Detection of Gauss-Markov random field on nearest-neighbor graph

被引：0
|
作者：
Anandkumar, Animashree [1 ]
Tong, Lang [1 ]
Swami, Ananthram [2 ]
机构：
[1] Cornell Univ, Ithaca, NY 14853 USA
[2] Army Res Lab, Adelphi, MD 20783 USA
来源：
2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL III, PTS 1-3, PROCEEDINGS | 2007年
基金：
美国国家科学基金会;
关键词：
signal detection; Gaussian processes; Markov processes; error analysis; graph theory;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
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.
引用
收藏
页码：829 / +
页数：2
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