Distributed data fusion via federated alpha-beta-gamma filter

A federated alpha-beta-gamma filter is developed for utilization in multi-sensor systems tracking a maneuvering target. Filter architecture that consists of local processors and global processor is employed to describe the distributed fusion problem due to correlation across track estimates for the same target when several sensors execute surveillance over the certain area. Each local processor uses decoupling technique to develop the tracking index to obtain the alpha-beta-gamma filter gain and the corresponding covariance formulations that are recursively computed in the line-of-sight Cartesian coordinate system and then transformed for use in the reference Cartesian coordinate system. Common process noise correlations are handled by the factor which is selected by a conservative matrix upper bound. The global processor combines local processor outputs via weighted least square estimator. The resulting filter has computational advantages over traditional maximum likelihood estimator with similar performance. Simulation results are included to demonstrate the effectiveness of the proposed algorithm.