Lost-in-Space Star Identification Using Planar Triangle Principal Component Analysis Algorithm

Fuqiang Zhou,Tao Ye

doi:10.1155/2015/982420

Abstract

It is a challenging task for a star sensor to implement star identification and determine the attitude of a spacecraft in the lost-in-space mode. Several algorithms based on triangle method are proposed for star identification in this mode. However, these methods hold great time consumption and large guide star catalog memory size. The star identification performance of these methods requires improvements. To address these problems, a star identification algorithm using planar triangle principal component analysis is presented here. A star pattern is generated based on the planar triangle created by stars within the field of view of a star sensor and the projection of the triangle. Since a projection can determine an index for a unique triangle in the catalog, the adoption of thek-vector range search technique makes this algorithm very fast. In addition, a sharing star validation method is constructed to verify the identification results. Simulation results show that the proposed algorithm is more robust than the planar triangle andP-vector algorithms under the same conditions.

Highlights

Accurate attitude information is essential for spacecraft autonomous navigation
We develop a star identification algorithm called the planar triangle principal component analysis (PTP) algorithm that performs well against star positional noise and magnitude noise
A simulation and an analysis of the results are provided. These focus on discussing the number of slave stars in guide database construction and comparing the robustness of the different algorithms with regard to the algorithm parameters, including positional noise, magnitude noise, and the number of stars in the field of view (FOV)

Summary

Introduction

Accurate attitude information is essential for spacecraft autonomous navigation. This plays a very important role in spacecraft control systems. This algorithm can give precise statistical analysis of the polar moment or area errors and can provide an accurate range for the observation triangles matching the catalog triangles. When the star sensor has an 8 × 8-degree FOV, the area and polar moment calculated can generate a guide star catalog of 167 MB This algorithm is affected by star positional noise in the image. These focus on discussing the number of slave stars in guide database construction and comparing the robustness of the different algorithms (our PTP algorithm, P-vector algorithm, and planar triangle algorithm) with regard to the algorithm parameters, including positional noise, magnitude noise, and the number of stars in the FOV.

Algorithm Description

Simulation and Analysis

Findings

Conclusion