obtain a trained YOLO_X or SSD lightweight neural network; using the trained YOLO_X or SSD lightweight neural network to detect the initial position frame of the elliptical cross configuration target in the elliptical cross configuration target image, and obtaining an image to be detected whose main body is the elliptical cross configuration target and whose size is twice that of the initial position frame; Step 3: Use Gaussian filtering to filter out noise from the image to be detected, perform gradient calculation and non-maximum suppression on the image, and then use the Sobel operator or Canny operator to extract edge information to obtain an edge image composed of multiple line segments; Step 4: Use the LSD algorithm (line segmentation detection algorithm) to detect line segments in the edge image, obtain the endpoint coordinate set of the line segments, calculate the direction angle of each line segment relative to the horizontal line, and normalize it to between 0 and 180 degrees. According to the normalized angle, use the full-link method of hierarchical clustering, group them with 15 degrees as the threshold, and the angle difference between any two line segments in the same group does not exceed 15 degrees. Output the line segment set and quantity of each group.

[0020] In this embodiment, the full-link method of hierarchical clustering is used, and the grouping is performed with 15 degrees as the threshold: Step a: The direction angle of each line segment in the edge image relative to the horizontal line is ,like If is regular, the value remains unchanged. If it is a negative value, add 180 degrees, based on the direction from the starting point to the end point of the line segment, and number each line segment 1, 2, 3, ..., 10; Step b: Calculate the angle difference between all line segments ,in Indicates i The direction angle of the line segment relative to the horizontal line, Indicates j The direction angle of each line segment relative to the horizontal line; i and j The value range is 1, 2, 3, ..., 10; Step c: Treat each line segment as an independent clusterC k ={Line segment k}, k is the number of the line segment, k =1,2,3,...,10, forming the initial cluster set ; Step d: The independent clusters in the initial cluster set C are fully linked hierarchically merged with a threshold of 15 degrees to obtain a new cluster set; the specific process is as follows: ① Traverse the current initial cluster set C and take any two clusters from the initial cluster set C C a and C b ; ② Define the inter-cluster distance as the maximum angle difference between the line segments formed by the line segments in the two clusters, and calculate the inter-cluster distance It is expressed as: ; ③ If satisfied ,merge and For new cluster , update the initial cluster set , return to ①; If not satisfied , select a cluster from the current initial cluster set C C c Replacing a Cluster C a and C b Any one of them; return to ② until no one is satisfied The cluster pairs can be merged to obtain a new cluster set.

[0021] Step e: Output each new cluster in the new cluster set as the line segment set of each group, and the number of line segments in the new cluster is used as the number of line segments of each group.

[0022] Step 5: traverse the line segments of each group, and extract the initial ellipse set from the grouped line segments in combination with the ellipse arc support algorithm; the specific process is as follows: Step 5.1, for each line segment in the group, extract the endpoints and sample several intermediate points at equal intervals along the line segment to form a sampling point coordinate set P of the group, and use the RANSAC strategy to perform robust fitting on the sampling point coordinate set P; Step 5.2: Randomly select 5 points from the fitting results of step 5.1 to generate candidate ellipses; calculate the number of points whose distances from the 5 points to the candidate ellipses are less than the threshold value τ; Step 5.3, return to step 5.2, randomly select 5 points to generate candidate ellipses again, and calculate the number of points whose distances from the 5 points to the candidate ellipse are less than the threshold τ. After 5-10 calculations, retain the candidate ellipse with the largest number of points less than the threshold τ; Step 5.4: Calculate the geometric distances from each line segment sampling point to the candidate ellipse retained in step 5.3. If the average geometric distance is less than the threshold value τ, it is considered supported, otherwise it is not supported. At the same time, calculate the angle between each line segment and the tangent line of the corresponding point of the candidate ellipse retained in step 5.2. If it is less than the angle threshold θ, it is considered supported, otherwise it is not supported. The line segment that satisfies both of the above supports is regarded as the enhanced support line segment in the group. Step 5.5: Count the number of enhanced support segments. If it exceeds the threshold N_min, it is considered valid and executes step 5.6. Otherwise, it is considered invalid and returns to step 5.2. Step 5.6, taking the candidate ellipses retained by each group in step 5.3 as the optimal candidate ellipse set; Step 5.7: Use the optimal candidate ellipse set as the initial ellipse set.

