Abstract
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.
Highlights
In the last decade, localization services have become widespread in everyday life, supported by the advances in communication technologies that allow to detect the position of people and devices [1]
Our other proposed algorithm based on block-sparsity performs close to spatial averages approaches in case of low noise and only for high values of σ it behaves worse than the spatial sparsity method [9] and the weighted k-nearest neighbor (WkNN) [17]
With 3 × 2 cells hierarchical method, we incur a mean localization error that is always higher than, or at most the same as, WkNN, but our method is better than spatial sparsity, except for very high noise cases
Summary
Localization services have become widespread in everyday life, supported by the advances in communication technologies that allow to detect the position of people and devices [1]. The presence of obstacles causes inaccuracies using methods based on the measurement of the received signal strength (RSS), that is, the power transmitted by the device to be localized. While more robust and accurate, RSS-fingerprinting methods require the exchange of a large number of data between the receiver and the transmitter to achieve the desired level of performance This issue has been recently tackled by recasting localization into a sparse approximation problem [9,10,11]. Block-sparsity assumes that the non-zero entries of the sparse signal to be reconstructed are confined in some blocks, while other segments are completely empty; recent work [13, 14] provides theoretical guarantees for their reconstruction in a compressed sensing framework Leveraging these general results, we explore RSS-fingerprinting methods based on block-sparsity and we prove their efficiency.
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