LOS/NLOS Classification Using Scenario-Dependent Unsupervised Machine Learning

Location information is essential for a wide range of applications requiring, for example, highly accurate positioning information such as industrial automation, autonomous driving, inbound logistics and augmented reality. One of the major error sources in positioning is non-line-of-sight (NLOS) propagation while a line-of-sight (LOS) propagation is anticipated. Therefore, classifying positioning measurements as LOS or NLOS plays a key role to enable high accuracy positioning use cases.Existing solutions for the classification task either rely on availability of label or reference measurement data or do not consider effects of different scenarios or channel models. In this paper, we propose an unsupervised method for the classification task through a channel feature selection process to select only useful channel features to be used for the classification. Then, we show based on real-world data from several measurement campaigns that the proposed method outperforms the existing solutions both in classification performance and ranging accuracy.