Accurate Location Tracking From CSI-Based Passive Device-Free Probabilistic Fingerprinting - 2018


The research on indoor localization has received great interest in recent times. This has been fueled by the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface. Analyzing the signal fluctuation on the RF-interface can, for instance, solve the still open issue of ubiquitous reliable indoor localization and tracking. Device bound and device free approaches with remarkable accuracy have been reported recently. During this Project, we tend to gift an correct device-free passive (DfP) indoor location tracking system that adopts channel state data (CSI) readings from off-the-shelf WiFi 802.11n wireless cards. The fine-grained subchannel measurements for multiple input multiple output orthogonal frequency-division multiplexing PHY layer parameters are exploited to enhance localization and tracking accuracy. To enable precise positioning in the presence of serious multipath effects in cluttered indoor scenarios, we tend to experimentally validate the unpredictability of CSI measurements and suggest a probabilistic fingerprint-based mostly technique as an accurate solution. Our theme any boosts the localization potency by using principal element analysis to filter the most relevant feature vectors. Furthermore, with Bayesian filtering, we tend to continuously track the trajectory of a moving subject. We tend to have evaluated the performance of our system in four indoor environments and compared it with state-of-the-art indoor localization schemes. Our experimental results demonstrate that this complicated channel information enables additional accurate localization of nonequipped people.

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