Radar signal processing techniques for high-precision target detection in hybrid cognitive radar

Radar signal processing is crucial for modern surveillance, defense, and autonomous navigation, requiring advanced techniques for accurate target detection and tracking. This paper reviews methods in hybrid cognitive radar, which integrates traditional techniques with deep learning models like YOLO, Mask R-CNN, and LSTM. Key components include Kalman filtering for predictive tracking, Doppler velocity estimation for differentiating moving objects, true track ID for consistent identification, and radar cross-section (RCS) analysis for target classification. By combining conventional radar methods with AI models, the study enhances detection accuracy and adaptability; YOLO enables rapid object detection, Mask R-CNN improves segmentation, and LSTM refines trajectory predictions. Simulation results show an increase in detection accuracy from 99.2% to 99.8%, fewer false positives, and improved trajectory predictions. The review highlights the potential of AI-driven radar technologies in defense, aerospace, and autonomous navigation, paving the way for future research in cognitive radar optimization and sensor fusion.