Multi-target Passive Tracking for Multi-spacecraft with Limited Fields-of-View

Abstract

In this article, the problem of three-dimensional multitarget tracking (MTT) by multi-spacecraft equipped with passive sensors under limited field-of-view (FoV) is considered. A distributed communication network is formed between sensors, each local node runs a Gaussian mixture probability hypothesis density filter to extract the state of targets. Based on arithmetic average fusion rule, a bearing-only FoV function that incorporates the position, attitude, and angular detection range of the sensors is proposed and applied to adjust the fusion weights accordingly. The region of bearing-only FoV is hard to calculate under inertial Cartesian coordinate. Subsequently, the cubature points sampling is employed to compute the integral in the FoV function. To obtain trajectory information, labeled Gaussian component strategy is adopted in local nodes. Then, a sequential label matching method based on Cauchy-Schwarz divergence is proposed to solve the problem of label inconsistency. To verify the proposed algorithm, a real-time hardware-in-the-loop simulation system that restores the MTT scenario in space environment is established. Simulation results demonstrate that the proposed method can significantly improve the trajectory tracking duration, while ensuring the estimation accuracy and has high computational efficiency.