CFAR Simulation in Radar Signal Processing

This CFAR (Constant False Alarm Rate) simulation program serves as a crucial technique in radar signal processing. The CFAR algorithm adaptively adjusts detection thresholds based on the statistical characteristics of background noise while maintaining a constant false alarm probability, thereby enhancing radar system performance. The simulation implements and compares four distinct CFAR detection thresholds under identical false alarm rate conditions: ideal detection threshold (theoretical baseline), cell averaging CFAR (CA-CFAR) that estimates noise power from reference cells, censored CFAR that eliminates outlier interference through trimming operations, and ordered statistics CFAR (OS-CFAR) that employs rank-ordered samples for robust performance in non-uniform clutter. Through comparative analysis of these thresholding methods, we evaluate their respective performance characteristics and applicability in practical radar signal processing scenarios. Key implementation aspects include Monte Carlo simulations for false alarm probability validation, sliding window mechanisms for reference cell selection, and threshold multiplier calculations based on different statistical models. The code structure typically involves noise power estimation routines, threshold factor computation modules, and detection probability analysis functions to systematically assess each algorithm's effectiveness across varying clutter environments.