Description: 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.
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