Joint Detection and Classification of Communication and Radar Signals in Congested RF Environments Using YOLOv8

In this paper, we present a comprehensive study on the application of YOLOv8, a state-of-the-art (SOTA) computer vision (CV) model, to the challenging problem of joint detection and classification of signals in a highly dynamic and congested radio frequency (RF) environment. Using our uniquely created synthetic RF datasets, we were able to explore three different scenarios with congested communication and radar signals. In the first study, we applied YOLOv8 to detect and classify multiple digital modulation signals coexisting within a highly congested and dynamic spectral environment with significant overlap in both frequency and time domains. The trained model was able to achieve an impressive mean average precision (mAP) of 0.888 at an intersection over union (IoU) threshold of 50%, signifying its robustness against spectral congestion. The second part of our research focuses on the detection and classification of multiple polyphase pulse radar signals, including the Frank code and P1 through P4 codes. We were able to successfully train YOLOv8 to deliver a nearly perfect mAP50 score of 0.995 in a densely populated signal environment, further showcasing its capability in radar signal processing. In the last scenario, we demonstrated that the model can also be applied to the multi-target detection problem for continuous-wave radar. Our study offers a unique approach to the spectrum sensing problem, a crucial prerequisite for developing the spectrum sharing strategies. The generated synthetic datasets, consisting of radar and communication signals, reflect a realistic dynamic spectrum environment with varying degrees of interference and congestion—a setup often overlooked by many past research efforts. Our study demonstrated the potential of advanced CV models in addressing spectrum sensing challenges in congested and dynamic RF environments involving both communication and radar signals. We believe these findings can drive advancements in both military and civilian spectrum sensing applications, enabling more efficient and intelligent spectrum sharing technologies through faster signal detection, classification, and spectrum activity pattern analysis.