Bearings-only Tracking Based on Distributed Multisensor Pseudolinear Kalman Filter

Abstract

For bearings-only tracking (BOT), there are mainly two problems of nonlinear filtering and poor range observability. In the paper, a new distributed multisensor pseudolinear Kalman filter (PLKF) algorithm is proposed. The sensors use an instrumental vector PLKF (IV-PLKF) to process the measurements of the target independently, which can tackle the bias arising from the correlation between the measurement vector and pseudolinear noise by the bias compensation PLKF (BC-PLKF). The IV-PLKF embeds the recursive instrumental vector estimation method into the BC-PLKF, uses it to construct the instrumental vector, and applies the method of selective angle measurement to modify the local target state estimation and covariance. In the fusion center, the target state can be estimated by using the multisensor optimal information fusion criterion. Then the Cramer-Rao lower bound (CRLB) of multisensor BOT is derived. Simulation results show the effectiveness of the algorithm.