# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 8 Discussions  
The identification process of the LOS, NLOS, and MP conditions in a wireless ranging system, especially in UWB, is crucial because they strongly influence the quality and accuracy of the actual measurement. For that reason, several contributions have been proposed in the literature to identify these conditions as already mentioned in Section 1. However, most of the contributions treated the issue as a binary classification problem (Section 2). To the best of our knowledge, only two papers [11; 21] addressed the UWB-based classification process as a multi-class problem. In this paper, we defined three classes in UWB measurement data (LOS, NLOS, and MP conditions as presented in Section 2) and evaluated three ML classifiers (SVM, RF, and MLP as presented in Section 5) to identify these three defined classes. Two metrics, F1-score and confusion matrix, were used for evaluating the performance of each classifier. Apart from these scores, the training and test times for each classifier were also given in our evaluation. As a matter of fact, this type of metric (training and test times) is typically ignored in the literature. However, it is undeniable that the magnitude of a test time in a certain classifier is usually vital in the overall performance of the system. Our results presented in Section 5 revealed that SVM had the poorest performance among the three classifiers in terms of the test time. In contrast, SVM gave the best performance in terms of the F1-score when different environments of training and test states were applied (Section 7). The two mentioned results showed us the interesting contradiction of two metrics in the SVM classifier, which could be an important factor in the system implementation of some ML applications. The evaluation results based on the F1-score and overall accuracy pointed out that the measured environments in UWB had a strong effect on the performance of the three classifiers (Section 7). This referred to the striking degradation of the performance in all classifiers when different environments of training and test were applied in the evaluation. One may argue that this was caused by the overfitting of the model in the training state. However, this outcome occurred in all three evaluated classifiers regardless of the hyperparameters used in each classifier. Indeed, this was a part of the generalization problem caused by the inadequate representation of the conditions in the data. However, the data collection process in UWB, especially for the three mentioned classes, was quite time-consuming, elaborate, and costly because of the nature of the wireless signal. This meant the evaluated outcomes would be affected differently in several different ways by the conditions of the materials, types of walls, types of furniture, etc., in the measured environment. Our attempt was to provide the feasibility of the ML approach in the multi-class classification of the UWB measurement data in contrast to the conventional technique given in Section 2. The results based on the F1-score in our experiments showed 0.69 in the worst-case scenario and 0.92 in the best-case scenario. The outcomes were quite satisfying and promising. In general, the classification results of UWB in the literature were usually reported using single value metrics, i.e., the overall accuracy score [21; 23] and F1-score [11]. In some cases, the recall and true negative rate were applied in addition to the accuracy score, as in [38]. Typically, in a binary classification problem, the receiver operating characteristic (ROC) curve [50] and cumulative distribution function [9; 18] are widely used. Those scores and metrics are particularly helpful for the comparative analysis of different classifiers. However, the insightful details of the actual conditions are usually overlooked or missed in some cases. Therefore, we used the complete confusion matrix in this paper to examine the individual outcomes of each class in all evaluated classifiers (Section 7.2).