Particle Filtering for Source Depth and Water Depth Joint Tracking in Shallow Water

Abstract

Environmental mismatch degrades the performance of source localization and tracking methods in shallow water. One solution is to estimate source parameters and the key environmental parameters simultaneously from the acoustic data. In this paper, an unconventional approach of joint tracking source depth and water depth parameters by a particle filter is proposed. This approach is free of prior environmental knowledge and numerical calculation of any forward model. First, a state-space model based on modal nature behavior is established driving the shallow-water propagation, instead of modeling in time or space, as was done previous works. Subsequently, particle filtering is employed for joint tracking, in which the evolution with mode-order of vertical wavenumbers and the relationship between state parameters and beam-wavenumber outputs transformed from the data are exploited. Final, the particle smoother reduces the uncertainty of state parameters at initial steps, and improves the overall tracking accuracy. Our approach is demonstrated using simulated data in an ideal waveguide and applied to shallow-water SWellEx-96 experimental data to substantiate its superior performance.