Multimodal constellation recognition method

Abstract

This invention relates to the field of navigation technology, and more particularly to a multimodal star map recognition method. The method involves capturing a full-coverage image of the target sky region to obtain multiple star maps containing star targets. The star catalog is then divided into sub-catalogs, and both the star map and sub-catalogs are further divided into grids. The star map and sub-catalogs are encoded separately to obtain feature vectors. A coarse matching process is performed on the two feature vectors, and the sub-sky regions corresponding to the coarsely matched feature vectors are marked. A triangulation algorithm is used to finely identify the marked sub-sky regions, finding the sub-catalogs that match the current star map, and determining the navigation stars corresponding to the star targets in the current star map. The process is repeated by changing the star map until navigation stars corresponding to the star targets in all star maps are obtained. This invention combines the advantages of grid algorithms and triangulation algorithms, ensuring high recognition speed while avoiding interference from false targets, thus improving the recognition rate of star map recognition.

Description

Technical Field

[0001] This invention belongs to the field of navigation technology, and in particular relates to a multimodal star map recognition method. Background Technology

[0002] A star sensor is a high-precision, high-reliability attitude measurement instrument that measures stars. It possesses fully autonomous attitude measurement capabilities and is widely used in space payloads such as spacecraft. The main working principle of a star sensor is as follows: It uses an optical system and image sensor to image the starry sky. Through star point extraction and centroid localization, it obtains the position and brightness information of the target star in the star chart. Then, through star chart identification, it finds the corresponding navigation star in the star catalog for the target star. Finally, based on the identification results, it measures the star sensor's attitude relative to the inertial coordinate system.

[0003] Star map recognition is the most computationally complex and time-consuming step in the operation of a star sensor. Based on different algorithmic recognition methods, star map recognition methods can be mainly divided into two categories: pattern recognition methods and subgraph isomorphism methods. A typical algorithm in pattern recognition is the grid algorithm, which divides the star map into a grid and uses the binarized result of the grid as features for star map recognition. This algorithm is characterized by strong robustness to centroid positioning errors and magnitude errors, and the binarized pattern library greatly simplifies the search and matching process, resulting in fast recognition speed. However, when noise exists in the star map, the feature results after grid division are easily affected by noise, leading to a decrease in the algorithm's recognition rate. The triangle algorithm is a typical algorithm in subgraph isomorphism methods. This algorithm uses the angular distance between navigation stars as feature values ​​for matching. Each triangle requires multiple matching operations, thus the recognition speed is slower. However, this algorithm is not sensitive to noise, and the recognition rate is less affected by noise in the star map.

[0004] Analysis of the characteristics of the two types of star map recognition methods shows that each method has its own advantages and disadvantages. A star map recognition algorithm that balances recognition speed and recognition rate is an important key technology that urgently needs to be solved in the current research field of star sensors. Summary of the Invention

[0005] In view of this, the present invention aims to provide a multimodal star map recognition method, which combines the advantages of grid algorithm and triangle algorithm, uses coarse matching to ensure the recognition speed of star map recognition, and uses fine recognition to avoid interference from false targets, thus ensuring the recognition rate of star map recognition.

[0006] To achieve the above objectives, the technical solution created by this invention is implemented as follows:

[0007] A multimodal star map recognition method includes:

[0008] S1: Take a full-coverage photo of the target sky area to obtain multiple star maps containing star targets;

[0009] S2: Divide the star catalog according to the coverage area of ​​the star map to obtain sub-star catalogs; divide the star map and sub-star catalogs into grids;

[0010] S3: Using the results of step S2, encode each star map and each sub-star table separately to obtain the star map feature vector and the star table feature vector respectively;

[0011] S4: Randomly select one of the star map feature vectors obtained in step S3 and perform coarse matching with all star catalog feature vectors. Mark the sub-sky regions corresponding to the star catalog feature vectors that have successfully matched the coarse matching to obtain multiple marked sub-sky regions.

[0012] S5: Using the triangle algorithm, the multiple marked sub-sky regions obtained in step S4 are precisely identified to find the sub-sky region that matches the current star map. The navigation star in the sub-star table corresponding to the sub-sky region is the navigation star that matches the star point target in the current star map.

[0013] S6: Change the star map and repeat steps S4~S5 to obtain the corresponding navigation stars for all star targets in the star map.

[0014] Furthermore, the process of meshing the star map and sub-star table in step S2 includes: meshing the star map according to the number of star targets in the star map and the distribution of star targets in the star map; and meshing the sub-star table according to the number of navigation stars in the sub-star table and the distribution of navigation stars in the sub-star table.

