# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 1 Introduction
This model utilizes CIR data to accurately identify LoS and segmented NLoS conditions, requiring no prior knowledge and making it adaptable to various deployment scenarios. The main contributions are as follows:  
1. A novel network that integrates CNN, LSTM, and SE attention mechanisms is proposed. This architecture efficiently combines the deep feature extraction capability of 1D-CNN, the time series processing strength of LSTM, and the SE attention mechanism's focus on critical features, significantly enhancing NLoS classification accuracy for UWB indoor positioning. Figure 1: Example of NLoS and LoS propagation in a UWB IPS. By leveraging 1D-CNN and other lightweight design considerations, the proposed method achieves a balance between high classification accuracy and low processing time. The integration of LSTM spatiotemporal features with the SE attention mechanism enables the model to effectively distinguish challenging features that other methods often confuse, such as LoS and human obstructions. 3. We validated the efficacy of our proposed method on both publicly available datasets and multiclass datasets collected from experiments. The experimental outcomes indicate that our approach not only significantly improves NLoS binary classification performance but also shows excellent performance in multiclass recognition of common NLoS obstructions in indoor environments. The structure of the remaining sections of this paper is as follows. Section 2 reviews the related work on UWB NLoS identification. Section 3 explores the feasibility of using DL for UWB NLoS identification. Section 4 describes the 1D-CLANet model for NLoS identification, which is based on CNN, LSTM, and Squeeze-and-Excitation (SE) attention mechanisms. Section 5 details the findings from our experiments. Finally, Section 6 summarizes the article and briefly addresses future research directions. All symbols used in the paper are defined as shown in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(d_{i}\) & The distance from the tag to \(i\)-th anchor \\ \(d_{\text{LoS}}/d_{\text{NLoS}}\) & The UWB measurements under LoS/NLoS \\ \(d^{*}\) & The true distance between tag and anchor \\ \(\eta(t)\) & The UWB measurement noise \\ \(s(t)\) & The transmitted symbol \\ \(A\) & The amplitude of the transmitted pulse \\ \(p(t)\) & The single Gaussian pulse waveform \\ \(M\) & The number of pulses \\ \(T_{p}\) & The period of each pulse \\ \(\eta_{c}\) & The fading coefficient of the \(c\)-th path \\ \(\alpha_{c}\) & The time delay of the \(c\)-th path \\ \(n(t)\) & Additive white Gaussian noise with variance \(\sigma_{r}^{2}\) \\ \(\mathsf{Z}\) & Input CIR signal \\ \(L\) & The length of input CIR signal \\ \(\mathbf{U}\) & The output feature map after convolution operation \\ \(H\times W\times C\) & The spatial dimensions of feature map \\ \(\mathbf{V}\) & The set of learned filter kernels \\ \(\gamma_{BN}\beta\) & The learnable parameters of BN operation \\ \(\varepsilon\) & A small constant added to the variance \\ \(\hat{\mathbf{U}}\) & The outputs after the batch normalization operation \\ \(f_{t}\) & The forget gate’s activation vector \\ \(i_{t}\) & The input/update gate’s activation vector \\ \(\hat{\mathbf{C}}_{t}\) & Cell input activation vector \\ \(\hat{\mathbf{C}}_{t}\) & Current cell memory \\ \(\hat{\mathbf{C}}_{t-1}\) & Previous cell memory \\ \(C_{t}\) & The updated cell state \\ \(O_{t}\) & The output gate’s activation vector \\ \(h_{t}\) & Current cell output \\ \(\tanh\) & The tanh activation function \\ \(h_{t-1}\) & The previous cell output \\ \(b\) & The bias vector \\ \(w\) & The weight matrices \\ \(h_{c}(i_{j})\) & The feature value at spatial location \((i,j)\) \\ \(W_{1}\text{and}\ W_{2}\) & Weights of the FC layers \\ \(b_{1}\text{and}\ b_{2}\) & Biases of the FC layers \\ \(\text{ReLU}(\cdot)\) & The ReLU function \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of notations.