Real time evaluation of RF fingerprints in wireless LAN localization systems

RF location fingerprinting has received significant attention as a practical solution to the indoor localization problem due to its use of the available wireless infrastructure (WLANs) and the simplicity of measuring the Received Signal Strength (RSS) fingerprint. The improvement of RSS-based fingerprinting has been limited due to RSS being a weak fingerprint structure; where it has been reported in literature that using more complex pattern recognition algorithms provides diminishing gains. Recently channel-based RF fingerprints such as the channel impulse response (CIR), channel transfer function (CTF) and frequency coherence function (FCF) have been proposed to improve the accuracy at the physical layer. An empirical evaluation of the physical layer fingerprints does not exist in literature and there is a need to understand the advantages/limitations of these fingerprint structures/signatures. As a result in this paper we provide a comprehensive empirical performance evaluation of location fingerprinting with a focus on analytical comparison of the RSS, CIR, CTF and FCF -based fingerprints using the weighted k-nearest neighbor (kNN) pattern recognition technique. By conducting frequency domain channel measurements in the IEEE 802.11 band at the university campus we evaluate the accuracy of the fingerprints and their robustness to human induced motion perturbations to the channel. We also provide analysis on the impact of system parameters such as the number of access points and the number of nearest neighbors.