TY - GEN
T1 - Parameter estimation from multiple sensors with mixed resolution of quantization
AU - Heiman, Elad
AU - Messer, Hagit
N1 - Publisher Copyright:
© Copyright 2015 IEEE All rights reserved.
PY - 2014
Y1 - 2014
N2 - To comply with system requirements (e.g., power consumption, bandwidth) in many wireless sensor networks, the signals are roughly quantized. In this paper we address the case in which different sensors in the network sense autonomously a physical parameter field and are quantized in different quantization resolution. We present the Maximum Likelihood estimator (MLE) for general parameter estimation in such a case, and we study in details the special case of linear parameter estimation, for which we compare the MLE to the Naive MLE (NMLE) which disregards the quantization. While being asymptotically optimal, the MLE is a complex processor. Therefore, we suggest a sub-optimal estimator, simpler than the MLE, whose performance is close to the optimal one, being better than that of the NMLE. Simulation results demonstrate the operation of the different estimators in various conditions.
AB - To comply with system requirements (e.g., power consumption, bandwidth) in many wireless sensor networks, the signals are roughly quantized. In this paper we address the case in which different sensors in the network sense autonomously a physical parameter field and are quantized in different quantization resolution. We present the Maximum Likelihood estimator (MLE) for general parameter estimation in such a case, and we study in details the special case of linear parameter estimation, for which we compare the MLE to the Naive MLE (NMLE) which disregards the quantization. While being asymptotically optimal, the MLE is a complex processor. Therefore, we suggest a sub-optimal estimator, simpler than the MLE, whose performance is close to the optimal one, being better than that of the NMLE. Simulation results demonstrate the operation of the different estimators in various conditions.
KW - Parameter estimation
KW - Physical field
KW - Quantization resolution
KW - Signal processing
KW - Wireless sensor network
UR - http://www.scopus.com/inward/record.url?scp=84941236779&partnerID=8YFLogxK
U2 - 10.1109/EEEI.2014.7005733
DO - 10.1109/EEEI.2014.7005733
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AN - SCOPUS:84941236779
T3 - 2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
BT - 2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
Y2 - 3 December 2014 through 5 December 2014
ER -