[0015] Furthermore, the process of obtaining the star map feature vector in step S3 is the same as the process of obtaining the star catalog feature vector; the process of obtaining the star map feature vector includes: counting the number of star targets in each grid; and forming a vector by sequentially arranging the number of star targets in all grids from left to right and from top to bottom to obtain the star map feature vector.

[0016] Furthermore, the coarse matching process in step S4 includes: counting the number of different element values ​​at the same element position in the star map feature vector and the star catalog feature vector to obtain the number of different elements between the two feature vectors; comparing the number of different elements with a preset difference threshold: if the number of different elements is not greater than the preset difference threshold, the coarse matching between the star map feature vector and the star catalog feature vector is considered successful, otherwise the coarse matching fails.

[0017] Furthermore, in step S5, a triangle algorithm is used to perform precise identification on all marked sub-sky regions to obtain the navigation star that matches the star point target in the star table. This includes: in the star map and sub-star table corresponding to each marked sub-sky region: calculating the first angular distance between the coordinates of any two star points in the star map corresponding to each marked sub-sky region; calculating the second angular distance between any two navigation stars in the sub-star table corresponding to each marked sub-sky region; comparing the first angular distance and the second angular distance one by one. If the difference between the first angular distance and the second angular distance is less than or equal to a preset angular distance threshold, then the navigation star in the current sub-star table is considered to be the navigation star that matches the star point target in the current star map.

[0018] Furthermore, the first angular distance is calculated using the following formula:

[0019] ;

[0020] Where, d ij ω represents the first angular distance between the i-th and j-th star targets in the star map. i and ω j These represent the direction vectors of the i-th and j-th star targets, respectively.

[0021] Furthermore, the direction vector of the star target is:

[0022] ;

[0023] Where (x,y) represents the position coordinates of the star point in the star map, and f represents the shooting focal length.

[0024] Furthermore, the second angular distance is calculated using the following formula:

[0025] ;

[0026] Where, d mn μ represents the second angular distance between the m-th and n-th navigation stars in the sub-star list. m and μ n These represent the direction vectors of the m-th and n-th navigation satellites, respectively.

[0027] Furthermore, the direction vector of the navigation star is:

[0028] ;

[0029] Where (α,β) represents the right ascension and declination of the navigation star.

[0030] Compared with the prior art, the present invention can achieve the following beneficial effects:

[0031] The multimodal star map recognition method described in this invention first employs an improved grid algorithm to coarsely match the sky regions corresponding to star targets in the star table, and then uses a triangle algorithm to finely identify the filtered sky regions. This method combines the advantages of the grid algorithm and the triangle algorithm, ensuring the recognition speed of star map recognition through coarse matching, and avoiding interference from false targets through fine identification, thus guaranteeing the recognition rate of star map recognition. Attached Figure Description

[0032] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0033] Figure 1 A schematic flowchart of the multimodal star map recognition method described in the embodiments of the present invention;

[0034] Figure 2 This is a schematic diagram of the grid division described in an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not constitute a limitation thereof. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0037] The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] like Figure 1 As shown, the multimodal star map recognition method described in this embodiment of the invention includes:

[0039] S1: Perform full-coverage imaging of the target sky area to obtain multiple star images containing star targets. Since the imaging angle of the star sensor is limited, it can only capture star images of a partial sky area. Therefore, when using the star sensor to image the target sky area, it is necessary to capture star images of different regions at different locations within the target sky area until full coverage imaging of the target sky area is achieved. In this embodiment of the invention, it is assumed that a total of S star images are captured to complete the full-coverage imaging of the target sky area.

[0040] S2: Divide the star catalog according to the coverage area of ​​the star map to obtain sub-star catalogs; divide the star map and sub-star catalogs into grids.

[0041] In some embodiments, the process of meshing the star map and sub-star table includes: meshing the star map according to the number of star targets in the star map and the distribution of star targets in the star map; similarly, meshing the sub-star table according to the number of navigation stars in the sub-star table and the distribution of navigation stars in the sub-star table.

[0042] It should be noted that, as is known to those skilled in the art, when no limitation is made on the term "star catalog," it refers to the entire sky; furthermore, the coverage area of ​​a star chart is the area in the sky covered by the field of view of the star sensor used to photograph the target sky region. In this embodiment of the invention, the size of each star chart is defined as M×N. After comprehensively analyzing the number of star targets and their distribution in the star chart, the star charts are divided into... scale as Figure 2 The grid division shown results in a star map containing 64 grids. Figure 2 The star-shaped markers in the table are star point targets. The grid division method of the sub-star table is consistent with that of the star map. After comprehensively analyzing the number and distribution of navigation stars in the sub-star table, the sub-star table is divided into 8 equal parts, resulting in 64 grids.

[0043] S3: Using the results of step S2, encode each star map and each sub-star table separately to obtain the star map feature vector and the star table feature vector.

