Localization of a mobile acoustic scatterer from sub-Rayleigh resolved acoustic arrivals in a refractive environment

Computational Bayesian inference on the state of an underwater mobile object from a continuous active acoustic transmission is presented. The challenge of sub-Rayleigh resolvable wave vectors in refractive environments with uncertainty in ambient acoustic noise power is addressed. The location and speed of the mobile scatterer are inferred under the challenging constraint of a small receive vertical aperture. The need for jointly inferring the vertical angles and Doppler offsets of the arrivals is addressed with a Gibbs sampling approach. The posterior density of the plane wave components is mapped to the object's range, depth, and speed through ray interpolation. A case scenario from an acoustic duct environment in the western Indian ocean is presented.Computational Bayesian inference on the state of an underwater mobile object from a continuous active acoustic transmission is presented. The challenge of sub-Rayleigh resolvable wave vectors in refractive environments with uncertainty in ambient acoustic noise power is addressed. The location and speed of the mobile scatterer are inferred under the challenging constraint of a small receive vertical aperture. The need for jointly inferring the vertical angles and Doppler offsets of the arrivals is addressed with a Gibbs sampling approach. The posterior density of the plane wave components is mapped to the object's range, depth, and speed through ray interpolation. A case scenario from an acoustic duct environment in the western Indian ocean is presented.