Improved GM-PHD filter based on threshold separation clusterer for space-based starry-sky background weak point target tracking

Abstract

The movement of the background caused by the movement of a sensor platform has always been a major challenge in space-based infrared weak and small target tracking. In addition to clutter, false alarms, noise, and other disturbances, the most serious source of interference in space-based infrared weak target tracking comes from the stars. This kind of interference always exists and is large enough to be often mistakenly recognized as a new target by traditional algorithms. To reduce the influence of sensor platform movement and stellar interference on the target tracking of weak points in space, this paper respectively proposes a fast iterative closest point (ICP) registration algorithm for sparse target points and a threshold separation clustering algorithm. After pruning and merging using a Gaussian mixture probability hypothesis density (GM-PHD) filter-based algorithm, the threshold separation cluster separates the weak point targets by thresholds, and then clusters according to the Euclidean distance between the targets. Moreover, to prevent stars with special positions from being clustered into a single group, a dynamic weight extraction scheme is adopted to better distinguish stars in the group. We compared the proposed algorithm with the original GM-PHD, forward-backward smoothing (FBS), and N-scan approaches in starry-sky tracking scenarios simulated using the Tycho-2 catalog. Experimental results show that the proposed algorithm has better tracking accuracy and a lower rate of false detections.