Analysis of Smallest-Of Cell Averaging CFAR Detection in Homogeneous Gamma-Distributed Background - Mohamed DJABALLAH; ENCA/ Mohamed SAHED (University of M'sila, 2025)
This study investigates the problem of adaptive radar target detection in both homogeneous and non-homogeneous Gamma-distributed clutter environments. The objective is to maintain a Constant False Alarm Rate (CFAR) during the detection process. It is assumed that the radar system employs a square-law detector preceding the CFAR processing stage. Initially, the fundamental principles of radar target detection in noise are presented, along with an overview of CFAR detection techniques. The study then focuses on a detailed examination of several Mean-Level CFAR detectors operating in Gamma-distributed clutter, specifically the Cell Averaging (CA-CFAR), Greatest Of (GO-CFAR), and Smallest Of (SO-CFAR) algorithms. A comprehensive theoretical analysis is conducted for each detector. Closed-form expressions for the probability of false alarm (Pfa) are derived. However, the calculation of the probability of detection (Pd), based on the exact statistical characterization of the cell under test (CUT), involves complex integrals that are computationally intensive. To address this issue, approximate expressions for Pd are proposed. These approximations offer computational efficiency and are suitable for real-time implementation. The theoretical findings are validated through numerical evaluation, including both integral-based computations and Monte Carlo simulations, under various clutter scenarios. Furthermore, a performance comparison between the studied detectors and the optimal detector is performed, assuming a homogeneous clutter environment. The results demonstrate that the SO-CFAR detector exhibits superior performance in homogeneous clutter, while the GO-CFAR detector proves to be more effective in nonhomogeneous environments.