Distributed detection of a non-cooperative target via generalized locally-optimum approaches |
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| Affiliation: | 1. University of Naples “Federico II”, DIETI, Via Claudio 21, 80125 Naples, Italy;2. Department of Electronics and Telecommunications, Norwegian University of Science and Technology, Trondheim, Norway;1. Department of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111, Iran;2. Department of Electrical and Computer Engineering, McMaster University, Hamilton L8S 4L8, Canada;3. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor 48109, USA;4. Uinversity of Michigan Center for Integrative Research in Critical Care, Ann Arbor 48109, USA;1. School of Computer Science, China University of Geosciences, Wuhan, 430074, China;2. College of Business, University of Texas at San Antonio, USA;3. School of Information Technology and Mathematical Sciences, University of South Australia, Australia;4. Department of Systems Data Processing and Computers, Polytechnic University of Valencia, Spain;5. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, China;6. Hubei Key Laboratory of Intelligent Geo-information Processing, China University of Geosciences, Wuhan 430074, China;1. Wells Fargo & Company, San Francisco, CA, USA;2. The H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA;1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China;2. State Grid Information & Communication Company of Hunan Electric Power Company, Changsha,410007, China |
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| Abstract: | In this paper we tackle distributed detection of a non-cooperative target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an unknown random signal with amplitude attenuation depending on the distance between the sensor and the target (unknown) positions, embedded in white Gaussian noise. The Fusion Center (FC) receives sensors decisions through error-prone Binary Symmetric Channels (BSCs) and is in charge of performing a (potentially) more-accurate global decision. The resulting problem is a one-sided testing with nuisance parameters present only under the target-present hypothesis. We first focus on fusion rules based on Generalized Likelihood Ratio Test (GLRT), Bayesian and hybrid approaches. Then, aimed at reducing the computational complexity, we develop fusion rules based on generalizations of the well-known Locally-Optimum Detection (LOD) framework. Finally, all the proposed rules are compared in terms of performance and complexity. |
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