Comparison of sampling-based algorithms for multisensor distributed target tracking

This paper considers the problem of tracking a maneuvering target in multisensor environment. A novel scheme is proposed for distributed tracking which utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is nonlinear In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented-particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results front extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving a distributed target tracking problem.