An Improved Particle Filtering Technique for Source Localization and Sound Speed Field Inversion in Shallow Water

Both source localization and environmental inversions are practical problems for long-standing applications in underwater acoustics. This paper presents an approach of the moving source localization and sound speed field (SSF) inversion in shallow water. The approach is formulated in a state-space model with a state equation for both the source parameters (e.g., source depth, range, and speed) and SSF parameters (first three empirical orthogonal function coefficients, EOFs) and a measurement equation that incorporates underwater acoustic information via a vertical line array (VLA). As a sequential processing algorithm that operates on nonlinear systems with non-Gaussian probability densities, an improved sequential importance resampling type particle filtering (SIR PF) is proposed to counter degeneracy. The improved PF performs tracking of source and SSF parameters simultaneously, and evaluates their uncertainties in the form of time-evolving posterior probability densities (PPDs). The performance of improved PF is illustrated with well-tracked simulations of real-time source localization and time-varying SSF inversion. Moreover, the influence of different particle numbers on PF tracking accuracy and computational cost is also demonstrated. Simulation results show that the high-particle-number PF has an outperform performance. For a given hardware system, the reasonable compromise between accuracy and computational cost is a matter of tradeoff.