Asynchronous multi-sensor tracking in clutter with uncertain sensor locations using Bayesian sequential Monte Carlo methods

Abstract

This paper presents the development of a tracking algorithm for multi-sensor single target tracking in the presence of asynchronous or missing measurements and high clutter levels. The algorithm is based upon the random sample representation of state PDFs and uses sequential Monte Carlo or "particle" filtering methods to perform prediction and update. The performance of the algorithm is illustrated on the challenging problem of naval subsurface target tracking using multiple drifting sonobuoys of the DIFAR type. Good performance was demonstrated on simulated scenarios with a high level of uncertainty represented by unknown sensor location, 20% missing measurements and 70% clutter.