Passive manuvering target tracking via hybrid coordinates federated filtering fusion with information feedback

A hybrid coordinates federated filtering fusion algorithm with information feedback is developed for a passive maneuvering target tracking. The algorithm is designed to deal with angle-only measurements for accurately estimating the kinematic state components of target motion. The hierarchical architecture of the algorithm comprises a group of local processors and a global processor. In each local processor, an extended Kalman filter is utilized with federated filtering in hybrid coordinates for state and state covariance extrapolation and updating. For merging the outputs of local processors, the global processor a recursive least squares (RLS) estimator is utilized to sequentially generate a global estimate for system outputs and information feedback. A typical target maneuver scenario is employed to investigate the performance of the RLS estimator with and without information feedback through computer simulation. In simulation study, each local processor encounters slow convergence problem under the scenario. By using the proposed algorithm with information feedback, the convergence of each local processor is greatly accelerated. The algorithm markedly improves the local tracking accuracy as well.