Abstract
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), which Jemmott [(2010)] successfully used to localize a moving source in range and depth. Jemmott’s Bayesian localization algorithm employs Monte Carlo simulations to build a probability density function (pdf) model for TL based on uncertainty in environmental parameters such as water column depth, sound speed profile, and bathymetry. The TL pdf models are incorporated into the recursive histogram filter as prior pdfs and used to process received signal amplitudes and generate a posterior pdf representing the likelihood of the source location. The localization algorithm has been shown to be robust to known uncertainty in environmental parameters, but other sources of uncertainty such as ambient noise have not been included in the work to date. This paper examines how performance of the algorithm depends on signal-to-noise ratio when the noise is not included in the prior distributions.
Published Version
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