Abstract
Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reverberation. In this work, to solve the problem of multi-target multi-sensor sonar tracking in the presence of clutter, a novel probabilistic multi-hypothesis tracker (PMHT) approach based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) is proposed. The PMHT can efficiently handle the unknown measurements-to-targets and measurements-to-transmitters data association ambiguity. The EKF and UKF are used to deal with the high degree of nonlinearity in the measurement model. The simulation results show that the proposed algorithm can improve the target tracking performance in a cluttered environment greatly, and its computational load is low.
Highlights
The active sonar system [1] is one of the most used sonar systems; it uses one or more transmitters and receivers
We propose a novel probabilistic multi-hypothesis tracker (PMHT) tracker based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) for the problem of multi-target multi-sensor sonar tracking in a cluttered environment
PMHT is an expectation maximization (EM)-based [28], batch-tracking algorithm for multi-target tracking in a cluttered environment
Summary
The active sonar system [1] is one of the most used sonar systems; it uses one or more transmitters and receivers. The major advantage of the multistatic sonar system is that it has additional detection opportunities in comparison to a monostatic system. It is very hard for a target to remain covert in a multistatic sonar system due to the transmitter-target-receiver geometry. The “usual” PMHT algorithm just considers the data association ambiguity between measurements and targets [20,21,22]. We propose a novel PMHT tracker based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) for the problem of multi-target multi-sensor sonar tracking in a cluttered environment.
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