Bearings-only tracking of maneuvering targets using a batch-recursive estimator

We present a new batch-recursive estimator for tracking maneuvering targets from bearings-only measurements in clutter (i.e., for low signal-to-noise ratio (SNR) targets). Standard recursive estimators like the extended Kalma n filter (EKF) suffer from poor convergence and erratic behavior due to the lack of initial target range information. On the other hand, batch estimat ors cannot handle target maneuvers. In order to rectify these shortcomings, we combine the batch maximum likelihood-probabilistic data association (ML -PDA) estimator with the recursive interacting multiple model (IMM) estimat or with probabilistic data association (PDA) to result in better track init ialization as well as track maintenance results in the presence of clutter. It is also demonstrated how the batch-recursive estimator can be used for adaptive decisions for ownship maneuvers based on the target state estimati on to enhance the target observability. The tracking algorithm is shown to be effective for targets with 8dB SNR.