High-Frequency Radar Target Detection Using Cell Averaging Constant False Alarm Rate Method

This article explores a high-frequency radar target detection approach utilizing the Cell Averaging Constant False Alarm Rate (CA-CFAR) method with simulated data. In radar target detection systems, false alarms present significant challenges as they lead to incorrect alerts and resource wastage. The proposed CA-CFAR algorithm dynamically adjusts detection thresholds by averaging noise power estimates from reference cells surrounding the test cell, maintaining consistent false alarm rates while preserving detection sensitivity.

Implementation typically involves sliding window processing where reference cells are sampled symmetrically around the cell under test. Key computational steps include: calculating the average noise power from reference cells, applying scaling factors based on desired false alarm probability, and comparing the test cell's signal power against the adaptive threshold. The MATLAB/Simulink implementation would likely use vectorized operations for efficient sliding window processing and threshold calculations.

Experimental results with simulated data demonstrate that this method outperforms conventional approaches in false alarm control while maintaining comparable detection performance. The article further analyzes the method's advantages in computational efficiency and adaptive thresholding, while addressing limitations such as performance degradation near clutter edges and multiple target scenarios. Potential improvements include implementing adaptive reference window sizing or incorporating ordered statistics CFAR (OS-CFAR) variants for enhanced robustness in heterogeneous environments.