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首页> 外文期刊>Applied Acoustics >Particle filtering with adaptive resampling scheme for modal frequency identification and dispersion curves estimation in ocean acoustics
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Particle filtering with adaptive resampling scheme for modal frequency identification and dispersion curves estimation in ocean acoustics

机译：具有自适应重采样方案的粒子滤波，用于海洋声学中的模态频率识别和色散曲线估计

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摘要

The goal of this work is to accurately estimate the modal frequencies and dispersion curves from a measured ocean acoustics signal. A particle filtering approach, a class of sequential Monte Carlo methods, is developed for modal frequency identification and dispersion curves estimation from a time-frequency representation of ocean acoustics signal. The adaptive resampling algorithm for enhancing the quality of a set of particles after likelihood calculation is implemented to improve the accuracy of the modal estimates as well as the dispersion curves of the signal. Results demonstrate the advantages in implementing the adaptive resampling into the conventional sequential importance sampling particle filter (SIS-PF) instead of using the sequential importance resampling (SIR) scheme. The noise robustness of the proposed method is demonstrated through examples where the realizations of different Signal-to-Noise Ratio (SNR) levels were used to test the performance of the adaptive resampling method. The results display the evidences that the adaptive resampling particle filter (AR-PF) is superior to the SIR-PF. Via root mean square error (RMSE), the AR-PF delivers smaller errors than those obtained by the SIR-PF for all SNR levels, emphasizing the benefit in incorporating the adaptive resampling into the PF for modal frequency identification and dispersion curves estimation of ocean acoustics signal. (C) 2019 Elsevier Ltd. All rights reserved.
机译：这项工作的目的是根据测得的海洋声学信号准确估算模态频率和色散曲线。提出了一种粒子滤波方法，它是一类顺序蒙特卡罗方法，用于从海洋声学信号的时频表示中进行模态频率识别和色散曲线估计。实施了用于在似然计算之后提高一组粒子质量的自适应重采样算法，以提高模态估计的准确性以及信号的色散曲线。结果证明了在将自适应重采样实施到常规顺序重要性采样粒子滤波器（SIS-PF）而不是使用顺序重要性重采样（SIR）方案的优势。通过使用不同信噪比（SNR）级别的实现来测试自适应重采样方法性能的示例，演示了该方法的噪声鲁棒性。结果表明，自适应重采样粒子滤波器（AR-PF）优于SIR-PF。通过均方根误差（RMSE），对于所有SNR级别，AR-PF所产生的误差均小于SIR-PF所获得的误差，从而强调了将自适应重采样结合到PF中以用于模态频率识别和海洋色散曲线估计的好处声学信号。 （C）2019 Elsevier Ltd.保留所有权利。

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