Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Distributed weighted-multidimensional scaling for node localization in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Kernel-Based Positioning in Wireless Local Area Networks
IEEE Transactions on Mobile Computing
Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks
Proceedings of the workshop on Real-world wireless sensor networks
On denoising and best signal representation
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Compressive sensing based positioning using RSS of WLAN access points
INFOCOM'10 Proceedings of the 29th conference on Information communications
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In this paper, a novel multiple target localization approach is proposed by exploiting the compressive sensing theory, which indicates that sparse or compressible signals can be recovered from far fewer samples than that needed by the Nyquist sampling theorem. We formulate the multiple target locations as a sparse matrix in the discrete spatial domain. The proposed algorithm uses the received signal strengths (RSSs) to find the location of targets. Instead of recording all RSSs over the spatial grid to construct a radio map from targets, far fewer numbers of RSS measurements are collected, and a data pre-processing procedure is introduced. Then, the target locations can be recovered from these noisy measurements, only through an l1-minimization program. The proposed approach reduces the number of measurements in a logarithmic sense, while achieves a high level of localization accuracy. Analytical studies and simulations are provided to show the performance of the proposed approach on localization accuracy.