A robust fusion algorithm for multi-sensor tracking

This paper presents a technique for the online adaptive weighted fusion algorithm for multi-sensor tracking. A suitable method for estimating measurement noise variance is developed, and the formulas of fuzzy logic-based fusion and parallel adaptive tracking are derived. The algorithm consists of three steps: (i) estimation of the sensor\´s measurement noise variance using the statistical theory; (ii) adjustment of the fused sensor\´s weight coefficient according to the sensor\´s noise variance change; (iii) prediction of the target position using the "current" statistical model and Kalman filter method. The algorithm is able to adapt itself to the changes of sensor\´s noise, and its estimation error is of least mean square. Computer simulation results are presented to demonstrate the robust performance of this algorithm.