Learning-based Ultra-Wideband Indoor Ranging and NLOS Identification




Li, Xin

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The need for precise indoor positioning has become increasingly important with the rise of Internet of Things (IoT) technology, robotics, and autonomous vehicles. Indoor positioning has a wide range of applications, including asset tracking, indoor navigation, and location-based services. To achieve high positioning precision for these applications, accurate and reliable indoor ranging is a key factor when using techniques like time of arrival (ToA), as it enables the calculation of distances between different objects in the indoor environment. In this thesis, we focus on machine learning-based approaches for indoor ranging and non-line-of-sight (NLOS) identification. The first part of the thesis concentrates on reducing ranging errors through machine learning with the improvement of the resolution of channel impulse response (CIR) data. We collect a dataset of 412, 172 traces of CIR data across 12 indoor Line-of-Sight (LOS) scenarios. This dataset is used to train and test three machine learning models, including long short-term memory (LSTM), gated recurrent units (GRU), and multi-layer perception (MLP), to predict the range between the anchor and tag directly through the CIR data. The results demonstrate that LSTM and GRU models outperform traditional meth-ods and the device built-in algorithm in terms of mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), thereby showing the effectiveness of machine learning techniques for indoor ranging applications. On the other hand, indoor ranging accuracy can be significantly affected by NLOS conditions, where the direct path between the transmitter and receiver is obstructed, and the signal has to travel through multiple reflections and diffractions before reaching the receiver. In this thesis, we propose a quantitative approach to differentiate between Soft and Hard NLOS based on the ranging error percentage. We develop machine learning models to identify and classify NLOS conditions. Our study shows that when NLOS is classified into Soft NLOS and Hard NLOS, the accuracy of LOS identification is achieved better than using binary classification. Compared to traditional methods such as leading edge detection or search back window for ranging and positioning, our method exhibits superior performance in noise, multipath, and NLOS environments.



Machine Learning, UWB, CIR, LSTM, GRU, CNN