This study proposes a method to address the need for real-time learning due to the high power of jamming signals and environmental variability, where traditional neural networks are unsuitable because of their long training and inference times, and SV...
This study proposes a method to address the need for real-time learning due to the high power of jamming signals and environmental variability, where traditional neural networks are unsuitable because of their long training and inference times, and SVR lacks sufficient interference cancellation performance relative to its complexity. To address these issues, this study extracts key features using F-Regression, removes outliers with Isolation Forest, and employs NuSVR to achieve real-time interference cancellation.