[0023] Step 6: Use the trained lightweight neural network to detect the image to be detected, obtain the real-time position frame of the elliptical cross configuration target and the inscribed ellipse of the real-time position frame, calculate the distance between the center point of the inscribed ellipse and the center point of the ellipse in the initial ellipse set, and the angle formed by the line connecting the center point of the inscribed ellipse and the center point of the ellipse in the initial ellipse set and the horizontal direction, eliminate the ellipse whose center point distance is greater than 20% of the major axis of the inscribed ellipse and the ellipse whose angle is not less than 5 degrees, and obtain the updated initial ellipse set; Step 7: The real-time position frame and the parameters of the ellipses in the updated initial ellipse set are averaged to obtain a final candidate ellipse, and the final candidate ellipse is used to identify the elliptical cross configuration target (target) of the docking mechanism to complete the target identification of the space rendezvous and docking.

[0024] Figure 2 is the elliptical cross configuration target simulated in this embodiment, Figure 3 This is a rendering of the target recognition method of this embodiment being used to recognize a simulated elliptical cross configuration target. In a dynamic video of a simulated elliptical cross configuration target containing 500 frames, the recognition recall rate reaches 100%, the recognition accuracy reaches 98%, and the number of frames per second reaches 20. Figure 4 It is a publicly available real elliptical cross configuration target. Figure 5 This is the effect of using the method of the present invention to identify real targets. In a dynamic video of a real elliptical cross configuration target containing 1513 frames, the recognition recall rate reaches 99%, the recognition accuracy reaches 97%, and the number of frames per second reaches 26. In the traditional Hough transform ellipse detection algorithm, the recognition recall rate is 95%, the recognition accuracy is 87%, and the number of frames per second is 0.7.

[0025] Figure 6 This is a schematic diagram of the results of detecting multiple ellipses in an embodiment of a target recognition method suitable for space rendezvous and docking of the present invention. Figure 6 The middle red represents the detected ellipse, which shows that the target recognition method of the present invention is suitable for target recognition in multi-ellipse scenes.

[0026] Through the verification and implementation of the method of the present invention by simulating target dynamic videos and real target dynamic videos, it is found that the target recognition method proposed in the present invention can realize fast and accurate recognition of space rendezvous and docking targets compared with the traditional Hough transform-based ellipse detection algorithm, and plays a supporting role in the completion of the rendezvous and docking task.

[0027] The above description is only a specific embodiment of the present invention and a comparison of the effects of the specific embodiments, but the protection scope of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A target recognition method suitable for space rendezvous and docking, characterized in that: The following steps are involved: Step 1, designing an elliptical cross configuration target and installing it at a relative position of the docking mechanism, imaging the elliptical cross configuration target through a camera, and obtaining an image of the elliptical cross configuration target; Step 2: Using a trained lightweight neural network to detect the initial position frame of the elliptical cross configuration target in the elliptical cross configuration target image, and obtaining an image to be detected whose main body is the elliptical cross configuration target and whose size is twice that of the initial position frame; Step 3: extract edge information from the image to be detected to obtain an edge image composed of multiple line segments; Step 4: Use the LSD algorithm to perform line segment detection on the edge image to obtain a line segment set, group the line segments based on their angle differences, and output the line segment sets of each group; Step 5, traverse each line segment in the line segment set of each group, and extract an initial ellipse set from the line segment set of each group in combination with the ellipse arc support algorithm; Step 6: Use the trained lightweight neural network to detect the image to be detected, obtain the real-time position frame of the elliptical cross configuration target, obtain the inscribed ellipse according to the real-time position frame, remove the ellipse with a large deviation from the inscribed ellipse in the initial ellipse set, and obtain the updated initial ellipse set; Step 7: average the parameters of the real-time position frame and the ellipse in the updated initial ellipse set to obtain the final candidate ellipse, and use the final candidate ellipse to identify the elliptical cross configuration target of the docking mechanism to complete the target identification of the space rendezvous and docking.

2. According to claim 1, a target recognition method suitable for space rendezvous and docking is characterized in that: The shape of the elliptical cross configuration target described in step 1 is elliptical, the main body color is black, a plurality of white square grids are arranged at intervals on the outer edge, and the middle part is a white cross shape.

3. According to claim 1, a target recognition method suitable for space rendezvous and docking is characterized in that: The lightweight neural network described in step 2 is a YOLO_X or SSD lightweight neural network.

4. A target recognition method suitable for space rendezvous and docking according to claim 3, characterized in that: The trained lightweight neural network described in step 2 is: using an elliptical cross configuration target image containing no less than 500 images, and using the initial position box in each elliptical cross configuration target image as a label, using the elliptical cross configuration target image and the corresponding label as training samples, making a sample training set, and training the lightweight neural network to obtain a lightweight neural network.

5. The target recognition method suitable for space rendezvous and docking according to claim 1, characterized in that: Step 3 is as follows: use Gaussian filtering to filter out noise from the image to be detected, perform gradient calculation and non-maximum suppression on the image, and then use the Sobel operator or Canny operator to extract edge information to obtain an edge image composed of multiple line segments.

