# 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
## 5 Experiments and Analysis  
### Experimental Setting Up and Dataset Description  
We tested the binary classification performance of our proposed model for NLoS identification using the publicly available dataset from [42]. This dataset was collected from seven different locations: office 1, office 2, a small apartment, a small workshop, a kitchen with a living room, a bedroom, and a boiler room. Each location provided 3000 LoS samples and 3000 NLoS samples, resulting in a total of 42,000 samples. Due to the lack of detailed CIR multi-class datasets online, we validated the model's performance using our own data. The experimental scenarios shown in Figure 8 include the office area and stair corridor of a university laboratory. Data collection was performed using the DecaWave DWM3000 chip, with modules fixed on tripods at a height of 1.6 m. The specific parameter configurations are shown in Table 5. The distance information between the base station and the tag was obtained using a Leica Disto X3 measurement instrument with an accuracy of \(\pm\)1 mm, as shown in Figure 8b. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameters** & **Set Value** \\ \hline Batch size & 32 \\ Training times & 20 \\ Optimizer & Adam \\ Learning rate & 0.0001 \\ Loss function & Focal loss \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The hyper-parameter settings during the 1D-CLANet model training process. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Network Layer** & **Kernel Size** & **Output Size** \\ \hline \multirow{8}{*}{Feature Extractor} & Conv1 & \(16\times 3\) & \(16\times 512\) \\  & BN1 + ReLU + Pool1 & \(1\times 2\) & \(16\times 256\) \\  & Conv2 & \(32\times 3\) & \(32\times 256\) \\  & BN2 + ReLU + Pool2 & \(1\times 2\) & \(32\times 128\) \\  & Conv3 & \(64\times 3\) & \(64\times 256\) \\  & BN3 + ReLU + Pool3 & \(1\times 2\) & \(64\times 64\) \\  & Flatten1 & / & \(1\times 4096\) \\  & LSTM & 512 & Sequence \\  & SE & 512, 4 & / \\  & Concat & / & / \\ \hline \multirow{8}{*}{Label predictor} & Flatten2 & / & / \\  & FC1 + ReLU & 128 & / \\ \cline{1-1}  & Dropout & 0.5 & / \\ \cline{1-1}  & FC2 & 5 & / \\ \cline{1-1}  & Output & \(1\times 5\) & / \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The network structure of 1D-CLANet. The collected dataset comprises CIR data from four common indoor occlusion obstacles: human body, glass, wooden door, and concrete wall, with 3000 samples per scenario. In total, we collected and screened 15,000 samples representing five categories of NLoS obstructions or LoS signals for the training and testing datasets. During the CIR data collection process for each batch, the position of the anchor \"\(\Delta\)\" remained stationary, while the tag \"\(\square\)\" was moved a fixed distance of 1 m each time to collect a set of data. Figure 8 provides detailed location information of the anchor and tag for each batch of CIR data collection, as well as initial distance measurements between the base station and the tag and equidistant distance information between each occlusion obstacle. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration Value** \\ \hline Central frequency & 6489.6 MHz \\ Bandwidth & 499.2 MHz \\ Pulse repetition frequency & 64 MHz \\ Channel & 5 \\ Inter-ranging delay & 200 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: DWM3000 Configurations. Figure 8: Instruments and experimental environment. The anchor point (“\(\Delta\)”) and tag (“\(\square\)”) are positioned as shown. LoS ranging positions are shown in blue and NLoS ranging positions are shown in red. (**a**) Stairway passage, (**b**) office corridor. In the stairwell scenario shown in Figure 8a, data were collected for LoS I, wooden door I (0.036 m), human I (1.74 m, 64 kg), and concrete wall I for NLoS obstructions.