[0044] In some embodiments, the process of obtaining the star map feature vector is the same as the process of obtaining the star catalog feature vector. The process of obtaining the star map feature vector includes: counting the number of star targets in each grid; and forming a vector by sequentially arranging the number of star targets in all grids from left to right and from top to bottom to obtain the star map feature vector.

[0045] In this embodiment of the invention, the star map and sub-star table are divided into eight equal parts along the horizontal and vertical directions to obtain their respective grids. Therefore, during the process of obtaining the star map feature vector, the number p of star points in each grid is counted. ab ;Count the number p of all star targets in the grid from left to right and from top to bottom. ab By sequentially assembling the vectors, we obtain the following star map feature vectors. :

[0046] ;

[0047] Similarly, during the process of obtaining the feature vector of the satellite acquisition table, the number q of navigation satellites in each grid is counted. ab ; In a left-to-right, top-to-bottom direction, assign q values ​​to all navigation stars in the grid. ab By sequentially assembling the vectors, we obtain the following star map feature vectors. :

[0048] ;

[0049] At this point, a total of S star map eigenvectors are obtained. and S star map eigenvectors .

[0050] S4: Randomly select one of the star map feature vectors obtained in step S3 and perform a coarse match with all star catalog feature vectors. Mark the sub-sky regions corresponding to the star catalog feature vectors that successfully match the coarse match, resulting in multiple marked sub-sky regions. It can be understood that a sub-sky region is a local sky region corresponding to a sub-star catalog in the entire sky.

[0051] In some embodiments, the coarse matching process includes: counting the number of different element values ​​at the same element position in the star map feature vector and the star catalog feature vector to obtain the number of different elements between the two feature vectors; comparing the number of different elements with a preset difference threshold: if the number of different elements is not greater than the preset difference threshold, the coarse matching between the star map feature vector and the star catalog feature vector is considered successful, otherwise the coarse matching fails.

[0052] In this embodiment of the invention, one star map feature vector is arbitrarily selected from the S star map feature vectors and coarsely matched with each of the S star catalog feature vectors. The coarse matching process can be represented by the following formula:

[0053] ;

[0054] Where N represents the number of different elements, f nonzero Represents the eigenvectors of the star map Eigenvectors of star tables The number of different element values ​​at the same element position is counted.

[0055] When there are 6 false targets in the star chart ( Figure 2 When using the circular marker (in the text), the function f is used. nonzero The number of difference elements between the star map feature vector and the star catalog feature vector of the corresponding celestial region is 6. A preset difference threshold is reasonably selected based on the actual application. When the number of difference elements is not greater than the preset difference threshold, the star map feature vector and the star catalog feature vector are considered to have a successful coarse match, and the sub-celestial region corresponding to the star catalog feature vector is marked. Otherwise, the coarse match is deemed to have failed, and the sub-celestial region corresponding to the star catalog feature vector is not marked.

[0056] S5: Using the triangle algorithm, the multiple marked sub-sky regions obtained in step S4 are precisely identified to find the sub-sky region that matches the current star map. The navigation star in the sub-star table corresponding to the sub-sky region is the navigation star that matches the star point target in the current star map.

[0057] Obtain the navigation stars that match the star point targets in the star list.

[0058] In some embodiments, a triangle algorithm is used to perform precise identification on all marked sub-sky regions to obtain navigation stars in the star catalog that match the star point targets, including:

[0059] In the star chart and sub-star table corresponding to each marked sub-region:

[0060] Calculate the first angular distance between the coordinates of any two stars in the star map corresponding to each marked sub-region;

[0061] Calculate the second angular distance between any two navigation stars in the sub-star table corresponding to each marked sub-sky region;

[0062] Compare the first angular distance with the second angular distance one by one. If the difference between the first angular distance and the second angular distance is less than or equal to the preset angular distance threshold, then the navigation star in the current sub-star table is considered to be the navigation star corresponding to the star point target in the current star map.

[0063] The first angular distance is calculated using the following formula:

[0064] ;

[0065] Where, d ij ω represents the first angular distance between the i-th and j-th star targets in the star map. i and ω j Let represent the direction vectors of the i-th and j-th star targets, respectively. The direction vector of a star target is:

[0066] ;

[0067] Where (x,y) represents the position coordinates of the star point in the star map, and f represents the shooting focal length, specifically the focal length of the star sensor.

[0068] The second angular distance is calculated using the following formula:

[0069] ;

[0070] Where, d mn μ represents the second angular distance between the m-th and n-th navigation stars in the sub-star list. m and μ n Let represent the direction vectors of the m-th and n-th navigation satellites, respectively. The direction vector of the navigation satellite is:

[0071] ;

[0072] Where (α,β) represents the right ascension and declination of the navigation star.

