MoLoc: Unsupervised Fingerprint Roaming for Device-Free Indoor Localization in a Mobile Ship Environment

• Published in: IEEE internet of things journal 2020-12, Vol.7 (12), p.11851-11862
• Main Authors: Chen, Mozi, Liu, Kezhong, Ma, Jie, Zeng, Xuming, Dong, Zheng, Tong, Guangmo, Liu, Cong
• Format: Article
• Language: English
• Subjects: Adaptation models; Cruise ships; Data models; Dynamics; Fingerprint recognition; Fingerprints; Indoor environments; Localization; Marine vehicles; Mobile ship environment; Passenger safety; passive human localization; Representations; Sailing; Sensors; Ship motion; Unsupervised learning
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.3004240

Device-free indoor localization may play a critical role in improving passengers' safety in large vessels, particularly for scenarios without equipped radios. However, due to dynamic internal and external influences from the sailing ship such as changing sailing speed, the existing localization systems suffer huge accuracy degradation in a mobile ship environment. The challenges are mainly due to rich and arbitrary ship motions and the resulting complicated impacts on the indoor wireless channels. To address the challenges, in this article, we first propose a ship motion descriptor to extract discriminative latent representation from complex ship motions by leveraging deep-learning techniques. Based on this representation, we then design a novel fingerprint roaming model, i.e., MoLoc, to automatically learn the predictive fingerprint variation pattern and transfer the online fingerprint measurement to adapt to dynamic ship motions in real time. Furthermore, an unsupervised learning strategy is proposed to train the fingerprint roaming model using unlabeled onboard collected data which do not incur any labor costs. We have implemented and extensively evaluated MoLoc on real-world cruise ships, where experimental results demonstrate that MoLoc improves localization accuracy from 63.2% to 92.8% compared to the state-of-the-art localization methods, including Pilot, LiFS, SpotFi, and AutoFi while achieving a mean error of 0.68 m.