Tracking of time-evolving sound speed profiles with the auto-regressive state-space model

A tracking approach of time-evolving sound speed profiles suitable to shallow water is discussed. Inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for the time-evolving sound speed profile and a measurement equation that incorporates local acoustic measurements. Here, auto-regression (AR) method is introduced to obtain high-order AR evolution model of the sound speed field time variations, and the ensemble Kalman filter is utilized to track the sound speed field. To validate the approach, the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and sound velocity measurement data. Compared with traditional approaches based on the state evolution represented as a random walk, simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed, and still keep good tracking performance with low signal-to-noise ratios.