MVDT-SI: A Multi-View Double-Triangle Algorithm for Star Identification

Sun, Lijian; Zhou, Yun

doi:10.3390/s20113027

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…Since the first star identification algorithm was proposed in 1979, many star identification algorithms have been designed to estimate attitude data without any prior attitude data, and several algorithms [ 2 , 3 , 4 , 5 ] have been verified in engineering practice for star sensing. The existing star identification algorithms are generally divided into three categories: subgraph isomorphism algorithms [ 6 , 7 ], artificial intelligence algorithms [ 8 ], and pattern recognition algorithms [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Grid Pattern Star Identification Algorithm Based on Multi-Calibration Star Verification

Shen,

Ma,

Gao

et al. 2024

Sensors

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In order to solve the star identification problem in the lost space mode for scientific cameras with small fields of view and higher instruction magnitudes, this paper proposes a star identification algorithm based on a hybrid grid pattern. The application of a hybrid pattern generated by multi-calibration stars in the initial matching enables the position distribution features of neighboring stars around the main star to be more comprehensively described and avoids the interference of position noise and magnitude noise as much as possible. Moreover, calibration star filtering is adopted to eliminate incorrect candidates and pick the true matched navigation star from candidate stars in the initial match. Then, the reference star image is utilized to efficiently verify and determine the final identification results of the algorithm via the nearest principle. The performance of the proposed algorithm in simulation experiments shows that, when the position noise is 2 pixels, the identification rate of the algorithm is 96.43%, which is higher than that of the optimized grid algorithm by 2.21% and the grid algorithm by 4.05%; when the magnitude noise is 0.3 mag, the star identification rate of the algorithm is 96.45%, which is superior to the optimized grid algorithm by 2.03% and to the grid algorithm by 3.82%. In addition, in the actual star image test, star magnitude values of ≤12 mag can be successfully identified using the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Hybrid Grid Pattern Star Identification Algorithm Based on Multi-Calibration Star Verification

Shen,

Ma,

Gao

et al. 2024

Sensors

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“…Zhao et al [18]acquired better identification accuracy by using the Karhunen-Loève (K-L) transform to form star rows, which display the geometric distribution of each star in the neighborhood. Sun et al [19]proposed a multi-viewpoint double triangle star identification algorithm based on the triangle algorithm. This algorithm constructs the double triangle feature of the star point using angle and distance information, exhibiting strong anti-interference ability against false stars and missing stars, but its robustness against noise in the star position is not well evaluated.…”

Section: Introductionmentioning

confidence: 99%

Star Identification Algorithm Based on Multi-Dimensional Features and Multi-Layered Joint Screening for Star Sensors

et al. 2023

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The algorithm for star identification is a crucial technology for determining the orientation of spacecraft using star sensors. Traditional star identification algorithms achieve matching by seeking a unique or a few optimal solutions. However, in high-noise environments, some solutions may be lost, which could result in matching failure. A new lost-in-space architecture algorithm aimed at rapid identification under high star position noise conditions by directly using star positions for final matching is proposed in this paper. The main idea of this algorithm is to construct sufficiently redundant navigation triangles, fully utilizing the physical relationships of the features and forming a screening method from high to low dimensions and from loose to strict. During identification, a multi-layer joint screening matching method is adopted to screen triangles as a whole, narrowing the range of matches quickly while retaining error tolerance. In a series of simulation experiments, this algorithm achieved identification rates of 99.51%, 99.06%, and 98.42% for 2.0 pixel star position noise, 1.0 Mv star magnitude noise, and 5 false stars, respectively. In terms of practical application, all 1000 star images taken by the star sensor in orbit have been successfully identified, and it only takes 28ms to identify each image. In addition, star images taken by consumer-grade cameras from the ground also show that the algorithm has strong robustness to star position noise, magnitude error and false star interference in more severe environments. This method provides partial algorithmic reference for non-specialized design of star sensors for low-cost, large-scale satellites in the future.INDEX TERMS Multi-dimensional features, multi-layered joint screening, star identification, star sensor, star tracker.

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“…Then, a feature subgraph unit for the matching process is built to search for similar subgraphs stored in the database, including entire sky graphs. The triangle algorithm [21][22][23], pyramid algorithm [24], and geometric voting algorithm [25] are typical star-identification methods based on subgraph isomorphism, in which polygons, pyramids, and star pairs are used as feature subgraph units. The triangle algorithm usually selects at least three true stars to generate some triangles and uses the angular distance or intersection angle of the graph sides as match elements.…”

Section: Introductionmentioning

confidence: 99%

A Star-Identification Algorithm Based on Global Multi-Triangle Voting

Yuan

Zhu

et al. 2022

Applied Sciences

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A star-identification algorithm aimed at identifying imaged stars in a “lost in space” scene, named the global multi-triangle voting algorithm (GMTV), is presented in this paper. There are two core parts included in the proposed algorithm: in the initial match part, triangle feature units are treated as vote units to find the initial match relationship via matching vote units and counting the vote number of each catalog star. During this step, the principal component analysis (PCA) method is implemented to reduce feature dimensions, and a two-dimension lookup table and fuzzy match strategy are utilized to promote database searching efficiency and noise tolerance. After acquiring the initial match results, a verification part is implemented to filter potential errors from initial candidates by the largest cluster method and output the final identification results. The proposed algorithm achieves a 98.6% identification rate with 2.0 pixels position noise and exhibits more robustness to position noise, magnitude noise, and false stars of different levels than the two reference algorithms used in simulations. In addition, our algorithm’s real-time performance is better than reference algorithms, but it requires a larger database.

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MVDT-SI: A Multi-View Double-Triangle Algorithm for Star Identification

Cited by 7 publications

References 26 publications

Hybrid Grid Pattern Star Identification Algorithm Based on Multi-Calibration Star Verification

Hybrid Grid Pattern Star Identification Algorithm Based on Multi-Calibration Star Verification

Star Identification Algorithm Based on Multi-Dimensional Features and Multi-Layered Joint Screening for Star Sensors

A Star-Identification Algorithm Based on Global Multi-Triangle Voting

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