Decentralized 3-D Target Tracking in Asynchronous 2-D Radar Network: Algorithm and Performance Evaluation

The problem of 3-D target tracking in asynchronous 2-D radar network is considered. To deal with this problem, this paper presents a decentralized asynchronous track-to-track fusion (DAT2TF) algorithm. The DAT2TF algorithm is implemented by reconstructing the optimal centralized fusion result with asynchronous local estimates and their error covariance matrices. The derivations show that this algorithm actually operates in a centralized sense, but is not optimal due to two approximations about the local target motion model and the polar to Cartesian measurement conversion procedure. To evaluate the estimation performance of the DAT2TF algorithm, a decentralized Bayesian Cramér-Rao lower bound is also developed. Simulation results show that the proposed approach is effective and efficient, when compared with the particle filter-based centralized fusion architecture, in terms of the tracking accuracy and the computation load.