Abstract-This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in wireless networks. Particularly, we re-formulate the localization problem as a sparse approximation problem using the compressive sensing theory that provides a new paradigm for recovering a sparse signal solving an 1 minimization problem. The proposed received signal strength-based method does not require any time specific/propriatery hardware since the location estimation is performed at the Access Points (APs). The experimental results show that our proposed method, when compared with traditional localization schemes results in a better accuracy in terms of the mean localization error.

WiFi indoor localization has seen a renaissance with the introduction of RSSI-based approaches. However, manual fingerprinting techniques that split the indoor environment into predefined grids are implicitly bounding the maximum achievable localization accuracy. WoLF, our proposed Wireless localization and Laser-scanner assisted Fingerprinting system, solves this problem by automating the way indoor fingerprint maps are generated. We furthermore show that WiFi localization on the generated high resolution maps can be performed by sparse reconstruction which exploits the peculiarities imposed by the physical characteristics of indoor environments. Particularly, we propose a Bayesian Compressed Sensing (BCS) approach in order to find the position of the mobile user and dynamically determine the sufficient number of APs required for accurate positioning. BCS employs a Bayesian formalism in order to reconstruct a sparse signal using an undetermined system of equations. Experimental results with data collected in a university building validate WoLF in terms of localization accuracy under actual environmental conditions.