Multiple target tracking with asynchronous bearings-only-measurements

An algorithm for detection and tracking of multiple targets using bearings measurements from several sensors is developed. The algorithm is an implementation of a multiple hypothesis tracker with pruning of unlikely hypotheses. Tracking conditional on each hypothesis can be performed using any suitable filtering approximation. In this paper a range- parameterized unscented Kalman filter is used. Each hypothesis describes a track collection with varying number of targets. Final track estimates are obtained by weighted clustering according to hypothesis probabilities and clustered track states. Simulation experiments include arbitrary setup of multiple targets and multiple moving receiver platforms (sensors). The main results are the asynchronous modeling of measurements arrivals which allows an effective and efficient processing in a Bayesian MHT framework.