Sparsity promoting decentralized learning strategies for radio tomographic imaging using consensus based ADMM approach |
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| Institution: | 1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China;2. School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, Hubei 430205, PR China;1. School of Artificial Intelligence and Automation, Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan, China;2. China Ship Development and Design Center, Wuhan, China;1. the Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200237, China;2. Shanghai Institute of Space Power-Sources, Shanghai 200245, China;3. State Key Laboratory of Space Power-sources Technology, Shanghai 200245, China;4. Shanghai Power & Energy Storage Battery System Engineering Tech Co. Ltd, Shanghai 200245, China;1. School of Mathematics and Statistics, Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun 130024, Jilin Province, China;2. College of Science, China University of Petroleum, Qingdao 266580, Shandong Province, China;3. Logistics Industry Economy and Intelligent Logistics Laboratory, Jilin University of Finance and Economics, Changchun 130117, Jilin Province, China |
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| Abstract: | Radio tomographic imaging (RTI) has wide applications in the detection and tracking of objects that do not require any sensor to be attached to the object. Consequently, it leads to device-free localization (DFL). RTI uses received signal strength (RSS) at different sensor nodes for imaging purposes. The attenuation maps, known as spatial loss fields (SLFs), measure the power loss at each pixel in the wireless sensor network (WSN) of interest. These SLFs help us to detect obstacles and aid in the imaging of objects. The centralized RTI system requires the information of all sensor nodes available at the fusion centre (FC), which in turn increases the communication overhead. Furthermore, the failure of links may lead to improper imaging in the RTI system. Hence, a distributed approach for the RTI system resolves such problems. In this paper, a consensus-based distributed strategy is used for distributed estimation of the SLF. The major contribution of this work is to propose a fully decentralized RTI system by using a consensus-based alternating direction method of multipliers (ADMM) algorithm to alleviate the practical issues with centralized and distributed incremental strategies. We proposed distributed consensus ADMM (DCADMM-RTI) and distributed sparse consensus ADMM (DSCADMM-RTI) for the RTI system to properly localize targets in a distributed fashion. Furthermore, the effect of quantization noise is verified by using the distributed consensus algorithms while sharing the quantized data among the neighbourhoods. |
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