WLAN-based Indoor Path-Tracking using Compressive RSS Measurements

Abstract : In this paper, a hybrid path-tracking system is introduced, which exploits the power of compressive sensing (CS) to recover accurately sparse signals, in conjunction with the efficiency of a Kalman filter to update the states of a dynamical system. The proposed method first employs a hierarchical region-based approach to constrain the area of interest, by modeling the signal-strength values received from a set of wireless access points using the statistics of multivariate Gaussian models. Then, based on the inherent spatial sparsity of indoor localization, CS is applied as a refinement of the estimated position by recovering an appropriate sparse position-indicator vector. The experimental evaluation with real data reveals that the proposed approach achieves increased localization accuracy when compared with previous methods, while maintaining a low computational complexity, thus, satisfying the constraints of mobile devices with limited resources.
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Contributor : Dimitrios Milioris <>
Submitted on : Saturday, November 16, 2013 - 12:33:34 PM
Last modification on : Tuesday, May 14, 2019 - 10:15:07 AM


  • HAL Id : hal-00905114, version 1


Dimitrios Milioris, George Tzagkarakis, Panagiotis Tsakalides, Philippe Jacquet. WLAN-based Indoor Path-Tracking using Compressive RSS Measurements. EURASIP European Signal Processing Conference, Sep 2013, Marrakech, Morocco. ⟨hal-00905114⟩



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