6. A target recognition method suitable for space rendezvous and docking according to claim 3, characterized in that: The specific process of step 4 is as follows: Use the LSD algorithm to detect line segments in the edge image, obtain the endpoint coordinate set of the line segments, calculate the direction angle of each line segment relative to the horizontal line, and normalize it to between 0 and 180 degrees. According to the normalized angle, use the full-link method of hierarchical clustering, group them with X degrees as the threshold, and the angle difference between any two line segments in the same group does not exceed X degrees. Output the line segment set and number of each group; the value range of X is 14-18.

7. A target recognition method suitable for space rendezvous and docking according to claim 6, characterized in that: In step 4, the full-link method using hierarchical clustering is used to group with X degree as the threshold, specifically: Step a: The direction angle of each line segment in the edge image relative to the horizontal line is ,like If is regular, the value remains unchanged. If it is a negative value, add 180 degrees, based on the direction from the starting point to the end point of the line segment, and number each line segment 1, 2, 3, ..., N , N The value is 7-10; Step b: Calculate the angle difference between all line segments ,in Indicates i The direction angle of the line segment relative to the horizontal line, Indicates j The direction angle of each line segment relative to the horizontal line; i and j The value range is 1, 2, 3, ..., N ; Step c: Treat each line segment as an independent cluster C k ={Line segment k }, k is the line segment number, k =1,2,3,..., N , forming the initial cluster set ; Step d: Perform full-link hierarchical merging of independent clusters in the initial cluster set C with degree X as the threshold to obtain a new cluster set; Step e: Output each new cluster in the new cluster set as a set of line segments for each group.

8. A target recognition method suitable for space rendezvous and docking according to claim 7, characterized in that: The specific process of step d is: ① Traverse the current initial cluster set C and take any two clusters from the initial cluster set C C a and C b ; ② Define the inter-cluster distance as the maximum angle difference between the line segments formed by the line segments in the two clusters, and calculate the inter-cluster distance It is expressed as: ； ③If satisfied ,merge and For new cluster , update the initial cluster set , return to ①; If not satisfied , select a cluster from the current initial cluster set C C c Replacing a Cluster C a and C b Any of; Return to ② until no more is satisfied The cluster pairs can be merged to obtain a new cluster set.

9. The target recognition method suitable for space rendezvous and docking according to claim 1, characterized in that: The specific process of step 5 is as follows: Step 5.1, for each line segment in the group, extract the endpoints and sample several intermediate points at equal intervals along the line segment to form a sampling point coordinate set P of the group, and use the RANSAC strategy to perform robust fitting on the sampling point coordinate set P; Step 5.2: Randomly select 5 points from the fitting results of step 5.1 to generate candidate ellipses; calculate the number of points whose distances from the 5 points to the candidate ellipses are less than the threshold value τ; Step 5.3, return to step 5.2, randomly select 5 points to generate candidate ellipses again, and calculate the number of points whose distances from the 5 points to the candidate ellipse are less than the threshold τ. After 5-10 calculations, retain the candidate ellipse with the largest number of points less than the threshold τ; Step 5.4: Calculate the geometric distances from each line segment sampling point to the candidate ellipse retained in step 5.

3. If the average geometric distance is less than the threshold value τ, it is considered supported, otherwise it is not supported. At the same time, calculate the angle between each line segment and the tangent line of the corresponding point of the candidate ellipse retained in step 5.

2. If it is less than the angle threshold θ, it is considered supported, otherwise it is not supported. The line segment that satisfies both of the above supports is regarded as the enhanced support line segment in the group. Step 5.5: Count the number of enhanced support segments. If it exceeds the threshold N_min, it is considered valid and executes step 5.

6. Otherwise, it is considered invalid and returns to step 5.

2. Step 5.6, taking the candidate ellipses retained by each group in step 5.3 as the optimal candidate ellipse set; Step 5.7: Use the optimal candidate ellipse set as the initial ellipse set.

10. The target recognition method suitable for space rendezvous and docking according to claim 1, characterized in that: The specific method of eliminating ellipses with large deviations from the inscribed ellipse in the initial ellipse set in step 6 is as follows: respectively calculate the distance between the center point of the inscribed ellipse and the center point of the ellipse in the initial ellipse set, and the angle formed by the line connecting the center point of the inscribed ellipse and the center point of the ellipse in the initial ellipse set and the horizontal direction, eliminate ellipses whose center point distance is greater than 20% of the major axis of the inscribed ellipse and ellipses whose angle is not less than 5 degrees, and update the initial ellipse set.

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