Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-users. In this article, we propose a new architecture by combining Convolutional Neural Network (CNN), Long short-term memory (LSTM) and Generative Adversarial Network (GAN) in order to increase the training data and thus improve the position accuracy. The proposed combination of supervised and unsupervised models was tested in 17 public datasets, providing an extensive analysis of its performance. As a result, the positioning error has been reduced in more than 70% of them.

翻译：以机器学习为基础的室内定位已引起学院和业界越来越多的注意,因为可以从参考数据中提取有意义的信息,许多研究人员正在使用受监督、半监督和不受监督的机器学习模型来减少定位错误,并向最终用户提供可靠的解决方案。在本篇文章中,我们提议一个新的架构,将动态神经网络(CNN)、长期短期内存(LSTM)和General Aversarial网络(GAN)结合起来,以便增加培训数据,从而提高定位准确性。在17个公共数据集中测试了所拟议的受监督和非监督模型组合,提供了对其性能的广泛分析。因此,定位错误减少了70%以上。