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Detection of Gauss-Markov Random Field on Nearest-Neighbor Graph

Anandkumar, Animashree and Tong, Lang and Swami, Ananthram (2007) Detection of Gauss-Markov Random Field on Nearest-Neighbor Graph. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE , Piscataway, NJ, pp. 829-832. ISBN 1-4244-0727-3.

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The problem of hypothesis testing against independence for a Gauss-Markov random field (GMRF) with nearest-neighbor dependency graph is analyzed. The sensors measuring samples from the signal field are placed IID according to the uniform distribution. The asymptotic performance of Neyman-Pearson detection is characterized through the large-deviation theory. An expression for the error exponent is derived using a special law of large numbers for graph functionals. The exponent is analyzed for different values of the variance ratio and correlation. It is found that a more correlated GMRF has a higher exponent (improved detection performance) at low values of the variance ratio, whereas the opposite is true at high values of the ratio.

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Additional Information:© 2007 IEEE. This work was supported in part through the collaborative participation in the Communications and Networks Consortium sponsored by the U. S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0011 and by the National Science Foundation under Contract CNS-0435190. The third author was partially supported by the DARPA ITMANET program.
Funding AgencyGrant Number
Army Research Laboratory (ARL)DAAD19-01-2-0011
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Subject Keywords:Graph theory, Signal detection, Gaussian processes, Markov processes, Error analysis
Record Number:CaltechAUTHORS:20170920-152542556
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Official Citation:A. Anandkumar, L. Tong and A. Swami, "Detection of Gauss-Markov Random Field on Nearest-Neighbor Graph," 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, Honolulu, HI, 2007, pp. III-829-III-832. doi: 10.1109/ICASSP.2007.366808 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:81644
Deposited By: Tony Diaz
Deposited On:20 Sep 2017 22:36
Last Modified:15 Nov 2021 19:44

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