## 5 Detection in UWB Positioning Algorithms
### Recent Advances in ML for NLoS Effects  
In this section, we will present an overview of several research papers in the current literature related to UWB IPSs. The existing research work based on ML for UWB IPSs can be classified into two main categories: NLoS detection and NLoS error correction approaches. The primary goal of NLoS detection is to accurately classify the NLoS signal and then mitigate its effects. On the other hand, the primary objective of error correction is to identify the errors in the UWB ranges using precise ground truth, which can have a positive impact on the localization accuracy. Specifically, when there are at least four anchors available for 3\(D\) localization, an anchor selection algorithm can be used to mitigate range errors based on their NLoS detection before providing the ranges to the localization algorithm.  
* **NLoS Classification:** In the UWB feature-based methods category, two papers are mentioned. [PERSON] et al. [79] use three ML approaches to classify NLoS into multiple classes (LoS and NLoS) based on 12 extracted features, achieving an accuracy of up to 91.9% in the best case. Similarly, [PERSON] et al. [80] use a genetic algorithm to find the best combination of 18 features in an office environment, achieving an NLoS classification accuracy of 96%. In contrast, the non-feature-based methods category includes three papers. [PERSON] et al. [81] use a CNN to identify NLoS signals after denoising raw CIR data using a reversible transformation method, achieving an average accuracy increase of 27.9% for NLoS classification accuracy. [PERSON] et al. [84] propose an unsupervised ML approach based on Gaussian mixture models to identify NLoS links from unlabeled data. [PERSON] et al. [85] use a CNN to extract non-temporal features from UWB raw CIR data, and then feed the features into long short-term memory for NLoS classification, achieving an accuracy of 82.14%. Compared to the feature-based methods, the papers based on raw CIR measurements provide superior performance for NLoS detection. However, the authors did not evaluate the performance of the proposed approaches in unseen environments, which limits their suitability in practical settings. In contrast, [PERSON] et al. [86] propose transfer learning based on neural networks (NN) and convolutional neural networks (CNN) to identify UWB NLoS signals in unseen environments.
* **NLoS Error Correction:** Besides NLoS detection, UWB error correction is mentioned in [87; 88; 89; 90]. Similar to the NLoS approaches, some research papers focused on extracting features from the CIR data. The authors of [87] extracted the features based on  
Figure 13: Blocks of machine learning NLoS detection for UWB precise positioning process.  
distance measurement and received signal strength. Then the authors proposed local spatial feature extraction, temporal feature extraction, and position prediction to improve the positioning accuracy. Authors in [89] mainly focus on the UWB measured range associated with NLoS. A large dataset comprising of the measured distance and 7 different signal features are trained by an (ANN) to perform error prediction. The focus of [88] is on UWB feature-based error correction. Two classes of non-parametric regression techniques include a support vector machine and the Gaussian process and are applied by the authors to directly mitigate the ranging error in the physical layer, based on 6 signal features from the received waveform and the estimated distance. The fraction of residual errors less than \(1m\) is increased from 63% to around 90% by using support vector machine- and Gaussian process-based mitigation. Finally, in paper [90], a semi-supervised autoencoder-based ML approach is proposed by the authors, based on raw CIR data, to achieve high IPS accuracy for low-cost edge devices. The results achieve 29% higher localization accuracy than state-of-the-art deep neural networks in complex environments.