Alessandro Bay, Diego Carrera, Sophie M. Fosson, Pasqualina Fragneto, Marco Grella, Chiara Ravazzi, Enrico Magli
EURASIP Journal on Wireless Communications and Networking, vol. 2015 n. 182, pp. 1-15, 2015
In this paper, we 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, we develop different RSS-fingerprinting localization algorithms and propose a dictionary optimization based on the notion of the coherence to improve the reconstruction efficiency. The proposed protocols are then compared with traditional fingerprinting methods both via simulation and on-field experiments. The results prove that our methods outperform the existing ones in terms of the achieved localization accuracy.