Radar Detection and Tracking Project

This paper provides a detailed introduction to target detection in Weibull-distributed backgrounds [1] using Constant False Alarm Rate (CFAR) detectors. The Weibull distribution, a commonly used statistical model, features two key parameters: shape and scale parameters. For detection testing, we employ Maximum Likelihood (ML) estimation algorithms to accurately determine these parameters. The implementation involves calculating parameter estimates through iterative optimization techniques, typically using functions like `wblfit` in MATLAB or `scipy.stats.weibull_min.fit` in Python. During testing, we utilize visual representations and code demonstrations to present experimental results. The code structure includes: - Background parameter estimation module - CFAR threshold calculation component - Detection performance evaluation scripts Additionally, we provide relevant references for readers seeking deeper understanding of this subject. In summary, this paper comprehensively covers the methodology and experimental results of target detection using CFAR detectors in Weibull-distributed backgrounds, featuring practical implementation approaches and performance analysis.