無線電通信仰賴在各個不同頻譜上傳遞各項訊息，為了避免遭受通信干擾狀況，研發出新式跳頻通信，跳頻通信因為在每個頻率上停留的時間極短，廣泛的運用在各式各樣的通信頻段（如WiFi無線網際網路、藍芽通信及無人機操控等設備）上，其主要原因是跳頻通信較不易受到外在的影響，且不易被偵測到，以往傳統定頻無線電藉由使用頻率、聲音特徵、出網次數等數據來分析、判斷，近年來深度學習在影像辨識技術進步飛快，本研究希望開發出一個非線性深度學習模型，將即時頻譜轉換為頻譜圖透過快速影像辨識方式，快速完成識別後，應可發揮實際效用。 本研究商借國家中山科學研究院儀器設備，藉產生型態近似相同而速率不同之跳頻信號 ，透過深度學習模型實施頻譜圖影像辨識，實驗結果確實可達到高準確率，如能持續增加不同類別信號及資料實施訓練，當能發揮更佳效益。 ;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.
