Particle filtering for signal enhancement in a noisy shallow ocean environment

The development of model-based processing techniques in ocean acoustics is well-known evolving from the pure statistical approach of maximum likelihood parameter estimation, matched-field processing and sequential model-based processing for Gaussian uncertainties. More recent model-based techniques such as unscented Kalman filtering (UKF) and sequential Markov chain Monte Carlo (MCMC) methods using particle filters (PF) have been developed to improve both unimodal distribution estimates (UKF) as well as multimodal estimates (PF). In this paper we apply both techniques to provide enhanced signal estimates for acoustic hydrophone measurements on a vertical array and compare their performance. We use a normal-mode propagation solution to provide synthetic data in order to make the comparison and demonstrate the approach which will open the area to direct extensions such as localization, broadband processing, inversion, etc. We show how the normal-mode model can be incorporated directly into the processors along with the measurement array enabling the resulting enhancement capabilities.