## 5 Experiment, Results and Discussion
### Experimental Results and Discussion  
#### 5.2.1 Network Model Training Results and Discussion  
The NLOS data and the LOS data are 1812 and 6967, respectively. There is five times of discriminator training in one training, and one training of the generator, with a total of 1000 times of training. According to the theory of GAN, we want to maximize the loss function of the discriminator as much as possible. The loss function of the discriminator is shown in Figure 15. It can be seen that the loss function of the discriminator gradually increased and stabilized near the maximum value. After the GAN is trained, we put 2000 random Gaussian noise data into the generator to generate 2000 samples. We copied the original dataset and added those 2000 samples to form a new data set. Now our original data set includes 6967 LOS data and 1812 NLOS data, and the new data set includes 6967 LOS data and 3812 NLOS data. The two data sets are divided into the training set and the test set according to the ratio of 7:3. We use these training sets to train the MLP model and CNN model, and test our model with the same test set. After the network model is trained, on the same test set, the performance of MLP model and CNN model trained by the training set of the original data set is 88.0790% and 90.8125%, respectively. The performance of the MLP model and CNN model trained by  
Figure 14: **(a,b)** are the concept diagrams of the static experiment and dynamic experiments, respectively. The black thick solid line indicates the boundary of the environment, and the five-pointed star indicates the position of the tag at different positions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Static/Dynamic Experiment** & **Position** &
\begin{tabular}{c} **Amount of Sample Data** \\ **(Group)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Static experiment} & red & 70 \\  & blue & 68 \\  & green & 68 \\ \hline \multicolumn{2}{c}{Dynamic experiment} & - & 104 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The amount of data collected by the experiment to test our designed positioning system. The data is obtained by one tag and three anchors, each performing a ranging as a group. the training set of the new data set is 89.1420% and 91.1921% respectively. We further evaluate the performance of our model using the \(classification\_report()\) function from the machine learning _scikit-learn_ library. The performance of our trained MLP models and the performance of our trained CNN models are shown in the Tables 4 and 5, separately. As can be seen from Tables 4 and 5, the precision of the MLP model trained by the training set of the original and new data sets for the NLOS class is 0.80 and 0.78, the recall is 0.56 and 0.65, and the f1-score is 0.66 and 0.71, respectively. The accuracy of all samples are 0.88 and 0.89, respectively. The precision of the CNN model trained by the training set of the original data set and the new data set for the NLOS class is 0.82 and 0.79, the recall is 0.72 and 0.77, and the f1-score is 0.76 and 0.78, respectively. The accuracy of all samples is 0.91. After the GAN model is used to enhance the data, the recall of NLOS data in MLP model and CNN model is increased by 9% and 5% respectively, and the f1-score is increased by 5% and 2% respectively. This means that GAN data enhancement can improve the performance of the network model. In horizontal comparison, after the GAN model is used to enhance the data, the CNN model performs better than the MLP model in terms of the recall and f1-score of both LOS and NLOS class. In conclusion, data enhancement by GAN can improve the performance of the network model. By comparing the performance of MLP model and CNN model, it can be seen that the CNN model has better performance.