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Enhancing indoor location fingerprinting using channel state information

Abstract : With expeditious development of wireless communications, Location Fingerprinting (LF) has nurtured considerable indoor location based services in the field of Internet of Things. In this thesis, we first proposed EntLoc system, which adopts Autoregressive (AR) modeling entropy of the Channel State Information (CSI) amplitude as location fingerprint. It shares the structural simplicity of the Received Signal Strength (RSS) while reserving the most location-specific statistical channel information. Moreover, an upgraded AngLoc system is further designed, whose additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone Reference Point (RP) candidates. In the LF online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target’s location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches.
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Submitted on : Tuesday, May 19, 2020 - 6:51:09 AM
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Luan Chen. Enhancing indoor location fingerprinting using channel state information. Signal and Image processing. Conservatoire national des arts et metiers - CNAM, 2020. English. ⟨NNT : 2020CNAM1281⟩. ⟨tel-02612246⟩

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