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

• 作 者: JIN Liling LI Jianlong XU Wen
• 机 构: College of Information Science and Electronic Engineering,Zhejiang University,Key Laboratory of Ocea
• 出 处: Chinese Journal of Acoustics
• 发表日期: 2017年03期
• 关键词: regressive shallow Tracking utilized ocean ensemble validate predicting modeled

An approach for time-evolving sound speed profiles tracking in shallow water is discussed.The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem,which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements.In the paper,auto-regression(AR) method is introduced to obtain a 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 the 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 at low signal-to-noise ratios.

An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements. In the paper, auto-regression (AR) method is introduced to obtain a 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 the sound velocity measurement data. Compared with the 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 at low signa l-to-noise ratios.