Maneuvering target tracking using passive TDOA measurements

Abstract

This paper proposes a novel interacting multiple model (IMM) algorithm to track a maneuvering target, with the aim of improving the tracking performance of a time difference of arrival (TDOA) passive tracking system. Under the architecture of the proposed algorithm, the multiple model deals with the model switching, while the iterated extended Kalman filter (EKF) accounts for non-linearity in the dynamic system models. The tracking performances of the proposed algorithm, EKF, IMM are compared via Monte Carlo simulations. Simulation results indicate that the proposed algorithm is an effective nonlinear filtering algorithm for TDOA passive tracking system, and has higher tracking precision than the IMM and EKF. The proposed algorithm can reduce nearly 6.75% and 61.23% of the positioning error than IMM and EKF algorithms.