Indoor passive localisation based on reliable CSI extraction

Abstract

In indoor environment, passive human detection and localisation are important enabling technologies for elder healthcare, emergence rescue and target tracking applications. Recently, the fine-grained channel state information (CSI) of Wi-Fi was adopted for indoor localisation due to the low-cost Wi-Fi network interface card and available firmware modifications for CSI extraction. However, due to multipath fading and spatial-temporal dynamics of wireless channel, stable CSI extraction is a challenging task to achieve reliable CSI fingerprint matching. In this study, the sensitivity of CSI is first analysed and stable CSI fingerprints can be obtained by reducing the variance from interference and white noise. The stable CSI fingerprints are then classified by quadratic discriminant analysis to achieve location matching. Extensive experiments have been conducted to justify the system performance. The results reveal that the proposed indoor passive localisation system outperforms passive CSI-MIMO system in terms of performance.