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Semi-Supervised Deep Adversarial Forest for Cross-Environment Localization
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AI
DOI：10.1109/TVT.2022.3182039.png)
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摘要
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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.
Keyword:
Feature extraction
Location awareness
Fingerprint recognition
Forestry
Deep learning
Wireless fidelity
Training
Adversarial learning
deep learning
device free
indoor positioning
semi-supervised learning
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
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