Adaptive Density Graph-Based Manifold Alignment for Fingerprinting Indoor Localization

The received signal strength (RSS) fingerprint-based indoor localization has been considered as a promising solution, due to its relatively high localization accuracy and its ease of use in widespread Wireless Local Area Network (WLAN) infrastructure. A major bottleneck is that the offline fingerprint calibration is time consuming and labor intensive. In this study, inspired by our analysis that multi-density is inherent to the RSS distribution, we present a new radio map construction scheme, called Adaptive Density Graph-based Manifold Alignment (ADG-MA), which can reduce the number of Reference Points (RPs) in offline phase. In particular, it utilizes the density features to capture the exact neighborhood relations of RSS. Furthermore, the approach labels the RSS from user traces to construct the radio map. The extensive experiments demonstrate that the proposed method can construct an accurate radio map at a low deployment cost, as well as achieve a high localization accuracy.