基于Wasserstein生成对抗网络的室内定位改进

Shivam Kumar, Saikat Majumder, S. Chakravarty
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引用次数: 0

摘要

基于指纹的室内定位方法依赖于接收信号强度(RSS)测量数据库和相应的位置标签。然而,收集和维护这样的数据库既昂贵又耗时。在这项工作中,我们提出了Wasserstein生成对抗网络(WGAN)来生成基于指纹的室内定位的合成数据。该系统由一个WGAN组成,该WGAN在真实RSS测量数据集和相应的位置标签上进行训练。WGAN的生成器学习生成合成RSS测量值,批评家学习区分生成的测量值和实际测量值。我们在实际RSS测量数据集上验证了所提出的系统。结果表明,与使用真实数据相比,该系统具有更好的定位精度,同时具有更高的成本效益和可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Indoor Localization Advancement Using Wasserstein Generative Adversarial Networks
Fingerprint-based indoor localization methods rely on a database of Received Signal Strength (RSS) measurements and corresponding location labels. However, collecting and maintaining such a database can be costly and time consuming. In this work, we proposed Wasserstein Generative Adversarial Networks (WGAN) to generate synthetic data for fingerprinting-based indoor localization. The proposed system consists of a WGAN that is trained on a dataset of real RSS measurements and corresponding location labels. The generator of the WGAN learns to generate synthetic RSS measurements, and the critic learns to differentiate the generated and the real measurements. We validate the proposed system on a dataset of real RSS measurements. The result of the proposed system shows better localization accuracy as compared to using real data, while being more cost-effective and scalable.
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