A comparison of nonlinear filtering approaches with an application to ground target tracking

Abstract

With an application to ground target tracking, two groups of nonlinear filtering approaches are compared in this paper: Gaussian approximation and Monte Carlo simulation. The former group, consisting of the extended Kalman filter (EKF), Gauss–Hermite filter (GHF) and unscented Kalman filter (UKF), approximates probability densities of nonlinear systems using either single or multiple points in a state space, while the latter group, being particle filters, estimates probability densities using random samples. There are two sources contributing to nonlinearity in the ground target tracking problem: terrain and road constrained kinematic modeling and polar coordinate sensing. When tracking ground maneuvering targets with multiple models, one faces another problem, i.e., non-Gaussianity. This paper also compares interacting multiple model (IMM)-based filters IMM-EKF, IMM-GHF and IMM-UKF with particle-based multiple model filters for their capability in handling the non-Gaussian problem. Simulation results show that: (1) all the filters achieve a comparable performance when tracking non-maneuvering ground targets; (2) particle-based multiple model filters are superior to IMM-based filters in maneuvering ground target tracking.