Abstract
In this work, we apply the probabilistic multihypothesis tracker (PMHT) for the problem of underwater bearing-only multisensor–multitarget tracking in clutter. The PMHT is a batch tracking algorithm that can efficiently process a large number of measurements from multiple sensors. We investigate both the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) for dealing with the high degree of nonlinearity in the measurement model. Due to multiple sensors, the unobservability of single sensor bearing-only target tracking is avoided. Simulation results show that the PMHT works very well in a highly cluttered environment and its computational load is low.
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