2 research outputs found
Block-sparsity-based localization in wireless sensor networks
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
Block-sparsity-based localization in wireless sensor networks
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 accurac