A Novel CNN Model for NLoS Classification in UWB Indoor Positioning System

According to the research conducted, people spend about 70-90% of their living and working time indoors. Therefore, providing systems that offer adequate services to users in these environments seems essential. Locating users and devices is widely used in healthcare, industry, building management, surveillance, and other areas. There are various technologies for indoor positioning systems. In this paper, Ultra Wide Band (UWB) technology is considered due to its high accuracy in indoor positioning. However, there are many objects and people in indoor environments, so obstacles can reflect the transmitted signals. Compared to the Line of Sight (LoS) signal, the delay of the signal transmission path in the Non-Line of Sight (NLoS) signal leads to positive range errors.In order to reduce the effect of NLoS conditions on positioning. In this research, we have attempted to achieve high-precision accuracy separation for LoS and NLoS conditions by providing deep learning networks and using channel impulse response data as input without prior knowledge of the environment. In addition, the result of this classification is compared to other references that used a similar dataset. The results of the NLoS/LoS signal classification section show that the proposed Convolutional Neural Networks (CNN) are better than other neural network methods (such as Deep neural networks).