DocumentCode :
1415706
Title :
Distributed Estimation of Gauss - Markov Random Fields With One-Bit Quantized Data
Author :
Fang, Jun ; Li, Hongbin
Author_Institution :
Dept. of Electr. & Comput. Eng., Stevens Inst. of Technol., Hoboken, NJ, USA
Volume :
17
Issue :
5
fYear :
2010
fDate :
5/1/2010 12:00:00 AM
Firstpage :
449
Lastpage :
452
Abstract :
We consider the problem of distributed estimation of a Gauss-Markov random field using a wireless sensor network (WSN), where due to the stringent power and communication constraints, each sensor has to quantize its data before transmission. In this case, the convergence of conventional iterative matrix-splitting algorithms is hindered by the quantization errors. To address this issue, we propose a one-bit adaptive quantization approach which leads to decaying quantization errors. Numerical results show that even with one bit quantization, the proposed approach achieves a superior mean square deviation performance (with respect to the global linear minimum mean-square error estimate) within a moderate number of iterations.
Keywords :
Gaussian distribution; Markov processes; estimation theory; quantisation (signal); Gauss-Markov random field distributed estimation; communication constraints; data transmission; decaying quantization errors; global linear minimum mean-square error estimate; iterative matrix-splitting algorithms; one-bit adaptive quantization approach; one-bit quantized data; stringent power; superior mean square deviation; wireless sensor network; Adaptive quantization (AQ); Gauss–Markov random fields (GMRFs); distributed estimation;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2010.2043157
Filename :
5411756
Link To Document :
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