Abstract
Radio tomographic imaging as a coarse computational imaging method has attracted much attentions due to its potential applications in human surveillance. It utilizes the shadow fading characteristics of radio signals among wireless sensor network nodes to infer the targets' localization. The recovery process involves a large number of scanning links, which brings heavy burdens on node energy, communication routes and data storage. In general, there is a small number of targets and could be seen as a sparse signal compared with the original high-dimensional space. Hence, the key of scene recovery is the effective links selected. This part of work presents a heuristic link selection for radio tomographic localization system, which introduces a Bayesian compressive sensing (BCS) to heuristic link selection method for scene imaging reconstruction.
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