Application of EM algorithm to adaptive filter for multiple target tracking

Tracking the targets of interest has been one of the major research areas in radar surveillance systems. We formulate the tracking problem as an incomplete data problem and apply the EM algorithm to obtain the MAP estimate. The resulting filter has a recursive structure analogous to the Kalman filter. The difference is that the measurement-update deals with multiple measurements and the parameter-update can estimate the system parameters. Through extensive experiments, it turns out that the proposed system is better than the probabilistic data association filter (PDAF) and NNF in tracking the targets. Also, the performance degrades gracefully as the disturbances become stronger.