Target Detection in Weibull Clutter Environment

We can employ the CA-CFAR (Cell-Averaging Constant False Alarm Rate) method to detect targets in Weibull-distributed clutter. This approach is not only applicable to radar signal processing but also extends to other domains such as image processing and natural language processing. The CA-CFAR method operates on statistical principles, achieving target detection through statistical analysis of clutter characteristics by comparing signal levels against dynamically computed thresholds. In practical implementation, the CA-CFAR algorithm typically involves sliding window processing where reference cells surrounding the test cell are used to estimate the clutter power level. The threshold is calculated by multiplying the estimated noise power with a scaling factor determined by the desired false alarm probability. Additionally, we need to develop comprehensive clutter generation programs to facilitate experimentation and validation. These programs should simulate various types of clutter distributions, particularly focusing on Weibull distribution parameters (shape parameter k and scale parameter λ) to better evaluate the detection method's performance and applicability range. The clutter simulation code should include functions for parameter configuration, random number generation following Weibull distribution, and visualization tools for performance analysis.