MATLAB Implementation of Weibull Distribution for Time-Frequency 2D CFAR Processing

Time-frequency two-dimensional constant false alarm rate (CFAR) processing under Weibull, log-normal, and Gaussian distribution conditions represents a sophisticated signal processing methodology designed to mitigate false alarms across varying operational scenarios. This approach employs advanced statistical algorithms to analyze signal characteristics and classify them according to specified distribution models. The MATLAB implementation typically involves key functions such as wblfit() for Weibull parameter estimation, lognfit() for log-normal distribution fitting, and normfit() for Gaussian parameter calculation. The core algorithm structure includes: 1) Time-frequency transformation using spectrograms or wavelet analysis, 2) Statistical modeling of background clutter through distribution fitting functions, 3) Adaptive threshold calculation based on distribution parameters, and 4) False alarm suppression through multidimensional filtering techniques. By analyzing signals through these statistical distributions, we gain deeper insights into signal nature and improve false alarm identification accuracy. Furthermore, this methodology enhances predictive capabilities for future events, enabling proactive decision-making. The implementation often incorporates sliding window techniques for local parameter estimation and matrix operations for efficient 2D processing. Consequently, time-frequency 2D CFAR processing under Weibull, log-normal, and Gaussian distributions constitutes a valuable technical solution with broad applications across communication systems, medical diagnostics, financial analytics, and radar signal processing domains.