Channel State Information Fingerprints Research Articles

Indoor passive localisation based on reliable CSI extraction

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.

Device-Free Presence Detection and Localization With SVM and CSI Fingerprinting

Presence detection and localization are of importance to a variety of applications. Most previous approaches require the objects to carry electronic devices, while on many occasions device-free presence detection and localization are in need. This paper proposes a device-free presence detection and localization algorithm based on WiFi channel state information (CSI) and support vector machines (SVM). In the area of interest covered with WiFi, human movements may cause observable alteration of WiFi signals. By analyzing the CSI fingerprint patterns, the proposed algorithm is able to detect human presence through SVM classification. By establishing the nonlinear relationship between CSI fingerprints and locations through SVM regression, the proposed algorithm is able to estimate the object locations according to the measured CSI fingerprints. To cope with the noisy WiFi channels, the proposed algorithm applies density-based spatial clustering of applications with noise to reduce the noise in CSI fingerprints, and applies principal component analysis to extract the most contributing features and reduce the dimensionality of CSI fingerprints. Evaluations in two typical scenarios achieved the presence detection precision of over 97%, and the localization accuracy of 1.22 and 1.39 m, respectively.

IPil: improving passive indoor localisation via link-based CSI features

Passive indoor localisation acts as a key enabler for various emerging applications such as secured region monitoring, smart homes, intelligent nursing, etc. Despite of years of research, their accuracy of localisation still remains unsatisfactory for practical uses. The main hurdle lies in the coarse measurement of wireless channels, e.g., received signal strength indicator (RSSI), employed in most existing schemes. In this work, we explore the potential of using channel state information (CSI) for fine-grained passive indoor localisation on a single communication link. To achieve high accuracy, we propose a solution based CSI fingerprint and devise two novel localisation estimator approaches suited to different conditions: weighted Bayesian (WBayes) and the maximum similarity metric (MSM). Compared with RSSI, CSI has demonstrated itself with a high accuracy of location distinction. Experimental results show that our schemes can achieve a higher accuracy.