Particle smoothers in sequential geoacoustic inversion.
Sequential Bayesian methods such as particle filters have been used to track a moving source in an unknown and space/time-evolving ocean environment. These methods treat both the source and the ocean parameters as non-stationary unknown random variables and track them via the multivariate posterior probability density function. Particle filters are numerical methods that can operate on nonlinear systems with non-Gaussian probability density functions. Particle smoothers are a natural extension to these filters. A smoother is appropriate in applications where data before and after the time of interest are readily available. Both past and "future" measurements are exploited in smoothers, whereas filters just use past measurements. Geoacoustic and source tracking is performed here using two smoother algorithms, the forward-backward smoother and the two-filter smoother. Smoothing is demonstrated on experimental data from both the SWellEx-96 and SW06 experiments where the parameter uncertainty is reduced relative to just filtering alone.
Marine Physical Laboratory, Scripps Institution of Oceanography, La Jolla, California 92093-0238, USA. cyardim@ucsd.edu
Caglar
C
Yardim
Peter
P
Gerstoft
William S
WS
Hodgkiss
D000162:Acoustics; D000465:Algorithms; D001499:Bayes Theorem; D005844:Geology / Q000379:methods*; D008962:Models, Theoretical; D009038:Motion; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D012815:Signal Processing, Computer-Assisted; D013016:Sound; D013018:Sound Spectrography; D013997:Time Factors; D014867:Water
D014867:Water