Block-Sparsity-Based Localization in Wireless Sensor Networks

In this paper, the authors deal with the localization problem in wireless sensor networks, where a target sensor location must be estimated starting from few measurements of the power present in a radio signal received from sensors with known locations. Inspired by the recent advances in sparse approximation, the localization problem is recast as a block-sparse signal recovery problem in the discrete spatial domain. In this paper, they develop different RSS-fingerprinting (Received Signal Strength) localization algorithms and propose a dictionary optimization based on the notion of the coherence to improve the reconstruction efficiency.

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