MANEUVERING TARGET TRACKING IN A CLUTTERED ENVIRONMENT FROM NOISY IMAGES BY AN ADAPTIVE PROBABILISTIC DATA ASSOCIATION FILTER

Abstract

A new tracking method for estimating motion parameters of a manoeuvring target in a cluttered environment from noisy image sequences is presented. Traditionally, tracking systems have been broadly classified according to (1) moving target with or without manoeuvring, and (2) tracking in a clean (uncluttered) or cluttered environment. The difficulties studied here are those arising from (a) unknown acceleration inducing a randomly updated trajectory, and (b) the disturbance of returns from false alarms or other interfering objects. The probability data association filter (PDAF) is augmented by the semi-Markov process, called the adaptive PDAF, to handle the difficulties. Since the acceleration state applied either by a pilot or by a guidance control system is independent of the appearance of false returns, the treatment of target manoeuvring and false returns are separated into two sub-problems, i.e. the PDAF is applied to govern the disturbance of multiple returns and the semi-Markov process is applied to estimate uncertain accelerations. Three simulation results of variations show small biases from the true trajectory. By comparing with a nearest neighbour standard filter, the proposed tracking system also demonstrates better results.