Constant False Alarm Rate

Constant False Alarm Rate (CFAR) techniques aim to maintain a consistent rate of false positives in detection systems, regardless of background noise or clutter variations. Current research focuses on developing robust CFAR algorithms, including both parametric methods based on assumed statistical distributions (e.g., Gaussian, Weibull) and non-parametric approaches like the Wilcoxon test, which are less sensitive to distributional assumptions. These advancements are crucial for improving the reliability of various applications, such as radar object detection, spectrum sensing, and medical alarm systems, where minimizing false alarms is paramount for efficient resource allocation and accurate decision-making. The integration of machine learning, particularly deep learning, is a growing trend, enabling more flexible and data-driven CFAR solutions.