Tracking performance analysis for distributed active-passive sensors' network

The tracking performance for a distributed active-passive sensors' network was investigated here. One precise passive sensor and several imprecise active sensors were used in this network. In order to get accurate and prompt estimate, Unscented Kalman Filter (UKF) was used in the passive sensor and debiased consistent converted measurements Kalman filter (CMDK-D) was used in the active sensors. Target trajectory was first estimated in parallel in each sensor's local signal processing system, then the local trajectories were fused together to get the global estimates, finally the global estimates were fed back to the distributed sensors. Using Monte-Carlo simulation method, the tracking performance was analyzed in several scenarios. The simulation results show that the tracking performance based on active-passive sensors' network is much prior to those with only active sensors, while the former network's computational load is a bit heavier than the latter.