Adaptive particle filtering for mode tracking: A shallow ocean application

The shallow ocean is an ever changing environment primarily due to temperature variations in its upper layers (<;100 m) directly affecting sound propagation throughout. The need to develop processors capable of tracking these changes implies a stochastic as well as an environmentally adaptive design. The stochastic requirement follows directly from the multitude of variations created by uncertain parameters and noise. Some work has been accomplished in this area, but the stochastic nature was constrained to Gaussian uncertainties. It has been clear for a long time that this constraint was not particularly realistic leading to a Bayesian approach that enables the representation of any uncertainty distribution. Sequential Bayesian techniques enable a class of processors capable of performing in an uncertain, nonstationary (varying statistics), non-Gaussian, variable shallow ocean environment. A solution to this problem is addressed by developing a sequential Bayesian processor capable of providing a joint solution to the modal function tracking (estimation) and environmental adaptivity problem. The posterior distribution required is multi-modal (multiple peaks) requiring a sequential (nonstationary) Bayesian approach. Here the focus is on the development of a particle filter (PF) capable of providing reasonable performance for this problem. In our previous effort on this problem nonlinear/non-Gaussian processors were developed to operate on synthesized data based on the Hudson Canyon experiment using normal-mode representations. Here we extend the processors by applying them to the actual hydrophone measurements obtained from the 23-element vertical array. The adaptivity problem is attacked by allowing the modal coefficients to be estimated from the measurement data jointly along with tracking of the modal functions-the main objective.