Entropy-based location fingerprinting for WLAN systems

Localization for indoor environments has gained considerable attention over the last decade due to the enormous potential in the technology and the significant challenges facing this area of research. One practical localization technique that relies on the available fixed wireless infrastructure is RF location fingerprinting. Received Signal Strength (RSS)-based location fingerprinting has been the dominant fingerprinting approach in the literature due to the simplicity and practicality of measuring the RSS in a variety of wireless technologies (such as IEEE 802.11 and UMTS). Recognizing the diminishing gains using the RSS-based techniques, researchers have recently shifted focus to proposing improvements at the physical layer by adopting the channel impulse response (CIR) as an alternate fingerprint. In this paper we propose a novel fingerprint structure that is based on the entropy estimation of the channel; which provides a more unique/robust fingerprint that is capable of distinguishing between locations more effectively. Through extensive frequency domain channel measurements and analysis in a typical indoor environment we further validate the proposed technique and compare it against RSS and CIR-based fingerprinting. We will show that the technique combines the advantage of RSS-based fingerprinting simplicity of structure (storage and pattern recognition requirements) and improves on the robustness of the CIR-based fingerprinting techniques. Finally we will illustrate that our entropy-based location fingerprinting can be practically integrated into the architecture of popular OFDM-based WLAN systems.