可扩展性
计算机科学
人工智能
深层神经网络
基线(sea)
信道状态信息
深度学习
对抗制
机器学习
人工神经网络
特征(语言学)
活动识别
实时计算
无线
数据库
电信
语言学
海洋学
哲学
地质学
作者
Wei Cui,Lei Zhang,Bing Li,Zhenghua Chen,Min Wu,Xiaoli Li,Rong Yu
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2022-09-01
卷期号:71 (9): 10215-10219
被引量:1
标识
DOI:10.1109/tvt.2022.3182039
摘要
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.
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