Decoupled Adaptive Tracking Algorithm for Multi-Sensor Measurement Fusion
A decoupled adaptive tracking filter is developed for centralized measurement fusion to track the same maneuvering target to improve the tracking accuracy. The proposed approach consists of a dual-band Kalman filter and a two-category Bayesian classifier. Based upon data compression and decoupling techniques, two parallel decoupled filters are obtained for lessening computation. The Bayesian classification scheme is employed which involves switching between high-level-band filter and low-level-band filter to continuously resist different target maneuver turns. The simulation results are presented which demonstrate the effectiveness of the proposed method.
L. W. Fong, "Decoupled Adaptive Tracking Algorithm for Multi-Sensor Measurement Fusion", Applied Mechanics and Materials, Vols. 229-231, pp. 1235-1238, 2012