# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 2 Related Work  
Traditional methods for NLoS identification rely on signal propagation characteristics and prior information [17], including distance-based, prior map-based, and channel feature-based approaches. Distance-based methods typically use statistical analysis of the estimated or measured distances to identify NLoS conditions, employing metrics including time of arrival (TOA), time difference in arrival (TDOA), angle of arrival (AOA), and received signal strength indicator (RSSI) [18; 19], or their combinations [19]. [PERSON] et al. [20] introduced a low-cost UWB distance compensation model that uses kurtosis to detect NLoS environments, addressing electromagnetic wave loss issues in real indoor settings. [PERSON] et al. [21] developed a SVM-based method using ratio data from ranging statistics for NLoS identification. Despite their efficiency, these methods are prone to misjudgments due to distance information fluctuations, increasing NLoS measurement errors. In static, well-mapped indoor environments, prior map-based methods are more suitable. These methods use the environmental layout and tag positions to identify NLoS conditions [22]. [PERSON] et al. [23] created LoS/NLoS maps and proposed an NLoS correction localization method based on these maps, using a map-corrected extended Kalman filter (MKF) algorithm for optimizing trajectories in NLoS environments with few anchors. The downside is the extensive data collection required for prior maps and the inability to identify the dynamic obstacles causing NLoS occlusion. Channel feature-based methods leverage advancements in signal systems to extract features from UWB data. Compared to distance-based methods, they better utilize signal propagation information and are often combined with ML techniques like SVM [24] and Gaussian distribution (GD) models [25]. These methods have shown success in the binary LoS/NLoS classification. For example, [PERSON] et al. [26] used static parameters from waveforms and trained an SVM for prediction, while [PERSON] et al. [27] proposed a generalized Gaussian distribution (GGD) technique for NLoS recognition. [PERSON] et al. [28] introduced a Two-Step NLoS detection method using decision trees and feedforward neural networks for fine-tuning. However, these methods often fail to generalize across various NLoS types and obtaining accurately labeled datasets is challenging, resulting in limited precision due to insufficient deep feature extraction by ML models. In contrast, DL methods excel in feature extraction from NLoS scenarios. [PERSON] et al. [29] proposed a multilayer perceptron (MLP) model combining raw CIR features with handcrafted features for LoS/NLoS recognition. [PERSON] et al. [30] improved CNN-based NLoS recognition \begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Definition** \\ \hline \(sm_{c}\) & The SE attention excitation output of the \(c\)-th channel \\ \(\widetilde{h}_{c}\) & The per-channel multiplication output \\ \(\mathbf{\tilde{H}_{\text{SE}}}\) & The final output of the SE attention block \\ \(\mathbf{H_{\text{lstm}}}\) & Deep spatiotemporal features extracted by LSTM \\ \(\mathbf{F_{\text{concat}}}\) & Fused feature vector \\ \(x_{is}^{\text{softmax}}/x_{js}^{\text{softmax}}\) & The \(is\)-th/\(js\)-th input elements for Softmax function \\ \(\sigma\) & The sigmoid function \\ \(\alpha\) & The balancing factor \\ \(\gamma\) & The focusing parameter \\ \(\hat{y}_{ly}\) & The predicted probability for class _iy_ \\ \(x_{t}\) & The LSTM input \\ \(ic\) & The five types of CIR signal propagation channel \\ \(\varepsilon_{r}\) & Energy of CIR \\ \(r_{max}\) & Maximum amplitude of CIR \\ \(\tau_{md}\) & Mean excess delay of CIR \\ \(\tau_{rms}\) & The Root-mean-square delay spread of CIR \\ \(k\) & CIR kurtosis \\ \(d_{t}\) & UWB ToF estimated distance \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._by researching CIR denoising methods, achieving a higher accuracy. [PERSON] et al. [31] used LSTM networks to identify and classify human obstructions under 6.5 GHz NLoS conditions.