Bearings-only multi-sensor multi-target tracking based on Rao-Blackwellized Monte Carlo data association

This paper addresses the problem of tracking multiple targets using multi-sensor bearings-only measurements in the presence of noise and clutter. The Rao-Blackwellized Monte Carlo data association (RBMCDA) scheme and the unscented Kalman filter (UKF) are applied to solve the problems of uncertain association and nonlinear filtering, respectively. In particular, the sensors are assumed to move back and forth alternately. Simulation results show that the filtering algorithm produces reliable position estimates under single and multiple tracking scenarios.