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

TL;DR

This paper explores applying YOLOv8, a computer vision model, to detect and classify communication and radar signals in congested RF environments, demonstrating high accuracy across multiple scenarios with synthetic datasets.

Contribution

It is the first to adapt YOLOv8 for joint detection and classification of RF signals in highly congested environments, including communication and radar signals.

Findings

Achieved mean average precision of 0.888 for digital modulation signals.

Reached nearly perfect mAP of 0.995 for radar pulse signals.

Demonstrated effectiveness in multi-target detection in continuous-wave radar scenarios.

Abstract

In this paper, we present a comprehensive study on the application of YOLOv8, a state-of-the-art computer vision (CV) model, to the challenging problem of joint detection and classification of signals in a highly dynamic and congested RF environment. Using our synthetic RF datasets, we explored 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 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 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. The synthetic datasets used in these experiments reflect a realistic mix of communication and radar signals with varying degrees of interference and congestion - a setup that has been overlooked by many past research efforts, which have primarily focused on ML-based classification of digital communication signal modulation schemes. 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 hope our findings will spur further collaborative efforts to tackle the complexities of congested RF spectrum environments.