Adaptive Localization Through Transfer Learning in Indoor Wi-Fi Environment

In a Wi-Fi based indoor localization system (WILS), mobile clients use received Wi-Fi signal strength to determine their locations. A major problem is the variation of signal distributions caused by multiple factors, which makes the old localization model inaccurate. Therefore, the transfer learning problem in a WILS aims to transfer the knowledge from an old model to a new one. In this paper we study the characteristics of signal variation and conclude the chief factors as time and devices. An algorithm LuMA is proposed to handle the transfer learning problem caused by these two factors. LuMA is a dimensionality reduction method, which learns a mapping between a source data set and a target data set in a low-dimensional space. Then the knowledge can be transferred from source data to target data using the mapping relationship. We implement a WILS in our wireless environment and apply LuMA on it. The online performance evaluation shows that our algorithm not only achieves better accuracy than the baselines, but also has ability for adaptive localization, regardless of time or device factors. As a result, the calibration efforts on new training data can be greatly reduced.