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In this paper, a novel multi-target Sparse Localization (SL) algorithm based on Compressive Sampling (CS) is proposed. Different from the existing literature for target counting and localization where signal/Received-Signal-Strength (RSS) readings at different Access Points (APs) are used separately, the authors propose to reformulate the SL problem so that they can make use of the cross-correlations of the signal readings at different APs. They analytically show that this new framework can provide a considerable amount of extra information compared to classical SL algorithms. They further highlight that in some cases this extra information converts the under-determined problem of SL into an over-determined problem for which they can use ordinary Least-Squares (LS) to efficiently recover the target vector even if it is not sparse.
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