Performance Evaluation of CA-CFAR Algorithm on Sinusoidal Beat FFT with Controllable SNR

The experiment evaluates the CA-CFAR algorithm performance on Fast Fourier Transform (FFT) processed sinusoidal beat signals under controlled SNR conditions. CA-CFAR (Cell Averaging Constant False Alarm Rate) is a widely used target detection algorithm that performs adaptive background noise estimation, enabling robust target detection and signal separation. In this implementation, we process sinusoidal beat signals through FFT analysis before applying CA-CFAR thresholding. The core algorithm involves calculating reference cells around the cell under test (CUT) to estimate noise power, then applying a scaling factor to maintain constant false alarm probability. Key implementation steps include: - Generating sinusoidal beat signals with programmable amplitude and phase parameters - Adding controlled Gaussian noise to achieve target SNR values using arithmetic SNR calculation methods - Applying windowing functions (Hamming/Hanning) before FFT processing to reduce spectral leakage - Implementing CA-CFAR with configurable guard cells, training cells, and threshold multipliers - Analyzing detection probability and false alarm rates across varying SNR conditions By systematically adjusting SNR parameters, we simulate diverse signal processing scenarios to assess the algorithm's stability and detection accuracy under different noise interference levels. The experimental results provide valuable insights into CA-CFAR's practical performance characteristics and establish benchmarks for future algorithm optimizations in radar and signal processing applications.