Parameter Estimation Algorithm for Suppression Jamming Signals Based on an Improved YOLOv8
TL;DR
A jamming detection and parameter estimation algorithm based on an improved YOLOv8 model that achieves high detection accuracy and low mean squared relative error in the considered simulation scenarios, and maintains effective detection and parameter-estimation performance in composite jamming scenarios.
Abstract
In military confrontations, intentional suppression jamming can significantly degrade the reliability of friendly communication systems. By integrating conventional frequency hopping with spectrum sensing, cognitive frequency hopping technology can proactively avoid jammed frequency bands, thereby enhancing anti-jamming performance. This paper proposes a jamming detection and parameter estimation algorithm based on an improved YOLOv8 model. The proposed method extracts the time–frequency features of jamming signals and predicts their bounding boxes in time–frequency images. Based on the coordinates of the predicted bounding boxes, the center frequency, bandwidth, and temporal parameters of the jamming signals are estimated, thereby supporting spectrum sensing. Simulation results under MATLAB-generated signal conditions show that the proposed method achieves high detection accuracy and low mean squared relative error in the considered simulation scenarios. In addition, the proposed method maintains effective detection and parameter-estimation performance in composite jamming scenarios. This study provides a useful simulation-based reference for spectrum sensing in cognitive frequency hopping systems.