Semi-Supervised Deep Adversarial Forest for Cross-Environment Localization

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Semi-Supervised Deep Adversarial Forest for Cross-Environment Localization
Title:
Semi-Supervised Deep Adversarial Forest for Cross-Environment Localization
Other Titles:
IEEE Transactions on Vehicular Technology
Publication Date:
13 June 2022
Citation:
Cui, W., Zhang, L., Li, B., Chen, Z., Wu, M., Li, X., & Kang, J. (2022). Semi-Supervised Deep Adversarial Forest for Cross-Environment Localization. IEEE Transactions on Vehicular Technology, 71(9), 10215–10219. https://doi.org/10.1109/tvt.2022.3182039
Abstract:
Extracting channel state information (CSI) from WiFi signals is of proved high-effectiveness in locating human locations in a device-free manner. However, existing localization/positioning systems are mainly trained and deployed in a fixed environment, and thus they are likely to suffer from substantial performance declines when immigrating to new environments. In this paper, we address the fundamental problem of WiFi-based cross-environment indoor localization using a semi-supervised approach, in which we only have access to the annotations of the source environment while the data in the target environments are un-annotated. This problem is of high practical values in enabling a well-trained system to be scalable to new environments without tedious human annotations. To this end, a deep neural forest is introduced which unifies the ensemble learning with the representation learning functionalities from deep neural networks in an end-to-end trainable fashion. On top of that, an adversarial training strategy is further employed to learn environment-invariant feature representations for facilitating more robust localization. Extensive experiments on real-world datasets demonstrate the superiority of the proposed methods over state-of-the-art baselines. Compared with the best-performing baseline, our model excels with an average 12.7% relative improvement on all six evaluation settings.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Career Development Award
Grant Reference no. : C210112046

National Natural Science Foundation of China (61903231)
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1939-9359
0018-9545
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