A Maneuvering Target Tracking Algorithm Based on Gaussian Filter for Multiple Passive Sensors

Abstract

When tracking a maneuvering target by multiple passive sensors, two problems need to be considered, one is the nonlinear problem, another is the maneuvering problem. Taking these into account, a Gaussian filter (GF) for nonlinear Bayesian estimation is introduced based on a deterministic sample selection scheme, which can solve the nonlinear problem better than the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Then, a new maneuvering target tracking algorithm is proposed based on the GF and Interacting Multiple Mode (IMM), called IMM-GF method in this paper. Simulation results show that the proposed method has better performance than the IMM-EKF and IMM-UKF in tracking a maneuvering target for multiple passive sensors.