Adaptive Localization in Dynamic Indoor Environments by Transfer Kernel Learning

Accurate Location Based Service (LBS) is one of the fundamental but crucial services in the era of Internet of Things (IoT). WiFi fingerprinting-based Indoor Positioning System (IPS) has become the most promising solution for indoor LBS. However, the offline calibrated received signal strength (RSS) radio map is unable to provide consistent LBS with high localization accuracy under various environmental dynamics. To address this issue, we propose TKL-WinSMS as a systematic strategy, which is able to realize robust and adaptive indoor localization in dynamic indoor environments. We developed a WiFi-based Non-intrusive Sensing and Monitoring System (WinSMS) that enables WiFi routers as online reference points by extracting real-time RSS readings among them. With these online data and labeled source data from the offline calibrated radio map, we further combine the RSS readings from target mobile devices as unlabeled target data, to design a robust localization model using an emerging transfer learning algorithm, namely transfer kernel learning (TKL). It is able to learn a domain-invariant kernel by directly matching the source and target distributions in the reproducing kernel Hilbert space instead of the raw noisy signal space. The resultant kernel can be used as input for the SVR training procedure. In this manner, the trained localization model can inherit the information from online phase to adaptively enhance the offline calibrated radio map. Extensive experiments were conducted and demonstrated that the proposed TKL- WinSMS is able to improve the localization accuracy by at least 26% compared with existing solutions under various environmental interferences.