Decoupled Adaptive Tracking Algorithm for Multi-Sensor Measurement Fusion

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 229-231)
• Pages: 1235-1238
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.229-231.1235
• Online since: November 2012
• Authors: Li Wei Fong
• Keywords: Bayesian Classifier, Decoupled Adaptive Tracking Filter, Measurement Fusion

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