Online Spatiotemporal Modeling for Robust and Lightweight Device-Free Localization in Nonstationary Environments

Recent advances in WiFi-based device-free localization (DFL) mainly focus on stationary scenarios, and ignore the environmental dynamics, hindering the large-scale implementation of the DFL technique. In order to enhance the localization performance in nonstationary environments, in this paper, a novel multidomain collaborative extreme learning machine (MC-ELM)-based DFL framework is proposed. Specifically, the whole environment is first divided into several sub-domains depending on the distributions of the collected data using clustering algorithm, and a corresponding number of local DFL models are then built to represent these sub-domains separately. Finally, a global DFL model is achieved through seamlessly integrating all the local DFL models in a global optimization manner. The created MC-ELM-based DFL model also can be incrementally updated with sequentially coming data without retraining to track the environmental dynamics. Extensive experiments in several indoor environments demonstrate the robustness and generalization of the proposed MC-ELM-based DFL framework.