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Journal Articles IEEE Access Year : 2019

CSI-Based Probabilistic Indoor Position Determination: An Entropy Solution


Location Fingerprinting (LF) is a promising localization technique that enables enormous commercial and industrial Location-Based Services (LBS). Existing approaches either appeal to the simple Received Signal Strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer Channel State Information (CSI), whose intricate structure leads to an increased computational complexity. In this paper, we adopt Autoregres-sive (AR) modeling based entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while exploiting the most location-specific statistical channel information. On this basis, we design EntLoc, a CSI-based probabilistic indoor localization system using commercial off-the-shelf Wi-Fi devices. EntLoc is deployed in an office building covering over 200 m 2. Extensive indoor scenario experiments corroborate that our proposed system yields superior localization accuracy over previous approaches even with only one signal transmitter.
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hal-02444914 , version 1 (20-01-2020)




Luan Chen, Iness Ahriz, Didier Le Ruyet. CSI-Based Probabilistic Indoor Position Determination: An Entropy Solution. IEEE Access, 2019, 7, pp.170048-170061. ⟨10.1109/ACCESS.2019.2955747⟩. ⟨hal-02444914⟩
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