# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Monitoring Technology Based on Artificial Feature Extraction
#### 3.3.5 Experimental Evaluation  
The Interference pattern evaluation index is demonstrated as follows:  
The confusion matrix, accuracy and recall rate are proposed as evaluation indicators for interference patterns. The prediction accuracy of each type of interference and the recall rate of each type of interference are calculated to reflect the performance of the model. The classification results of all interference styles will be displayed in a confusion matrix to clearly show the relationship and distribution between the real value and predicted value of each type of interference. Confusion matrix, also known as error matrix, is a standard format for accuracy evaluation, which is expressed in the form of matrix with n rows and N columns. In artificial intelligence, confusion matrix is a visual tool, especially for supervised learning. In the image accuracy evaluation, it is mainly used to compare the classification results with the actual measured values. The accuracy of the classification results can be displayed in a confusion matrix. In the two classification model, the confusion matrix combines all the results of the predicted situation with the actual situation to form four situations: true positive, false positive, true negative and false negative, which are represented by \(TP\), \(FP\), \(TN\) and \(FN\), respectively (\(T\) represents correct prediction and \(F\) represents wrong prediction). Precision (\(P\)) also known as precision refers to the ratio of the number of correctly classified samples to the total number of classified samples. The formula is as follows:  
\[P=\frac{TP}{TP+FP} \tag{1}\]  
Recall (\(R\)), also known as recall, shows the ratio of the number of correctly classified samples to the total number of samples that should be classified. The formula is as follows:  
\[R=\frac{TP}{TP+FN} \tag{2}\]  
Here, the Interference parameter evaluation index is shown below. Root mean square error  
the prediction are calculated, and the confusion matrix of interference pattern classification results are displayed. The root mean square error and average absolute error are also considered. Figure 9 is a confusion matrix diagram of the classification results of four types of interference signals (single sweep and round-trip sweep are classified into the same category). It can be seen from the figure that the categories of interference signals are accurately predicted. Combined with Table 1, the accuracy and recall rate of interference signals are close to 100%. Since the SIFT compresses the data, there is a quantization error of 2 ms in time and 4 KHz in frequency. Tables 2 and 3 are the root mean square error and absolute error of interference signal prediction parameters respectively. Single frequency interference has only one carrier frequency. Since the frequency conversion range of small slope sweep interference is small, only its center frequency is predicted in this paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Total** & **Accuracy (P)** & **Recall (R)** \\ \hline All samples & 2250 & 0.999 & \(1\) \\ \hline Single frequency & 450 & 0.996 & \(1\) \\ interference & 450 & 1 & 0.999 \\ \hline Sweep interference & 900 & 0.999 & \(1\) \\ \hline Sweep interference & 450 & 1 & 0.999 \\ with small slope & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative record of interference pattern test results. Figure 9: Confusion matrix of interference pattern classification results. It can be seen from Table 2 that the root mean square error of predicting the start and end time of four types of interference signals is less than 4 ms. Among them, the end time prediction of sweep interference is the most accurate, and the root mean square error is 1.1 milliseconds. However the root mean square error of starting time prediction is the largest (3.7 ms). The root mean square error of the other three types is about 2 ms, which is basically caused by quantization.