[0073] In an embodiment of the present invention,

[0074] From the k star targets in the star map, randomly select two star targets and calculate the first angular distance to form a set. Similarly, from the sub-star table corresponding to any sub-region after filtering, the second angular distance between any two navigation stars is calculated, resulting in a total of [number missing]. The second angular distance. The first angular distance d ij and The second angular distance d mn For comparison, the angular distance satisfies the following condition:

[0075] ;

[0076] The navigation star in the current sub-star list is the matching navigation star for the target point in the current star map, thus completing the star map recognition. In the above formula, ε is a preset angular distance threshold, which should be reasonably selected according to the actual application.

[0077] S6: Change the star chart and repeat steps S4-S5 to obtain the corresponding navigation stars for all the target stars in the star chart. At this point, all navigation stars in the target sky region have been found.

[0078] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0079] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A multimodal star map recognition method, characterized in that, include: S1: Take a full-coverage photo of the target sky area to obtain multiple star maps containing star targets; S2: Divide the star catalog according to the coverage area of ​​the star map to obtain sub-star catalogs; divide the star map and sub-star catalogs into grids; S3: Using the results of step S2, encode each star map and each sub-star table separately to obtain the star map feature vector and the star table feature vector respectively; S4: Randomly select one of the star map feature vectors obtained in step S3 and perform coarse matching with all star catalog feature vectors. Mark the sub-sky regions corresponding to the star catalog feature vectors that have successfully matched the coarse matching to obtain multiple marked sub-sky regions. S5: Using the triangle algorithm, the multiple marked sub-sky regions obtained in step S4 are precisely identified to find the sub-sky region that matches the current star map. The navigation star in the sub-star table corresponding to the sub-sky region is the navigation star that matches the star point target in the current star map. S6: Change the star map and repeat steps S4~S5 to obtain the corresponding navigation stars for all star targets in the star map.

2. The multimodal star map recognition method according to claim 1, characterized in that, Step S2, which involves meshing the star map and sub-star table, includes: The star map is divided into grids based on the number of star targets and their distribution. The sub-star table is divided into grids based on the number of navigation stars in the sub-star table and their distribution within the sub-star table.

3. The multimodal star map recognition method according to claim 1, characterized in that, The process of obtaining the star map feature vector in step S3 is the same as the process of obtaining the star catalog feature vector. The process of obtaining the star map feature vector includes: counting the number of star targets in each grid; and forming a vector by arranging the number of star targets in all grids in order from left to right and from top to bottom to obtain the star map feature vector.

4. The multimodal star map recognition method according to claim 1, characterized in that, The coarse matching process in step S4 includes: The number of different element values ​​at the same element position in the star map feature vector and the star catalog feature vector is counted to obtain the number of different elements between the two feature vectors. Compare the number of differing elements with a preset difference threshold: if the number of differing elements is not greater than the preset difference threshold, the coarse match between the star map feature vector and the star catalog feature vector is considered successful; otherwise, the coarse match fails.

5. The multimodal star map recognition method according to claim 1, characterized in that, In step S5, the triangle algorithm is used to perform precise identification on all marked sub-sky regions to obtain the navigation stars that match the star point targets in the star catalog, including: In the star chart and sub-star table corresponding to each marked sub-region: Calculate the first angular distance between the coordinates of any two stars in the star map corresponding to each marked sub-region; Calculate the second angular distance between any two navigation stars in the sub-star table corresponding to each marked sub-sky region; Compare the first angular distance with the second angular distance one by one. If the difference between the first angular distance and the second angular distance is less than or equal to the preset angular distance threshold, then the navigation star in the current sub-star table is considered to be the navigation star corresponding to the star point target in the current star map.

6. The multimodal star map recognition method according to claim 5, characterized in that, The first angular distance is calculated using the following formula: ； Where, d ij ω represents the first angular distance between the i-th and j-th star targets in the star map. i and ω j These represent the direction vectors of the i-th and j-th star targets, respectively.

7. The multimodal star map recognition method according to claim 6, characterized in that, The direction vector of the star target is: ； Where (x,y) represents the position coordinates of the star point in the star map, and f represents the shooting focal length.

8. The multimodal star map recognition method according to claim 4, characterized in that, The second angular distance is calculated using the following formula: ； Where, d mn μ represents the second angular distance between the m-th and n-th navigation stars in the sub-star list. m and μ n These represent the direction vectors of the m-th and n-th navigation satellites, respectively.

9. The multimodal star map recognition method according to claim 8, characterized in that, The direction vector of the navigation star is: ； Where (α,β) represents the right ascension and declination of the navigation star.

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