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Particle filtering based track-before-detect method for passive array sonar systems
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DOI：10.1016/j.sigpro.2019.07.027.png)
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This work considers the underwater tracking of an unknown and time-varying number of targets, i.e., acoustic emitters, using passive array sonar systems. This problem becomes more challenging if the signal-to-noise ratio (SNR) of the acoustic emitter is low. To address this problem, a complete particle filter track-before-detect (PF-TBD) signal processing procedure is especially developed for the passive array sonar systems. Specifically, in order to enhance the detection performance of the low SNR targets, the unthresholded spectrum measurements after the beamforming of the acoustic signals are directly used as the inputs of the PF-TBD method. To better model the statistical characteristics of the spectrum measurements, a data fitting based parameter estimation algorithm is proposed to build accurate likelihood functions. Then the joint multi-target probability density (JMPD) can be recursively propagated forward by particle filtering to estimate the multi-target states. To accommodate the time-varying number of targets, the trajectory initiation and termination strategies are also integrated into the filtering process by adaptively adjusting the state dimensions of the JMPD at each measurement time. Finally, the efficacy of the proposed PF-TBD method is demonstrated both in simulation and on collected real-world data. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Passive array sonar
Track-before-detect
Particle filtering
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