Maximum Likelihood Estimator (MLE) is a suitable algorithm for passive target tracking applications. Nardone, Lindgren and Gong [1] introduced this approach using batch processing [1]. In this paper, this batch processing is converted into sequential processing to use 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, making the estimate a generalized one. Instead of assuming some arbitrary values, Pseudo Linear Estimator (PLE) outputs are used for the initialization of MLE. The algorithm is tested in Monte Carlo simulation and its results are compared with that of Cramer-Rao Lower Bound (CRLB) estimator. The results of one scenario are presented. From the results, it is observed that this algorithm is also an effective method for the bearing-only passive target tracking.
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