Greatest Of Constant False Alarm Rate Detection - GO-CFAR Detector

Greatest Of Constant False Alarm Rate Detection - GO-CFAR Detector: This implementation includes simulation of GO-CFAR detection for target-free signals with noise only, and GO algorithm simulation for target-containing signals in Rayleigh clutter background. Some programs are primarily designed for visualization, plotting signal waveforms during GO-CFAR detection and threshold comparison diagrams; while other programs focus on calculating detection probability and false alarm probability during the GO-CFAR detection process.

To simulate GO-CFAR detection, we first need to generate target-free signals containing only noise. Then, we can apply the GO-CFAR algorithm to detect these signals and determine the false alarm probability through statistical analysis of detection results.

Additionally, we need to perform GO algorithm simulation for target-containing signals in Rayleigh clutter background. This requires adding target components to the signal and modeling Rayleigh-distributed clutter environment. By implementing the GO-CFAR detection algorithm, we can detect targets and compute the detection probability using Monte Carlo simulations.

To better understand the GO-CFAR detection process, we can plot comparison diagrams of signal waveforms and detection thresholds. This visualization approach helps intuitively demonstrate the performance characteristics of the GO-CFAR detector under different scenarios.

In summary, the simulation of the GO-CFAR detector involves multiple programming components including signal generation, application of GO-CFAR algorithm, visualization plotting, and probability calculation. These components collectively form a comprehensive simulation environment for the complete GO-CFAR detection process.