An Empirical Evaluation of a Probabilistic RF Signature for WLAN Location Fingerprinting

Localization for indoor environments has gained considerable attention over the last decade. The most popular technique is based on location fingerprinting using received signal strength (RSS) mainly due to the fact that it exploits the available wireless infrastructure and that RSS fingerprints are readily available using different wireless standards (IEEE 802.11, etc.). This simplicity however incurs a cost in accuracy and researchers focus on improving the performance from a pattern recognition perspective. Recently improvement in performance has been demonstrated using physical layer channel-based fingerprints such as the Channel Transfer Function (CTF) and Channel Impulse Response (CIR) at a cost of increased storage and computation requirements. In this paper we experimentally evaluate the performance of a probabilistic physical layer fingerprint that is based on entropy of the magnitude and phase of the CTF. We will show through extensive frequency domain channel measurements in an indoor office environment that entropy can be a practical alternative to RSS fingerprinting; where it shares the latter's simplicity of structure (scalar) but outperforms RSS and complex CIR fingerprints. We further investigate the impact of realistic channel and system impairments such as small-scale fading (Doppler), Signal-to-noise ratio (SNR) and interference on the performance of the proposed fingerprint signature.