TY - GEN
T1 - Detection in multiple channels having unequal noise power
AU - Sirianunpiboon, Songsri
AU - Howard, Stephen D.
AU - Cochran, Douglas
N1 - Publisher Copyright: © 2016 IEEE.
PY - 2016/8/24
Y1 - 2016/8/24
N2 - A Bayesian detector is formulated for the problem of detecting a signal of known rank using data collected at multiple sensors. The noise on each sensor channel is white and Gaussian, but its variance is unknown and may be different from channel to channel. A low-SNR assumption that enables approximation of one of the marginalization integrals in the likelihood ratio, yields a tractable approximate Bayesian detector for this regime. Performance of this detector is evaluated and compared to other recently introduced detectors.
AB - A Bayesian detector is formulated for the problem of detecting a signal of known rank using data collected at multiple sensors. The noise on each sensor channel is white and Gaussian, but its variance is unknown and may be different from channel to channel. A low-SNR assumption that enables approximation of one of the marginalization integrals in the likelihood ratio, yields a tractable approximate Bayesian detector for this regime. Performance of this detector is evaluated and compared to other recently introduced detectors.
KW - Bayesian detection
KW - Multiple-channel detection
KW - Uncalibrated receivers
UR - http://www.scopus.com/inward/record.url?scp=84987880325&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84987880325&partnerID=8YFLogxK
U2 - 10.1109/SSP.2016.7551793
DO - 10.1109/SSP.2016.7551793
M3 - Conference contribution
T3 - IEEE Workshop on Statistical Signal Processing Proceedings
BT - 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
PB - IEEE Computer Society
T2 - 19th IEEE Statistical Signal Processing Workshop, SSP 2016
Y2 - 25 June 2016 through 29 June 2016
ER -