Multiple Targets Tracking by Using Probability Data Association and Cubature Kalman Filter

In this paper, we investigate the problem of tracking multiple underwater targets when bearing-only measurements from multiple acoustic sensors are available. In this challenging scenario, we exploit the probability data association (PDA) to complete the measurement-target association task and the Cubature Kalman filter to deal with the nonlinear filtering problem encountered in the bearing measurement. Finally, simulation results show that the proposed method works better than the Unscented Kalman filter based counterpart.