CSI fingerprinting with SVM regression to achieve device-free passive localization

Abstract

Location is an important context on which a broad range of context-aware applications can be built. Most previous approaches require the targets to carry electronic devices, while device-free passive localization is in need on many occasions. This paper proposes a device-free passive localization algorithm based on WiFi Channel State Information(CSI) and Support Vector Machines(SVM). In a physical space covered with WiFi signals, movements of targets may cause observable alteration of CSI. By establishing the nonlinear relationship between CSI fingerprints and target locations through SVM regression, the proposed algorithm is able to estimate the target locations according to the corresponding CSI fingerprints. The algorithm applies Density-Based Spatial Clustering of Applications with Noise(DBSCAN) to reduce the noise in CSI fingerprints, and applies Principal Component Analysis(PCA) to extract the most useful features and reduce the dimension of CSI fingerprints. Evaluations achieved the mean localization error distance of 1.22m, outperforming the Received Signal Strength Indication(RSSI) approach by 43.0%, and outperforming SVM classification and Naive Bayesian by 39.9% and 41.9%.