Probabilistic Fingerprinting Based Passive Device-Free Localization From Channel State Information

Given the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface, researchers have shown great interest in analyzing signal fluctuation on this interface for environmental perception. A popular example is the enabling of indoor localization with RF signals. As an alternative to active device-based positioning, device-free passive (DfP) indoor localization has the advantage that the sensed individuals do not require to carry RF sensors. We propose a probabilistic fingerprinting-based technique for DfP indoor localization. Our system adopts CSI readings derived from off-the-shelf WiFi 802.11n wireless cards which can provide fine-grained subchannel measurements in the context of MIMO-OFDM PHY layer parameters. This complex channel information enables accurate localization of non-equipped individuals. Our scheme further boosts the localization efficiency by using principal component analysis (PCA) to identify the most relevant feature vectors. The experimental results demonstrate that our system can achieve an accuracy of over 92% and an error distance smaller than 0.5m. We also investigate the effect of other parameters on the performance of our system, including packet transmission rate, the number of links as well as the number of principle components.