A Review of Detection and Mitigation Techniques for Radio Frequency Interference (RFI) in Radio Astronomy

Radio astronomy is crucial for understanding the universe’s origin, structure, and evolution, and for exploring extreme matter states. However, radio signals are often disrupted by noise and interference. Detecting and reducing radio frequency interference (RFI) is vital for maximizing radio telescopes’ scientific output. Traditional methods like SVD, PCA, Cumsum, and SumThreshold have limitations in handling complex RFI. Recently, researchers have combined traditional machine learning with deep learning. Neural networks offer new ideas and tools for future RFI detection. This paper discusses the principles, advantages, challenges, and effectiveness of these techniques applied to real astronomical data.Keywords: RFI,Threshold detection, Linear method,Machine learning,Deep learning