A fine-grained indoor localization using multidimensional Wi-Fi fingerprinting

Although fingerprint based localization is promising for indoor applications, its accuracy still remains a huge challenge. Most of existing approaches rely on the Radio Signal Strength (RSS) to generate fingerprints. However, merely using RSS is unable to accurately localize objects since such an one-dimensional fingerprint will be seriously influenced by the interference and multi-path effect in the indoor environment. In this paper, we propose a new localization approach based on multidimensional Wi-Fi fingerprint. Instead of only using RSS to construct fingerprint, we employ RSS, transmitted power, and channel information to construct an integrated fingerprint. The extended fingerprint enables fine-grained localization and tracking services. We also deign a cosine similarity based matching algorithm and enhanced particle filter mechanism to achieve accurate localization and tracking. Extensive experiment and implementation results show that the new fingerprint and proposed algorithms can achieve an accuracy within two meters in 90% of testing points, while demonstrating a good adaptability to complex indoor environments.