Abstract
We investigate the problem of bearings-only tracking of manoeuvring targets using particle filters (PFs). Three different (PFs) are proposed for this problem which is formulated as a multiple model tracking problem in a jump Markov system (JMS) framework. The proposed filters are (i) multiple model PF (MMPF), (ii) auxiliary MMPF (AUX-MMPF), and (iii) jump Markov system PF (JMS-PF). The performance of these filters is compared with that of standard interacting multiple model (IMM)-based trackers such as IMM-EKF and IMM-UKF for three separate cases: (i) single-sensor case, (ii) multisensor case, and (iii) tracking with hard constraints. A conservative CRLB applicable for this problem is also derived and compared with the RMS error performance of the filters. The results confirm the superiority of the PFs for this difficult nonlinear tracking problem.
Highlights
The problem of bearings-only tracking arises in a variety of important practical applications
This paper presents the application of particle filters (PFs) [9, 10, 11] for bearings-only tracking of manoeuvring targets and compares its performance with traditional IMMbased filters
The comparison will be based on a set of 100MC simulations and where possible, the Cramer-Rao lower bounds (CRLBs) will be used to indicate the best possible performance that one can expect for a given scenario and a set of parameters
Summary
The problem of bearings-only tracking arises in a variety of important practical applications. In the case of autonomous TMA (single observer only), which is the focus of a major part of this paper, the observation platform needs to manoeuvre in order to estimate the target range [1, 3]. This need for ownship manoeuvre and its impact on target state observability have been explored extensively in [4, 5]. Interacting multiple model (IMM)-based trackers were proposed in [6, 7] for this problem These algorithms employ a constant velocity (CV) model along with manoeuvre models to capture the dynamic behaviour of a manoeuvring target scenario. Le Cadre and Tremois [8] modelled the manoeuvring target using the CV model with process noise and developed a tracking filter in the hidden Markov model framework
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