Sequential Bayesian filtering for a varying model-order passive fathometer problem
Caglar Yardim, Zoi-Heleni Michalopoulou, Peter Gerstoft · The Journal of the Acoustical Society of America · 2011
Sequential model selection is demonstrated for a drifting passive fathometer case. It has been shown that, by processing noise data at a specific array location, a reflector sequence can be extracted, consisting of a summation of sinc pulses. The center of each pulse identifies the depth of a reflector in the ocean environment at that location. Extracting the number and peak/depth locations of these pulses in the reflector sequence provides insight in the sediment structure of the medium. Collecting data in multiple ranges, a process facilitated by the drifting array, allows the study of multiple reflector sequences. Similarly to spatial time delay tracking with Bayesian filters that sample from posterior density functions, we treat sequences obtained at different ranges as data arriving sequentially into a particle filter that extracts at every range (state) the number of pulses and their corresponding depths using an observation equation. A state equation then predicts reflectors at the next range and updates estimates accordingly. The number of pulses varies with range, following changes in sediment layering. Probability density functions of the number of layers and their depths are calculated and demonstrate the successful tracking of changes in the structure of the ocean environment and the uncertainty therein.