# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 3 Related Works
### Identification of the NLOS and MP Conditions in the Literature Based on Machine Learning Techniques  
One of the earlier ML-based NLOS identifications in UWB was conducted in [9] using SVM as a classifier. In that paper, the identification process was considered as a binary classification problem (LOS vs. NLOS) showing that the ML approaches outperformed the traditional parametric techniques and signal processing approaches from the literature. Consequently, several investigations of the NLOS identification process in UWB were examined in the literature using different ML techniques as a classifier such as SVM in [9; 10; 17; 21], MLP in [23; 37; 38], boosted decision tree (BDT) in [38], recursive decision tree in [35], and other ML techniques such as kernel principal component analysis in [19], etc. Moreover, the unsupervised machine learning technique called \"expectation maximization for Gaussian mixture models\" was recently applied in [39] to classify the LOS and NLOS conditions in UWB measurement. Likewise, deep learning approaches such as the convolutional neural network (CNN) were also explored to distinguish the NLOS condition from LOS in UWB ranging [13; 22]. In CNN-based deep learning approaches, the authors generally modified the existing CNN network such as GoogLeNet [22], VGG-architecture (i.e., VGG-16 or VGG-19) [13; 22], AlexNet [22], etc., to be usable for the low cost UWB systems. The reported overall accuracy ranges started from 60% (using a typical ML technique such as SVM) up to 99% (using the CNN approach). In all of the above-mentioned approaches, the focus is solely on detecting the NLOS condition in UWRranging, i.e., the binary classification between LOS and NLOS. Moreover, the performance comparison of different ML techniques for the identification of NLOS in UWB ranging was conducted in [11; 17; 38]. The main purpose of these analyses was to compare the impact of model selection in ML-based system applications in UWB. In [38], the performance comparison of two ML methods namely MLP and BDT was carried out for the binary classification (LOS vs. NLOS). The resultant report concluded that the BDT outperformed the MLP. Likewise, the comparison of five classifiers using MATLAB (i.e., SVM, k-nearest neighbor (KNN), binary decision tree, Gaussian process (GP), generalized linear model) was performed in [11]. The authors concluded that KNN and GP performed better than the other three models. Similarly, the authors in [17] evaluated three ML models (SVM, MLP, RF) to classify the LOS and NLOS in narrowband wireless communications (i.e., not specifically for UWB systems in this case). The authors reported that RF and MLP performed better than SVM in all of their evaluations. In contrast to the binary classification between LOS and NLOS in UWB, the binary classification of the MP from LOS conditions was investigated in [13]. The author reported that MP effects could cause an error in UWB ranging from a few centimeters up to 60 cm. Similar deviation of the error in the MP condition can be seen in our experimental evaluation presented in Figure 2d. Throughout the literature, the problem has been treated as a binary classification problem or hypothesis test (i.e., LOS vs. NLOS or LOS vs. MP). To the best of the authors' knowledge, only two papers addressed UWB-based ranging errors as a multi-class problem [11; 21]. The first paper was based on a two-step identification process [21] using SVM as a classifier. In that paper, the LOS and NLOS were identified in the first step. Then, further classification (MP vs. NLOS) was categorized in the second step if NLOS was detected in the first step. The second paper categorized the NLOS conditions into two types (soft-NLOS vs. hard-NLOS) in addition to LOS while ignoring the MP effects [11]. The differentiation between the two NLOS types was primarily based on the material of the obstacles, which the UWB signal was passing through by penetration. The authors used two types of walls in their evaluation to classify a soft-NLOS and a hard-NLOS.