Abstract
The major advantage of the passive multiple-target tracking is that the sonars do not emit signals and thus they can remain covert, which will reduce the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the measurement to target data association uncertainty make the passive multiple-target tracking problem challenging. To deal with the target to measurement data association uncertainty problem from multiple sensors, this paper proposed a batch recursive extended Rauch-Tung-Striebel smoother- (RTSS-) based probabilistic multiple hypothesis tracker (PMHT) algorithm, which can effectively handle a large number of passive measurements including clutters. The recursive extended RTSS which consists of a forward filter and a backward smoothing is used to deal with the nonlinear Doppler and bearing measurements. The target range unobservability problem is avoided due to using multiple passive sensors. The simulation results show that the proposed algorithm works well in a passive multiple-target tracking system under dense clutter environment, and its computing cost is low.
Highlights
Passive multiple-target tracking has gained more and more attention in the fields of military and civilian, such as navigation, monitoring and early warning, and salvage [1,2,3]
The major advantage of the passive sonars multiple-target tracking is that the sonars do not emit signals, and they can remain covert, which will reduce the risk of being attacked
The underwater environment is with dense clutter which will cause the measurement to target data association uncertainty problem
Summary
Passive multiple-target tracking has gained more and more attention in the fields of military and civilian, such as navigation, monitoring and early warning, and salvage [1,2,3]. The multiple-target tracking problem is solved by the maximum entropy intuitionistic fuzzy data association [38], cross entropy [39], maximum fuzzy entropy-based Gaussian clustering algorithm [40], entropy distribution and game theory based on the probability hypothesis density (PHD) method [41], maximum entropy fuzzy based on the fire-fly and PF [42], and the distributed cross entropy-based δ-generalized labelled multi-Bernoulli filter [43]. In order to avoid the range unobservability problem, this paper uses multiple passive sensors to track targets. In order to improve the multiple targets’ range observability and tracking performance, we introduce the nonlinear Doppler measurement and use multiple sensors. The batch recursive multiple-sensor PMHT algorithm is used to handle the measurement to target data association complexity problem.
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