Computational Bayesian inference for active sonar localization under uncertainty in sound speed profile.

Abstract

A computational Bayesian approach is presented to address active sonar localization under the challenges of small receive aperture, uncertain sound speed profile (SSP), and limited coherence time. The approach draws inference on wavevectors associated with the closely spaced angle/Doppler spread arrivals, characterizing the scattered acoustic field. The wavevector posterior density is mapped to the scattering body's location and speed under an uncertain SSP using eigenray interpolation and marginalization. SSP uncertainty is captured by a multivariate Gaussian and a low-dimensional subspace mode representation. A case study using SSPs from the Mediterranean Sea demonstrates the efficacy of this approach.