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首页> 外文期刊>Signal processing >Particle filtering based track-before-detect method for passive array sonar systems
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Particle filtering based track-before-detect method for passive array sonar systems

机译：基于粒子滤波的无源阵列声纳探测前跟踪方法

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摘要

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.
机译：这项工作考虑了使用无源阵列声纳系统对未知数量和时变数量的目标（即声发射器）进行水下跟踪。如果声发射器的信噪比（SNR）低，则此问题将变得更具挑战性。为了解决这个问题，特别为无源阵列声纳系统开发了一个完整的粒子滤波器检测前跟踪（PF-TBD）信号处理程序。具体地，为了增强低SNR目标的检测性能，在声信号的波束形成之后的非阈值频谱测量被直接用作PF-TBD方法的输入。为了更好地建模频谱测量的统计特性，提出了一种基于数据拟合的参数估计算法，以建立准确的似然函数。然后可以通过粒子滤波将联合多目标概率密度（JMPD）递归向前传播，以估计多目标状态。为了适应随时间变化的目标数量，通过在每个测量时间自适应地调整JMPD的状态尺寸，还将轨迹起始和终止策略集成到过滤过程中。最后，所提出的PF-TBD方法的有效性在仿真和收集的实际数据中都得到了证明。 （C）2019 Elsevier B.V.保留所有权利。

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