High-Frequency Radar Target Detection with Maximum Likelihood Constant False Alarm Rate Method

In this paper, we investigate target detection methods for high-frequency radar systems, with particular focus on Maximum Likelihood Constant False Alarm Rate (ML-CFAR) detection under Weibull distributed clutter. The ML-CFAR algorithm implementation typically involves estimating distribution parameters using maximum likelihood estimation (MLE) and adapting detection thresholds based on local clutter statistics. This approach enables more accurate target detection while effectively controlling false alarm rates in non-Gaussian environments. We also discuss relevant technical methodologies including advanced signal processing techniques and statistical data analysis approaches. The signal processing implementation may involve clutter modeling, Doppler processing, and adaptive filtering algorithms to enhance target visibility. Our research findings demonstrate significant improvements in detection performance, and we propose future research directions focusing on computational optimization of the ML-CFAR algorithm and integration with machine learning techniques for further enhancement of high-frequency radar target detection capabilities.