TY - GEN
T1 - Non-parametric distributed quantization-estimation using wireless sensor networks
AU - Ribeiro, Alejandro
AU - Giannakis, Georgios B.
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - Wireless sensor networks deployed to perform surveillance and monitoring tasks have to operate under stringent energy and bandwidth limitations. These motivate well distributed estimation scenarios where sensors quantize and transmit only one, or a few bits per observation, for use in forming parameter estimators of interest. In a companion paper, we developed algorithms and studied interesting tradeoffs that emerge even in the simplest distributed setup of estimating a scalar location parameter in the presence of zero-mean additive white Gaussian noise of known variance. Herein, we derive distributed estimators based on binary observations along with their error-variance performance for unknown noise pdfs.
AB - Wireless sensor networks deployed to perform surveillance and monitoring tasks have to operate under stringent energy and bandwidth limitations. These motivate well distributed estimation scenarios where sensors quantize and transmit only one, or a few bits per observation, for use in forming parameter estimators of interest. In a companion paper, we developed algorithms and studied interesting tradeoffs that emerge even in the simplest distributed setup of estimating a scalar location parameter in the presence of zero-mean additive white Gaussian noise of known variance. Herein, we derive distributed estimators based on binary observations along with their error-variance performance for unknown noise pdfs.
UR - http://www.scopus.com/inward/record.url?scp=33646761004&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646761004&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2005.1415945
DO - 10.1109/ICASSP.2005.1415945
M3 - Conference contribution
AN - SCOPUS:33646761004
SN - 0780388747
SN - 9780780388741
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 61
EP - 64
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 -