Hybrid Denoising of B-Scan UWB Radar Data Using Higher-Order Statistics and Wavelet Shrinkage

This paper introduces a novel hybrid approach for noise reduction in B-scan UWB radar data, specifically designed for the detection of cyclists, pedestrians, and cars. The proposed method integrates Higher-Order Statistics (HOS) with Wavelet Shrinkage Denoising (WSD) to enhance signal quality and improve detection accuracy. The effectiveness of this hybrid approach is evaluated against widely used noise removal methods, including Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and the individual use of WSD and HOS. A quantitative analysis based on the Signal-to-Noise Ratio (SNR) demonstrates that the HOS-WSD combination outperforms the other methods in terms of noise reduction. These results highlight the superiority of the hybrid approach and its potential to enhance the robustness of UWB radar detection systems.