DocumentCode :
3636358
Title :
High-rate quantization for the Neyman-Pearson detection of Hidden Markov Processes
Author :
Joffrey Villard;Pascal Bianchi;Éric Moulines;Pablo Piantanida
Author_Institution :
SUPELEC, Telecom. Dpt., Gif-sur-Yvette, France
fYear :
2010
Firstpage :
1
Lastpage :
5
Abstract :
This paper investigates the decentralized detection of Hidden Markov Processes using the Neyman-Pearson test. We consider a network formed by a large number of distributed sensors. Sensors´ observations are noisy snapshots of a Markov process to be detected. Each (real) observation is quantized on log2(N) bits before being transmitted to a fusion center which makes the final decision. For any false alarm level, it is shown that the miss probability of the Neyman-Pearson test converges to zero exponentially as the number of sensors tends to infinity. The error exponent is provided using recent results on Hidden Markov Models. In order to obtain informative expressions of the error exponent as a function of the quantization rule, we further investigate the case where the number N of quantization levels tends to infinity, following the approach developed in. In this regime, we provide the quantization rule maximizing the error exponent. Illustration of our results is provided in the case of the detection of a Gauss-Markov signal in noise. In terms of error exponent, the proposed quantization rule significantly outperforms the one proposed by for i.i.d. observations.
Keywords :
"Quantization","Hidden Markov models","Testing","Wireless sensor networks","Telecommunications","H infinity control","Markov processes","Gaussian noise","Signal design","Stochastic processes"
Publisher :
ieee
Conference_Titel :
Information Theory (ITW 2010, Cairo), 2010 IEEE Information Theory Workshop on
Print_ISBN :
978-1-4244-6372-5
Type :
conf
DOI :
10.1109/ITWKSPS.2010.5503209
Filename :
5503209
Link To Document :
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