Geoacoustic and source tracking using particle filtering: experimental results.

A particle filtering (PF) approach is presented for performing sequential geoacoustic inversion of a complex ocean acoustic environment using a moving acoustic source. This approach treats both the environmental parameters [e.g., water column sound speed profile (SSP), water depth, sediment and bottom parameters] at the source location and the source parameters (e.g., source depth, range and speed) as unknown random variables that evolve as the source moves. This allows real-time updating of the environment and accurate tracking of the moving source. As a sequential Monte Carlo technique that operates on nonlinear systems with non-Gaussian probability densities, the PF is an ideal algorithm to perform tracking of environmental and source parameters, and their uncertainties via the evolving posterior probability densities. The approach is demonstrated on both simulated data in a shallow water environment with a sloping bottom and experimental data collected during the SWellEx-96 experiment.

[1]  E. Hamilton,et al.  SHEAR‐WAVE VELOCITY VERSUS DEPTH IN MARINE SEDIMENTS: A REVIEW , 1976 .

[2]  Finn B. Jensen,et al.  SNAP: The SACLANTCEN Normal-Mode Acoustic Propagation Model , 1979 .

[3]  E. Hamilton Sound velocity as a function of depth in marine sediments , 1985 .

[4]  Donald R. Del Balzo,et al.  Effects of water‐depth mismatch on matched‐field localization in shallow water , 1988 .

[5]  W A Kuperman,et al.  Focalization: environmental focusing and source localization. , 1991, The Journal of the Acoustical Society of America.

[6]  Loren W. Nolte,et al.  A posteriori probability source localization in an uncertain sound speed, deep ocean environment , 1991 .

[7]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[8]  M. D. Richardson,et al.  ON THE USE OF ACOUSTIC IMPEDANCE VALUES TO DETERMINE SEDIMENT PROPERTIES , 1993 .

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

[10]  William S. Hodgkiss,et al.  Mirages in shallow water matched‐field processing , 1995 .

[11]  Michael B. Porter,et al.  Computational Ocean Acoustics , 1994 .

[12]  Edmund J. Sullivan,et al.  Passive localization in ocean acoustics: A model‐based approach , 1995 .

[13]  Newell O. Booth,et al.  Geoacoustic databases for matched‐field processing: Preliminary results in shallow water off San Diego, California , 1996 .

[14]  K. Riedel Numerical Bayesian Methods Applied to Signal Processing , 1996 .

[15]  C. Mecklenbräuker,et al.  OBJECTIVE FUNCTIONS FOR OCEAN ACOUSTIC INVERSION DERIVED BY LIKELIHOOD METHODS , 1998 .

[16]  W.S. Hodgkiss,et al.  Detectability of low-level broad-band signals using adaptive matched-field processing with vertical aperture arrays , 2000, IEEE Journal of Oceanic Engineering.

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

[18]  L. A. Thompson,et al.  Broadband sound propagation in shallow water and geoacoustic inversion , 2003 .

[19]  N. Chapman,et al.  Benchmarking geoacoustic inversion methods for range-dependent waveguides , 2003 .

[20]  L. A. Thompson,et al.  Broadband sound propagation in shallow water and geoacoustic inversion. , 2000, The Journal of the Acoustical Society of America.

[21]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[22]  Loren W Nolte,et al.  Effects of environmental uncertainties on sonar detection performance prediction. , 2005, The Journal of the Acoustical Society of America.

[23]  Peter Gerstoft,et al.  Uncertainty analysis in matched-field geoacoustic inversions. , 2006, The Journal of the Acoustical Society of America.

[24]  Zoi-Heleni Michalopoulou,et al.  The effect of source amplitude and phase in matched field source localization , 2006 .

[25]  Validation of statistical estimation of transmission loss in the presence of geoacoustic inversion uncertainty , 2006 .

[26]  L. Sha,et al.  Bayesian Sonar Detection Performance Prediction With Source Position Uncertainty Using SWellEx-96 Vertical Array Data , 2006, IEEE Journal of Oceanic Engineering.

[27]  Edmund J. Sullivan,et al.  Broadband passive synthetic aperture: Experimental results , 2006 .

[28]  O. Carriere,et al.  Dynamic Estimation of the Sound-Speed Profile from Broadband Acoustic Measurements , 2007, OCEANS 2007 - Europe.

[29]  Stan E Dosso,et al.  Uncertainty estimation in simultaneous Bayesian tracking and environmental inversion. , 2008, The Journal of the Acoustical Society of America.

[30]  Ivan Zorych,et al.  Particle filtering for dispersion curve tracking in ocean acoustics. , 2008, The Journal of the Acoustical Society of America.

[31]  R Lee Culver,et al.  Sonar signal processing using probabilistic signal and ocean environmental models. , 2008, The Journal of the Acoustical Society of America.

[32]  James V. Candy,et al.  Bayesian Signal Processing , 2009 .

[33]  James V. Candy,et al.  Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods , 2009 .

[34]  Chensong He,et al.  Enhanced Kalman Filter Algorithm Using the Invariance Principle , 2009, IEEE Journal of Oceanic Engineering.

[35]  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.

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

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

[38]  Stan E Dosso,et al.  Comparison of focalization and marginalization for Bayesian tracking in an uncertain ocean environment. , 2009, The Journal of the Acoustical Society of America.