Radio Frequency Signal Detection and Identification Algorithm Based on Time-Frequency Joint Domain Electromagnetic Spectrum Data

• Published in: 2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) pp. 829 - 833
• Main Authors: Li, Yaling, Chen, Zhenjia, Zhang, Yonghui
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.08.2023
• Subjects: Artificial intelligence; Clustering; Clustering algorithms; Detection algorithms; Electromagnetic spectrum; Electromagnetic spectrum detection; Pattern recognition; Signal recognition; Time-domain analysis; Time-frequency analysis; Time-frequency joint domain
• DOI: 10.1109/PRAI59366.2023.10331947

Signal recognition is an important part of electromagnetic spectrum detection. With the complexity of signals and the diversity of signal modulation methods in the future space electromagnetic environment, it is especially necessary to identify and analyze signals in the electromagnetic spectrum detection. Currently, the main method of electromagnetic spectrum detection is energy detection algorithm, which can blind source recognition without prior information. Based on the previously collected time-frequency electromagnetic spectrum data, two kinds of radio signal detection and recognition framework are proposed in this paper, meanwhile, clustering algorithm is introduced into the field of radio signals. Python is used for radio frequency signal detection and identification simulation. This paper first analyses the energy detection algorithm, then introduce two kinds of radio signal detection and recognition, and finally carries out the practice to identify the frequency band of the signal, so as to distinguish the signal from the background noise.