Tracking of time-evolving sound speed profiles in shallow water using an ensemble Kalman-particle filter
MetadataShow full item record
KeywordAcoustic signal processing; Acoustic wave velocity; Hydrophones; Inverse problems; Kalman filters; Underwater acoustic propagation
This paper presents a tracking technique for performing sequential geoacoustic inversion monitoring range-independent environmental parameters in shallow water. The inverse problem is formulated in a state-space model with a state equation for the time-evolving sound speed profile (SSP) and a measurement equation that incorporates acoustic measurements via a hydrophone array. The particle filter (PF) is an ideal algorithm to perform tracking of environmental parameters for nonlinear systems with non-Gaussian probability densities. However, it has the problem of the mismatch between the proposal distribution and the a posterior probability distribution (PPD). The ensemble Kalman filter (EnKF) can obtain the PPD based on the Bayes theorem. A tracking algorithm improves the performance of the PF by employing the PPD of the EnKF as the proposal distribution of the PF. Tracking capabilities of this filter, the EnKF and the PF are compared with synthetic acoustic pressure data and experimental SSP data. Simulation results show the proposed method enables the continuous tracking of the range-independent SSP and outperforms the PF and the EnKF. Moreover, the complexity analysis is performed, and the computational complexity of the proposed method is greatly increased because of the combination of the PF and the EnKF.
Author Posting. © Acoustical Society of America, 2013. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 133 (2013): 1377-1386, doi:10.1121/1.4790354.
Suggested CitationJournal of the Acoustical Society of America 133 (2013): 1377-1386
Showing items related by title, author, creator and subject.
Kalman filter estimation of underwater vehicle position and attitude using Doppler velocity aided inertial motion unit Leader, Daniel Eugene (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 1994-09)This Paper explores the use of an extended Kalman filter to provide real-time estimates of underwater vehicle position and attitude. The types of previously available sensors are detailed including strapdown accelerometers, ...
Assessing performance of Bayesian state-space models fit to Argos satellite telemetry locations processed with Kalman filtering Silva, Monica A.; Jonsen, Ian; Russell, Deborah J. F.; Prieto, Rui; Thompson, Dave; Baumgartner, Mark F. (Public Library of Science, 2014-03-20)Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over ...
Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter Rastetter, Edward B.; Williams, Mathew; Griffin, Kevin L.; Kwiatkowski, Bonnie L.; Tomasky, Gabrielle; Potosnak, Mark J.; Stoy, Paul C.; Shaver, Gaius R.; Stieglitz, Marc; Hobbie, John E.; Kling, George W. (Ecological Society of America, 2010-07)Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal ...