Cooperative space object tracking using consensus-based filters

Bin Jia, Khanh D. Pham, Erik Blasch, Dan Shen, Zhonghai Wang, Genshe Chen

Abstract: Cooperative tracking plays a key role in space situation awareness (SSA) in which there are a limited number of observations and poor tracking performance from a single sensor. To utilize the information from multiple networked sensors, both centralized and decentralized fusion algorithms can be used. Compared with centralized fusion algorithms, decentralized fusion algorithms are more robust in terms of communication failure and computational burden. One popular distributed estimation approach is based on the average consensus which asymptotically converges to the optimal estimate by multiple iterations of neighborhood information. Consensus-based algorithms have become popular in recent years due to the fact that they do not require the global knowledge of the network or the routing protocols. In this paper, we utilize the information weighted consensus filter (ICF) to track space objects using multiple space-based optical (SBO) sensors. A scenario which contains a space object and four SBOs is used to test the ICF. To improve the performance of ICF, the cubature rule embedded ICF (Cub-ICF) is proposed and compared with the ICF. We also compare the ICF with the centralized extended information filter (CEIF). The results indicate that the proposed Cub-ICF is more robust than the ICF and the consensus based decentralized filters can achieve close performance to the centralized filters. Consensus based filters facilitate cooperative space tracking leading to robustness amongst sensor failures, reduction in computations, and elimination of complex network protocols.