Experimental Demonstration of Acoustic Inversion Using an AUV Carrying Source

This paper presents results of the sound speed profiles (SSPs) inversion using a data set obtained in shallow water of South China Sea in 2016 summer with an autonomous underwater vehicle (AUV) carrying source. In-site acoustic data collected by the vertical line array (VLA) are analyzed and employed for the inversion of time-evolving SSPs. The inversion is formulated as a tracking problem with the state-space model, in which the SSPs are parameterized by the empirical orthogonal functions (EOFs) to reduce the degree of freedom. The differ- ential evolution (DE) is implemented in the sequential process to maximize the a posterior probability. Results demonstrate the validation of the method for the SSP inversion, and the inversion errors are discussed.

[1]  Peter Gerstoft,et al.  Effect of ocean sound speed uncertainty on matched-field geoacoustic inversion. , 2008, The Journal of the Acoustical Society of America.

[2]  Michel Rixen,et al.  Full-field tomography and Kalman tracking of the range-dependent sound speed field in a coastal water environment , 2009 .

[3]  P. L. Houtekamer,et al.  Ensemble Kalman filtering , 2005 .

[4]  Stan E. Dosso,et al.  An adaptive-hybrid algorithm for geoacoustic inversion , 2001 .

[5]  S. Dosso Quantifying uncertainty in geoacoustic inversion. I. A fast Gibbs sampler approach. , 2002, The Journal of the Acoustical Society of America.

[6]  Peter Gerstoft,et al.  Inversion of seismoacoustic data using genetic algorithms and a posteriori probability distributions , 1994 .

[7]  Carl Wunsch,et al.  Ocean acoustic tomography: a scheme for large scale monitoring , 1979 .

[8]  S. Dosso,et al.  Quantifying uncertainty in geoacoustic inversion. II. Application to broadband, shallow-water data. , 2002, The Journal of the Acoustical Society of America.

[9]  Wen Xu,et al.  A method for tracking time-evolving sound speed profiles using Kalman filters. , 2014, The Journal of the Acoustical Society of America.

[10]  Peter Gerstoft,et al.  Geoacoustic and source tracking using particle filtering: experimental results. , 2010, The Journal of the Acoustical Society of America.

[11]  Peter Gerstoft,et al.  Parameter estimation using multifrequency range‐dependent acoustic data in shallow water , 1996 .

[12]  J.V. Candy,et al.  Inversion for Time-Evolving Sound-Speed Field in a Shallow Ocean by Ensemble Kalman Filtering , 2009, IEEE Journal of Oceanic Engineering.

[13]  Henry Cox,et al.  On the estimation of state variables and parameters for noisy dynamic systems , 1964 .

[14]  Peter Gerstoft,et al.  Inversion of broad-band multitone acoustic data from the YELLOW SHARK summer experiments , 1996 .

[15]  F. Middleton,et al.  An underwater acoustic sound velocity data model , 1980 .

[16]  E. J. Sullivan,et al.  Model‐based environmental inversion: A shallow water ocean application , 1995 .

[17]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[18]  Chen Sun,et al.  Inversion of the sound speed profiles with an AUV carrying source using improved ensemble Kalman filter , 2016, 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[19]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[20]  Peter Gerstoft,et al.  An Overview of Sequential Bayesian Filtering in Ocean Acoustics , 2011, IEEE Journal of Oceanic Engineering.

[21]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[22]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[23]  V. A. D. Grosso New equation for the speed of sound in natural waters (with comparisons to other equations) , 1974 .

[24]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[25]  Peter Gerstoft,et al.  Tracking of geoacoustic parameters using Kalman and particle filters. , 2009, The Journal of the Acoustical Society of America.

[26]  Hui Zhou,et al.  Tracking of time-evolving sound speed profiles in shallow water using an ensemble Kalman-particle filter. , 2013, The Journal of the Acoustical Society of America.

[27]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .