Particle filtering with adaptive resampling scheme for modal frequency identification and dispersion curves estimation in ocean acoustics

Abstract

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.