Enhancing radar tracking accuracy using combined Hilbert transform and proximal gradient methods

Abstract

Accurate radar tracking is crucial in defense, navigation, and surveillance applications, where high precision and resilience to noise are essential. Traditional radar tracking techniques, such as Kalman Filters and Particle Filters, often struggle with performance limitations in noisy and non-linear environments, leading to inaccuracies in target tracking. To address these challenges, we propose a hybrid radar tracking approach combining the Hilbert Transform with the Proximal Gradient Method within a convex optimization framework. This combination leverages the Hilbert Transform's signal enhancement capabilities with the Proximal Gradient Method's optimization strength, improving accuracy and robustness under challenging conditions. Experimental results demonstrate that the proposed method achieves a 23% reduction in Mean Squared Error (MSE) and a 20% increase in tracking accuracy compared to conventional methods, alongside a Signal-to-Noise Ratio (SNR) of approximately 18.3 dB, indicating superior noise resilience. While the hybrid method offers significant improvements, it does involve increased computational complexity and may be sensitive to initial parameter settings, requiring careful tuning for optimal performance. Nevertheless, this method represents a promising advancement over traditional techniques, providing a more accurate and resilient solution for modern radar tracking applications.

Keywords

• Radar tracking
• Proximal gradient method
• Hilbert transform
• Trajectory estimation
• Convex optimization