Distributed multi-sensor particle filter for bearings-only tracking

Abstract

In this article, the classical bearings-only tracking (BOT) problem for a single target is addressed, which belongs to the general class of non-linear filtering problems. Due to the fact that the radial distance observability of the target is poor, the algorithm-based sequential Monte-Carlo (particle filtering, PF) methods generally show instability and filter divergence. A new stable distributed multi-sensor PF method is proposed for BOT. The sensors process their measurements at their sites using a hierarchical PF approach, which transforms the BOT problem from Cartesian coordinate to the logarithmic polar coordinate and separates the observable components from the unobservable components of the target. In the fusion centre, the target state can be estimated by utilising the multi-sensor optimal information fusion rule. Furthermore, the computation of a theoretical Cramer–Rao lower bound is given for the multi-sensor BOT problem. Simulation results illustrate that the proposed tracking method can provide better performances than the traditional PF method.

Keywords:

• particle filtering
• logarithmic polar coordinate
• Cramer–Rao lower bound
• multi-sensor bearings-only tracking

Acknowledgements

This study was supported by the National Natural Science Foundation of China under Grant NSFC-60871074. The authors are grateful to the editor and referees for the valuable comments and suggestions.