Enlarged Deployment Regions to Circumvent the Conditional Dependence and Composite Hypothesis Problems in Sensor Detection Systems

It is usually difficult to design randomly deployed sensor systems to detect a signal emitter in a region of interest because measurements are conditionally dependent in general and the alternative hypothesis is composite. To circumvent these problems, this paper presents two system design approaches: in Approach 1, a modified decay function is considered; in Approach 2, a modified region of interest and a suitable distribution for the emitter location are considered; and both approaches use enlarged sensor deployment regions. It is shown that both approaches cause the measurements to become conditionally independent and identically distributed, cause the alternative hypothesis to become simple, and generate designs that ensure a detection performance. This paper further evaluates how conservative each approach is and compares them, helping a designer choose the most suitable approach for a situation.

[1]  Teng Joon Lim,et al.  Composite Hypothesis Testing for Cooperative Spectrum Sensing in Cognitive Radio , 2009, 2009 IEEE International Conference on Communications.

[2]  E. Lehmann Testing Statistical Hypotheses , 1960 .

[3]  Pramod K. Varshney,et al.  Distributed detection in a large wireless sensor network , 2006, Inf. Fusion.

[4]  Rick S. Blum,et al.  On the Limitations of Random Sensor Placement for Distributed Signal Detection , 2009 .

[5]  Charles W. Therrien,et al.  Probability and Random Processes for Electrical and Computer Engineers , 2011 .

[6]  Rick S. Blum,et al.  The good, bad and ugly: distributed detection of a known signal in dependent Gaussian noise , 2000, IEEE Trans. Signal Process..

[7]  Pramod K. Varshney,et al.  Performance Analysis of Distributed Detection in a Random Sensor Field , 2008, IEEE Transactions on Signal Processing.

[8]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[9]  D. Torney,et al.  Distributed sensor networks for detection of mobile radioactive sources , 2004, IEEE Transactions on Nuclear Science.

[10]  Zhi-Quan Luo,et al.  Universal decentralized detection in a bandwidth-constrained sensor network , 2005, IEEE Trans. Signal Process..

[11]  Venugopal V. Veeravalli,et al.  Decentralized detection in sensor networks , 2003, IEEE Trans. Signal Process..

[12]  Pramod K. Varshney,et al.  Distributed detection of a nuclear radioactive source using fusion of correlated decisions , 2007, 2007 10th International Conference on Information Fusion.

[13]  H. Vincent Poor,et al.  Minimax robust decentralized detection , 1994, IEEE Trans. Inf. Theory.

[14]  Benedito J. B. Fonseca,et al.  Least Favorable Distributions for the Design of Randomly Deployed Sensor Detection Systems , 2014, IEEE Transactions on Information Theory.

[15]  Peter Willett,et al.  Bayesian Data Fusion for Distributed Target Detection in Sensor Networks , 2010, IEEE Transactions on Signal Processing.

[16]  S. Sitharama Iyengar,et al.  Identification of low-level point radioactive sources using a sensor network , 2010, TOSN.

[17]  Benedito Jose Barreto Fonseca On the use of least favorable distributions to facilitate the design of randomly deployed sensor detection systems , 2012 .