Sequential Monte Carlo tracking schemes for maneuvering targets with passive ranging

Abstract

In this article we consider tracking a single maneuvering target in scenarios where range information is not available, or is denied. This tracking problem is usually referred to as passive ranging, or bearings-only tracking. Tracking any single maneuvering target naturally admits a jump Markov system, in which a collection of candidate dynamical systems is proposed to model various classes of motion, each of which is assumed to be executed by the target according to a Markov law. Standard techniques to solve this problem use the so called interacting multiple model (IMM), or its variants. Recently sequential Monte Carlo (SMC) techniques have been applied to passive ranging problems, however, most of the scenarios reported in the literature consider nonmaneuvering targets. In this article we apply a new SMC technique to the passive ranging problem in a maneuvering target scenario. The algorithm we propose is compared to the so called auxiliary particle filter (APF). A simulation study is included.