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Bayesian approach of nearfield acoustic reconstruction with particle filters

DSpace at IIT Bombay

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Title Bayesian approach of nearfield acoustic reconstruction with particle filters
 
Creator BAI, MSR
AGARWAL, A
CHEN, CC
WANG, YC
 
Subject LEAST-SQUARES METHOD
SOUND SOURCES
TRACKING
ENVIRONMENT
FIELD
 
Description This paper demonstrates that inverse source reconstruction can be performed using a methodology of particle filters that relies primarily on the Bayesian approach of parameter estimation. In particular, the proposed approach is applied in the context of nearfield acoustic holography based on the equivalent source method (ESM). A state-space model is formulated in light of the ESM. The parameters to estimate are amplitudes and locations of the equivalent sources. The parameters constitute the state vector which follows a first-order Markov process with the transition matrix being the identity for every frequency-domain data frame. Filtered estimates of the state vector obtained are assigned weights adaptively. The implementation of recursive Bayesian filters involves a sequential Monte Carlo sampling procedure that treats the estimates as point masses with a discrete probability mass function (PMF) which evolves with iteration. The weight update equation governs the evolution of this PMF and depends primarily on the likelihood function and the prior distribution. It is evident from the simulation results that the inclusion of the appropriate prior distribution is crucial in the parameter estimation. (C) 2013 Acoustical Society of America.
 
Publisher ACOUSTICAL SOC AMER AMER INST PHYSICS
 
Date 2014-10-17T05:22:07Z
2014-10-17T05:22:07Z
2013
 
Type Article
 
Identifier JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 133(6)4032-4043
0001-4966
1520-8524
http://dx.doi.org/10.1121/1.4803861
http://dspace.library.iitb.ac.in/jspui/handle/100/16055
 
Language en