Star identification based on euclidean distance transform, voronoi tessellation, and k-nearest neighbor classification

Abstract

In this paper, an image-based lost in space star identification algorithm is developed for a typical daytime star tracker that uses uniform selection of the 2 Micron All Sky Survey (2MASS) infrared point source catalog. The proposed algorithm uses Euclidean distance transform of the image followed by Voronoi tessellation of stars and k-nearest-neighbor cell classification. This algorithm recognizes more than 95% of 1000 frames containing more than 50 spikes using Monte Carlo simulation within a desirable update frequency. Performance of image-based algorithm is compared to traditional feature-based algorithms in the presence of spikes. The feature-based method uses star triangle planar and spatial angles followed by the k-vector search technique and the geometric voting concept in an iterative process. The algorithm is optimized using multiobjective genetic algorithm Pareto front analysis. Monte Carlo simulation of the feature-based method using optimal parameters shows unacceptable reduction of robustness in the presence of more than 12 spikes. In order to analyze the effects of hardware, both algorithms are evaluated in a hardware-in-the-loop simulation using prototype star tracker Nasir-I, available at the SSDI laboratory. The results of 100 sample images from the 2MASS catalog accord with the software simulation. The proposed image-based algorithm shows acceptable performance in small fields of view requiring modest memory and computation cost, making it suitable for small satellites as well.