| dc.description.abstract | Radio communication relies on transmitting various information on different frequency spectrums. In order to avoid communication interference, a new type of frequency hopping communication has been developed. Because frequency hopping communication stays on each frequency for a very short time, it is widely used in a variety of The main reason is that frequency hopping communication is less susceptible to external influences and is not easy to be detected. In the past, traditional fixed-frequency radios By using data such as frequency, sound characteristics, and network access times to analyze and judge, in recent years, deep learning has made rapid progress in image recognition technology. Identification method, after the identification is completed quickly, it will be able to play a practical role.
In this research, the instruments and equipment of the National Chung-Shan Institute of Science and Technology(NCSIST) are used to generate frequency hopping signals with approximately the same type but different rates. The deep learning model is used to implement spectrogram image recognition. The experimental results can indeed achieve high accuracy. Class signals and data implementation training should be able to play a substantial effect. | en_US |