A new adaptive weighted fusion algorithm for multi-sensor tracking

Presents a technique for the online adaptive weighted fusion algorithm for multi-sensor tracking. The algorithm consists three steps: (i) estimation of the variance of the sensor's measurements noise using statistical theory; (ii) adjustment of the fused sensor's weight coefficient according to the sensor's noise variance change; (iii) prediction of the next target position using the "current" statistical model and a Kalman filter method. Computer simulation results are presented to demonstrate the performance of this approach.