论文标题
半监督的学习与甘斯一起用于无设备的指纹室内定位
Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization
论文作者
论文摘要
无设备无线室内本地化是物联网（IoT）的关键启用技术。基于指纹的室内定位技术是一种常用的解决方案。本文提出了一个半监督，生成的对抗网络（GAN）的无设备指纹室内定位系统。提出的系统使用少量的标记数据和大量未标记的数据（即半监视），从而大大减少了昂贵的数据标记工作。实验结果表明，与最先进的监督方案相比，所提出的半监督系统以相等，足够的标记数据和相等，相等，高度有限的标记数据具有相同数量的标记数据可比性的性能。此外，提议的半监督系统将其性能保留在广泛的标记数据量中。在视觉上检查和讨论了基于GAN的系统的生成器，鉴别器和分类器模型之间的相互作用。还提供了对拟议系统的数学描述。
Device-free wireless indoor localization is a key enabling technology for the Internet of Things (IoT). Fingerprint-based indoor localization techniques are a commonly used solution. This paper proposes a semi-supervised, generative adversarial network (GAN)-based device-free fingerprinting indoor localization system. The proposed system uses a small amount of labeled data and a large amount of unlabeled data (i.e., semi-supervised), thus considerably reducing the expensive data labeling effort. Experimental results show that, as compared to the state-of-the-art supervised scheme, the proposed semi-supervised system achieves comparable performance with equal, sufficient amount of labeled data, and significantly superior performance with equal, highly limited amount of labeled data. Besides, the proposed semi-supervised system retains its performance over a broad range of the amount of labeled data. The interactions between the generator, discriminator, and classifier models of the proposed GAN-based system are visually examined and discussed. A mathematical description of the proposed system is also presented.
