The lost-in-space star identification algorithm is able to identify stars without a priori attitude information and is arguably the most critical component of a star sensor system. In this paper, the 2009 survey by Spratling and Mortari is extended and recent lost-in-space star identification algorithms are surveyed. The covered literature is a qualitative representation of the current research in the field. A taxonomy of these algorithms based on their feature extraction method is defined. Furthermore, we show that in current literature the comparison of these algorithms can produce inconsistent conclusions. In order to mitigate these inconsistencies, this paper lists the considerations related to the relative performance evaluation of these algorithms using simulation.

https://www.mdpi.com/1424-8220/20/9/2579/pdf

A Survey of Lost-in-Space Star Identification Algorithms Since 2009

A star identification algorithm based on simplest general subgraph

Aimed at solving the “lost-in-space” (LIS) problem, a robust star pattern identification algorithm is presented. The neighbor star pattern is introduced to increase separating ability and stabilization of star patterns and reduce the effects of magnitude noise. Redundant coding for radial and neighbor star patterns is proposed to more effectively remove the effects of position noise. Binary bit strings are used for storing the redundant code to reduce the size of the on-board database. A similar score measurement is proposed on the basis of shift and logical AND operations to quickly implement the star's initial match. The final match results are obtained using a strategy of searching for the longest matching chain. The algorithm is evaluated with synthesized and real star images. The identification rates of this algorithm are 97.98% with standard deviations of position noises of 1 pixel and 94.96% with standard deviations of magnitude noises of 0.646 Mv. Moreover, the identification rate is 77.12% when there are only four stars in the field of view (FOV). The algorithm obtains an average identification rate of 96.96% from real star images.

Redundant-Coded Radial and Neighbor Star Pattern Identification

In the previous study, there were a few direct star identification (star-ID) algorithms for smearing star image. An end-to-end star-ID algorithm is proposed in this article, to directly identify the smearing image from star sensors with fast attitude maneuvering. Combined with convolutional neural networks and the self-attention mechanism of transformer encoder, the algorithm can effectively classify the smearing image and identify the star. Through feature extraction and position encoding, neural networks learn the position of stars to generate semantic information and realize the end-to-end identification for the smearing star image. The algorithm can also solve the problem of low identification rate due to smearing of long exposure time for images. A dataset of dynamic stars is analyzed and constructed based on multiple angular velocities. Experiment results show that, compared with representative algorithms, the identification rate of the proposed algorithm is improved at high angular velocities. When the three-axis angular velocity is 10°/s, the rate is still 60.4%. At the same time, the proposed algorithm has good robustness to position noise and magnitude noise.

An End-to-End Identification Algorithm for Smearing Star Image

Star identification is a key technology in the research of star sensors. As a classical star pattern for identification, the radial pattern of a chosen star represents the geometric distribution of neighboring stars. Previous researches usually combine the rotation-invariant radial pattern with other less reliable patterns like cyclic pattern to identify the chosen star. Seldom studies use the radial pattern for direct identification as it is sensitive to noise and inefficient to eliminate spurious matches. To solve the problem, a novel star identification algorithm based on the recommended radial pattern was developed. The algorithm introduced the Apriori algorithm to mine the association rules between a star and neighboring stars, recommend the companion stars, and generate the recommended radial pattern. Based on the association rules, the algorithm integrated the matching results of a star and recommended companion stars to identify the chosen star. The simulation test and night sky image test both show that the proposed algorithm is robust to position noise, brightness noise, and false stars. The novel matching strategy is not limited to the radial pattern algorithms, it provides a new idea for all the star pattern algorithms.

A Star Identification Algorithm Based on Recommended Radial Pattern

A novel star identification network (RPNet) based on representation learning is proposed in this paper. Unlike other pattern-based stars identification algorithms, the RPNet does not require the creation of an elaborate pattern, nor does it need to search among patterns. Instead, a star pattern generator (SPG) in the RPNet helps in finding the best pattern that can distinguish different stars clearly. A star pattern classifier (SPC) in the RPNet is utilized to recognize the pattern generated before. The simulations show that the RPNet is extremely robust toward star position noise, star magnitude noise, and false stars. The performance on simulation images outperforms almost all other pattern-based stars identification algorithms. On average, it achieves an identification rate of 99.23% in simulated star images. The identification rate on real star images is higher than 98%. Moreover, the algorithm achieves this performance with lesser memory and faster speed compared to polygon algorithms.

/pdf/rpnet-a-representation-learning-based-star-identification-37b7rcbdac.pdf

RPNet: A Representation Learning-Based Star Identification Algorithm

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RPNet: A Representation Learning-Based Star Identification Algorithm