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Sparse target counting and localization in sensor networks based on compressive sensing

Sparse target counting and localization in sensor networks based on compressive sensing,10.1109/INFCOM.2011.5935041,Bowu Zhang,Xiuzhen Cheng,Nan Zhang

Sparse target counting and localization in sensor networks based on compressive sensing   (Citations: 1)
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In this paper, we propose a novel compressive sensing (CS) based approach for sparse target counting and positioning in wireless sensor networks. While this is not the first work on applying CS to count and localize targets, it is the first to rigorously justify the validity of the problem formulation. Moreover, we propose a novel greedy matching pursuit algorithm (GMP) that complements the well-known signal recovery algorithms in CS theory and prove that GMP can accurately recover a sparse signal with a high probability. We also propose a framework for counting and positioning targets from multiple categories, a novel problem that has never been addressed before. Finally, we perform a comprehensive set of simulations whose results demonstrate the superiority of our approach over the existing CS and non-CS based techniques.
Conference: IEEE INFOCOM - INFOCOM , pp. 2255-2263, 2011
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    • ... a value ‘1’ if one or more targets are detected in its sensing range and ‘0’ otherwise; (2) topological integration based approaches [19], [20] aim to obtain the expected target count in sensor networks based on the topological integration theory; (3) clustering based approaches [7], [8], [21] intend to identify multiple non-overlapping clusters, each of which containing one or more targets; and (4) statistics based approaches [9], [22] ...
    • ...sensing (CS) based approach [22] relies on the CS theory to estimate the number of targets in sensor networks...
    • ...distance. Notice that most existing target counting algorithms [7], [8], [18], [22] only consider the case where the targets are sparsely deployed...

    Dengyuan Wuet al. A Monte Carlo Method for Mobile Target Counting

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