Lab to Multiscene Generalization for Non-Line-of-Sight Identification With Small-Scale Datasets

Abstract

Ultra-wideband (UWB) wireless indoor positioning systems rely on time of flight (TOF) to estimate distances but can be biased and miscalculated due to non-line-of-sight (NLOS) transmission channels in complex environments. Therefore, to remove errors, several machine learning techniques have been proposed for identifying NLOS signals from Channel Impulse Responses (CIRs). However, as CIR signals could be heavily influenced by various environments, current NLOS classifiers are not universal to provide satisfactory accuracy for new scenarios and require detailed measurements on a large number of CIRs for training. Hence, we propose a generalization method based on data augmentation via noise injection and transfer learning to allow the deep neural network (DNN) trained under a lab condition to be applied to various and even harsh practical scenarios with the need to measure massive training data minimized. This paper presents the first demonstration that it is effective to utilize a lab-based pre-trained DNN for real-world transfer and white Gaussian noise data augmentation for ML-based NLOS identification on UWB CIRs to address the problem when it is not feasible to measure sufficient training data. Our testing results show that in two scenarios, corridor and parking lot, with only 50 CIR signals as the training set, the accuracy of the NLOS identification model after applying the proposed method is increased from 84.4% to 98.8% and from 81.1% to 97.1%, respectively.Impact Statement-In this paper, we propose a robust and data-efficient DNN-based method for identifying non-line-of-sight (NLOS) signals within ultra-wideband (UWB) indoor positioning signals to overcome distance estimation errors. For applications in a new environment or generalization across multiple environments, the need for sufficient data to train the DNN model can be largely lowered and higher accuracy can be offered. Furthermore, with our approach, the realization of accurate NLOS identification becomes possible in some harsh scenarios where collecting a large amount of data is costly, timeconsuming, or even impossible. In addition, we have investigated the possibility of applying noise injection to augment channel impulse response signals (CIRs) and to deal with environmental This work has been partly sponsored by the IoT Superproject, a strategic initiative of the