NLOS Detection using UWB Channel Impulse Responses and Convolutional Neural Networks

Indoor environments often pose challenges to RFbased positioning systems. Typically, objects within the environment influence the signal propagation due to absorption, reflection, and scattering effects. This results in errors in the estimation of the time or arrival (TOA) and hence leads to errors in the position estimation. Recently, different approaches based on classical, feature-based machine learning (ML) have successfully detected such obstructions based on CIRs of ultra wideband (UWB) positioning systems. This paper applies different convolutional neural network architectures (ResNet, Encoder, FCN) to detect non line-of-sight (NLOS) channel conditions directly from the CIR raw data. A realistic measurement campaign is used to train and evaluate the algorithms. The proposed methods highly outperform the featurebased ML baselines while still using low network complexities. We also show that the models generalize well to unknown receivers and environments and that positioning filters benefit significantly from the identification of NLOS measurements.