Detection of Gauss-Markov Random Field on Nearest-Neighbor Graph
Abstract
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.
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.Attached Files
Published - 04217838.pdf
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Additional details
- Eprint ID
- 81644
- Resolver ID
- CaltechAUTHORS:20170920-152542556
- DAAD19-01-2-0011
- Army Research Laboratory (ARL)
- CNS-0435190
- NSF
- Defense Advanced Research Projects Agency (DARPA)
- Created
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2017-09-20Created from EPrint's datestamp field
- Updated
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2021-11-15Created from EPrint's last_modified field