Bayesian navigation in shallow water using passive acoustics

Abstract

Autonomous underwater vehicle (AUV) navigation relying on active acoustic sources causes noise pollution, while dead reckoning leads to a localization error that increases with time. Therefore, AUV navigation based on passive acoustics is appealing. However, for AUV navigation, extracting location information with passive acoustics is a challenging signal processing task. Due to the small form factor of an AUV as a sensing platform, only a single hydrophone or a small aperture hydrophone array can be used as an acoustic sensor. Furthermore, the acoustic signals originate from uncooperative sources. Here, we propose a Bayesian navigation approach for an AUV that exploits acoustic signals generated from sources of opportunity (SOOs) in a shallow water environment. The waveguide invariant (WI) parameter is estimated from cross-correlation coefficients of non-linearly transformed tonal signals of an SOO. It is assumed that the location information of the SOO is transmitted by an automatic identification system. Additionally, the range rate is inferred using the spectrum of cross-correlatedacoustic fields over a time interval. The WI parameter estimate, the range rate estimate, and inertial measurements are fused in a Bayesian parameter estimation approach. The navigation capability is demonstrated using simulated and real data from the SwellEx-96 experiment.