TY - GEN
T1 - Universal decentralized estimation in a bandwidth constrained sensor network
AU - Luo, Zhi-Quan
AU - Xiao, Jin Jun
PY - 2005/1/1
Y1 - 2005/1/1
N2 - We consider universal decentralized estimation of a noise-corrupted signal by a bandwidth constrained sensor network with a fusion center (FC). We show that in a homogeneous sensing environment and under a bandwidth constraint of 1-bit per sample per node, there exist universal decentralized estimation schemes (DES) with a mean squared error (MSE) decreasing at the rate 1/K, where K is the total number of sensors. We extend such 1-bit decentralized estimators to the case of inhomogeneous sensing environment, and propose quantization and transmission power control strategies for local sensors in order to minimize the total consumed sensor energy while ensuring a given MSE performance. We also design a DES for the joint estimation of a vector source based on its noisy and linearly distorted observations, and show that to achieve a MSE within a factor of 2 away from the best linear unbiased estimator (BLUE), the local message length has a nice form of being the channel capacity of "a virtual AWGN channel" from "nature" to each local sensor.
AB - We consider universal decentralized estimation of a noise-corrupted signal by a bandwidth constrained sensor network with a fusion center (FC). We show that in a homogeneous sensing environment and under a bandwidth constraint of 1-bit per sample per node, there exist universal decentralized estimation schemes (DES) with a mean squared error (MSE) decreasing at the rate 1/K, where K is the total number of sensors. We extend such 1-bit decentralized estimators to the case of inhomogeneous sensing environment, and propose quantization and transmission power control strategies for local sensors in order to minimize the total consumed sensor energy while ensuring a given MSE performance. We also design a DES for the joint estimation of a vector source based on its noisy and linearly distorted observations, and show that to achieve a MSE within a factor of 2 away from the best linear unbiased estimator (BLUE), the local message length has a nice form of being the channel capacity of "a virtual AWGN channel" from "nature" to each local sensor.
UR - http://www.scopus.com/inward/record.url?scp=33646814433&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646814433&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2005.1416137
DO - 10.1109/ICASSP.2005.1416137
M3 - Conference contribution
AN - SCOPUS:33646814433
SN - 0780388747
SN - 9780780388741
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - 2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Signal Proces. Education, Spec. Sessions
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Y2 - 18 March 2005 through 23 March 2005
ER -