SUMMARY
Maximum likelihood estimator is a suitable algorithm for passive target tracking applications. Nardone, Lindgren and Gong introduced this approach using batch processing. In this paper, the batch processing is converted into sequential processing for real-time applications like passive target tracking using bearings-only measurements. Adaptively, the variance of each measurement is computed and is used along with the measurement in such a way that the effect of false bearings can be reduced. The transmissions made by radar on a target ship are assumed to be intercepted by an electronic warfare (EW) system of own ship. The generated bearings in intercept mode are processed through maximum likelihood estimator (MLE) to find out target motion parameters. Instead of assuming some arbitrary values, pseudo linear estimator outputs are used for the initialisation of MLE. The algorithm is tested in Monte-Carlo simulation and its results are presented for two typical scenarios.Defence Science Journal, 2010, 60(2), pp.197-203, DOI:http://dx.doi.org/10.14429/dsj.60.340