Maneuvering target tracking using jump processes

Abstract

The authors present a maneuvering target model with the maneuver dynamics modeled as a jump process of Poisson type. The jump process represents the deterministic maneuver (or pilot commands) and is described by a stochastic differential equation driven by a Poisson process taking values from a set of discrete states. Assuming that the observations are governed by a linear difference equation driven by a white Gaussian noise sequence, the authors have developed a linear, recursive, unbiased minimum variance filter. The performance of the proposed filter is assessed through a numerical example via Monte Carlo simulations. It is observed from the numerical results that the proposed filter provides good estimates for rapidly maneuvering targets.<>