On the Application of Entropy-Based Metrics for UltraWideBand Line of Sight (LOS)/Not LOS (NLOS) Classification with Ensemble Instance Selection

The knowledge of the Line of Sight (LOS) or Not Line of Sight (NLOS) propagation condition is useful information in wireless communication system. Such knowledge can be inferred by the analysis of the signal, by using specific signal structures (e.g., preambles) or by the application of machine learning (ML) algorithms. In recent times, deep learning (DL) has been applied with success to the classification of LOS/NLOS conditions but with a significant computational time, which can be a practical issue in computing constrained devices. On the other hand, ML relies on the identification of key discriminating features, which can enhance the classification performance. This paper explores the application of entropy metrics to this classification problem. Beyond Shannon entropy, researchers have developed various entropy metrics in recent years in various domains (e.g., healthcare), but they have been scarcely applied to UWB LOS/NLOS classification to the best of the author’s knowledge. This paper addresses this gap by applying entropy metrics in combination with ML classifiers to the public eWINE dataset, characterised by seven different propagation environments where UWB signals were transmitted and recorded in LOS and NLOS conditions. The results presented in this paper show that entropy metrics can significantly enhance the LOS/NLOS classification accuracy and can produce an overall competitive performance. In addition, this paper presents a novel instance selection approach based on the use of entropy metrics, which is demonstrated to significantly outperform even the direct application of some DL algorithms on the basis of the results presented in the literature on the same eWine data set. To summarise the novelty aspects of this study, for the first time in the literature, this study presents an extensive analysis of the discriminative advantage (discrimination index) of entropy measures introduced in the research literature in other domains (e.g., mechanical problems, analysis of physiological signals) in UWB multipath environments for UWB LOS/NLOS classification. In addition, this study presents for the first time the application of an ensemble instance selection algorithm based on entropy measures to the problem of UWB LOS/NLOS classification to handle “noise” or “boundary” samples in the data set, thereby improving model generalisation.