English

Abstract

Two aspects involved in automatic target recognition namely, (i) Location and identity estimation (LIE) of a target by fusing infrared (IR) and acoustic sensor data, and (ii) centroid tracking for target state estimation using IR sensor data are discussed in this paper. The LIE has been achieved using a combination of Bayesian fusion and one of the three search algorithms namely, metropolis hastings (MH), simulated annealing (SA) and gradual greedy (GG). It was observed that the performance of the GG search algorithms was better in terms of success rate which has been evaluated through Monte Carlo simulations. For tracking of the centroid, an algorithm, where the centroid of the gray level image is tracked using probabilistic data association filter, has been implemented. Simulated data results indicate good tracking performance of this algorithm. For robust tracking of centroid, the track from the imaging sensor was fused with the track from ground-based radar using state vector fusion. It was observed that fusion generates robust tracks even when there is data loss in one of the sensors.