Abstract
A model‐based recursive Bayesian signal processing framework is shown to localize a moving source emitting a low‐frequency tonal signal in a shallow water environment. Source motion maps spatial variation in transmission loss into amplitude modulation of the signal received on a passive horizontal array. Acoustic propagation modeling predicts this variability, which is used to estimate source range, depth, range rate, and acoustic level. Uncertainty in transmission loss resulting from uncertainty in environmental parameters is predicted using Monte Carlo modal propagation modeling. Monte Carlo marginalization over environmental uncertainty provides robustness against data‐model mismatch. The maximum entropy method is used to construct a probability density function (pdf) of transmission loss at each range depth location based on the Monte Carlo results. The resulting pdfs belong to the exponential family and result in an implementable recursive Bayesian processor. The physics of acoustic modeling determine the form of the processor through the transmission loss pdfs and are an intimate part of the localization technique. This processor is distinct from Bayesian matched field processing in that it neither relies on a vertical array nor computes modal amplitudes from received data. Results using SWellEx‐96 will be shown. [Work supported by ONR Undersea Signal Processing.]
Published Version
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