TY - GEN
T1 - Factorized variational approximations for acoustic multi source localization
AU - Cevher, V.
AU - Sankaranarayanan, A. C.
AU - Chellappa, R.
PY - 2008
Y1 - 2008
N2 - Estimation based on received signal strength (RSS) is crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present a Gaussian approximation of the Chi distribution that is applicable to general RSS source localization problems in sensor networks. Using our Gaussian approximation, we provide a factorized variational Bayes (VB) approximation to the location and power posterior of multiple sources using a sensor network. When the source signal and the sensor noise have uncorrelated Gaussian distributions, we demonstrate that the envelope of the sensor output can be accurately modeled with a multiplicative Gaussian noise model. In turn, our factorized VB approximations decrease the computational complexity and provide computational robustness as the number of targets increases. Simulations are provided to demonstrate the effectiveness of the proposed approximations.
AB - Estimation based on received signal strength (RSS) is crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present a Gaussian approximation of the Chi distribution that is applicable to general RSS source localization problems in sensor networks. Using our Gaussian approximation, we provide a factorized variational Bayes (VB) approximation to the location and power posterior of multiple sources using a sensor network. When the source signal and the sensor noise have uncorrelated Gaussian distributions, we demonstrate that the envelope of the sensor output can be accurately modeled with a multiplicative Gaussian noise model. In turn, our factorized VB approximations decrease the computational complexity and provide computational robustness as the number of targets increases. Simulations are provided to demonstrate the effectiveness of the proposed approximations.
KW - Multisensor systems
KW - Object tracking
KW - Stochastic approximation
KW - Variational methods
UR - http://www.scopus.com/inward/record.url?scp=51449105837&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51449105837&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2008.4518133
DO - 10.1109/ICASSP.2008.4518133
M3 - Conference contribution
AN - SCOPUS:51449105837
SN - 1424414849
SN - 9781424414840
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2409
EP - 2412
BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
T2 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Y2 - 31 March 2008 through 4 April 2008
ER -