Target tracking algorithm based on Gauss-Hermite quadrature in passive sensor array

Abstract

In this paper, a new target tracking algorithm based on Gauss-Hermite quadrature is proposed in passive sensor array. Firstly, the quadrature Kalman filter (QKF) that used statistical linear regression (SLR) to linearize a nonlinear function through a set of Gauss-Hermite quadrature points is analyzed for passive target tracking. The performance of the filter is more accurate than the extended Kalman Filter (EKF), the Pseudo Linear Kalman Filter (PLKF) and the unscented Kalman Filter (UKF) in nonlinear dynamic system. Secondly, in order to avoid the unobservability problem of passive target tracking, a nonlinear measurement model of multiple passive sensors is founded, and the algorithm can deal with the case of non-Gaussian noise. Finally, the simulation results show that the proposed algorithm is effective, and its performance is superiority over above methods.