Abstract
In the past decade, great progresses have been made in WiFi-based device-free localization (DFL). However, some challenging issues still hinder the large-scale implementation of DFL techniques, mainly including fingerprint vanishing and environmental dynamics. In order to enhance the localization performance in cluttered environments, in this article, a modified hierarchical framework for DFL is designed, which consists of several functional modules. Specifically, the collected data are first divided into several subsets in the spatiotemporal separation module. Next, the raw data are mapped to another feature space to mitigate the effects of fingerprint vanishing with the help of a deep neural network. In the distributed modeling module, local DFL models are built to separately represent the subsets. Additionally, probability distributions of local DFL models are calculated to estimate and control the effects of the noise. Finally, a global DFL model is built by integrating all the local DFL models with the embedding of the probability distribution information of those local DFL models. In this manner, the localization performance in cluttered environments could be significantly enhanced by the proposed hierarchical framework. Comprehensive experiments in several indoor environments demonstrate the robustness and generalization performance of the proposed hierarchical framework.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.