Multi-Sensor Space Debris Tracking for Space Situational Awareness With Labeled Random Finite Sets

As a result of the dependence worldwide on satellite technology, it is now necessary to use advanced multi-target tracking algorithms for space debris tracking systems to maintain custody of space objects around the earth. One principal challenge is the correct association of observations with objects. This paper presents a multi-sensor, space-debris tracking algorithm using δ-generalized labeled multi-Bernoulli (δ-GLMB) filtering. The algorithm provides a solution to the key challenges (e.g., detection uncertainty, data association uncertainty, and clutter) in multiple object tracking. An efficient implementation of the multi-sensor δ-GLMB filter is proposed. In order to avoid exhaustively computing all the terms, we propose to use the ranked assignment algorithm with an extended assignment matrix for multiple sensors to determine the most significant terms. A measurement-based birth model is used to identify the previously unknown space objects. Sensors can have the same or different observation volumes. The expectation-maximization (EM) algorithm is used to approximate densities across observation volumes. The performance is demonstrated using the simulation results.