Passive sonar source tracking using a model-based Bayesian approach

In the ocean, passive source tracking typically utilizes acoustic energy radiated by the target. Many scenarios of interest occur on the continental shelf where the water is shallow (depth is less than 200 m). Low acoustic frequencies (<1 kHz) are more useful because they suffer less attenuation due to absorption. However, low frequency acoustic signals propagating in shallow water are strongly affected by interference between multiple paths resulting from boundary interactions. These interactions cause an interference pattern in the transmission loss (TL) between the source and receiver. In this work, the pattern of TL variation has been used successfully to localize (or estimate the range and depth of) a moving source. A Bayesian localization algorithm employs knowledge of uncertainty in environmental parameters such as water column depth, sound speed profile, and bathymetry and utilizes Monte Carlo simulation to build a probability density functions (pdfs) for TL. The TL pdfs are incorporated into a recursive histogram filter as prior pdfs and used to process received signal amplitudes and generate posterior pdfs representing the probability of the source location. This paper examines how performance of the algorithm depends upon input signal-to-noise ratio.