Suppression of stray light based on energy information mining.

Abstract

The star tracker plays a critical role in precision aerospace missions due to its high accuracy, absolute attitude output, and low power consumption. For an optical sensor, the problem of stray light is always an important research issue. A star energy information mining method for stray light suppression is proposed in this study. The gray-level co-occurrence matrix and k-nearest neighbor algorithm are adopted to identify the types of stray light that enter the optical system. Effective recognition of the stray light types is an important premise for the following steps. Then the parameters are optimized during background estimation. When star spots are extracted, the local differential encoding combined with Levenshtein distance filtering is conducted to eliminate the interference noise spots. The proposed algorithm can achieve accurate star spot extraction even when stray light exists in real night sky observation experiments.