A deep learning-based approach for pseudo-satellite positioning

Traditional pseudo-satellite-based indoor positioning techniques are greatly affected by the presence of multipath effects, leading to a notable reduction in the positioning precision. In order to tackle this challenge, a pseudo-satellite indoor positioning method based on deep learning is proposed. The method grids the localization region, thus transforming positioning from a regression problem to a classification problem in the gridded areas. 1D-convolutional neural network is employed to extract the correlation between pseudo-satellite data and the positioning of indoor areas. Data are collected and the method is validated in three types of areas of the experimental field, namely unobstructed area, semi-unobstructed area and obstructed area. The experimental results demonstrate that the method exhibits superior positioning accuracy compared to traditional methods, enabling effective localization even in obstructed area.