# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Monitoring Technology Based on Artificial Feature Extraction
#### 3.3.3 Dataset Construction  
In the field of deep learning, the quality of data sets has a vital impact on solving problems. A high-quality dataset can often improve the quality of model training and the accuracy of prediction. Many researchers have spent a lot of effort on the creation and modification of data sets. However, in the practical application in specific fields, the public datasets play a limited role, and often cannot solve the problem with characteristics. The training effect on imagenet dataset with millions of samples may not be as good as the small dataset collected by researchers themselves. For the problem of interference information monitoring within 2 MHz monitoring bandwidth, it is very necessary to make a specific data set in this scheme.  
An excellent dataset is composed of two characteristics: the distribution and diversity of the dataset is close to the real application scenario.  
In order to ensure the diversity of data, this scheme makes diversified changes from three aspects: interference type, transformation range of interference frequency and interference duration. For the monitoring bandwidth of 2 MHz, the frequency conversion range of all interference signals designed in this scheme is 35 KHz to 1975 KHz. Since the time-frequency map is obtained from the sampled data per second through SIFT, the time corresponding to the abscissa of each time-frequency map is 1 s. In this scheme, the duration of all interference information is set in the range of 1 ms to 1000 ms.  
The dataset is composed of four types of interference: single frequency interference, frequency hopping interference, frequency sweeping interference and unknown interference. Among them, there are two types of sweep interference: single sweep and round-trip sweep. In addition to normal single tone, single frequency interference also includes sweep frequency interference with small slope (aspect ratio greater than 20). As shown in Figure 5, the dataset has six types of interference patterns, which fully ensures the diversity of interference signals.  
Figure 4: Network structure.  
In addition, in order to make the data set close to the real scene, this scheme simulates and realizes three common channels: Gaussian channel, rice channel and Rayleigh channel according to the modeling of satellite communication channel in Section 3.1; Three different signal-to-noise ratios (SNR): 5 dB, 8 dB and 10 dB, are set to ensure the diversity of interference energy. Taking single tone interference as an example, Figure 6 shows the time-frequency diagram of single tone interference signal passing through three different channels under 10 dB signal-to-noise ratio; Figure 7 shows the time frequency diagram of single tone interference signal under three different drying ratios in Gaussian channel.  
Figure 5: Time frequency diagram of six kinds of interference. (**a**) Single frequently interference, (**b**) Frequency hopping interference, (**c**) Single sweep interference, (**d**) Round-trip frequency sweep, (**e**) Sweep interference, (**f**) Unknown type interference.  
Figure 6: Time frequency diagram of single frequency interference in different channels, SNR = 10 dB. (**a**) Gaussian channel, (**b**) Rayleigh channel, (**c**) Rice channel.  
To sum up, the sample diversity of the data set can be expressed as follows:  
* Interference type: single frequency, single sweep, round-trip sweep, small slope sweep, frequency hopping, unknown type interference
* Channel type: Gaussian white noise channel, Rayleigh channel, Rice channel
* Interference- noise ratio: 5 dB, 8 dB, 10 dB.