Optimum and Near-Optimum Coherent CFAR Detection of Radar Targets in Compound-Gaussian Clutter With Generalized Inverse Gaussian Texture

In this article, we propose optimum and near-optimum adaptive coherent detectors of radar targets in compound-Gaussian clutter with generalized inverse Gaussian texture (CG-GIG clutter). The target amplitude and the speckle covariance matrix are modeled as unknown quantities to be estimated. On the basis of the two-step generalized likelihood ratio test (GLRT) and the estimate of the speckle covariance matrix, the optimum coherent detector and its adaptive version are designed in this article. It is demonstrated that the proposed optimum coherent detector contains three common detectors, which are the optimum K detector, the generalized likelihood ratio test linear-threshold detector (GLRT-LTD), and the GLRT detector for compound-Gaussian clutter with inverse Gaussian texture. Due to the existence of the modified Bessel function depending on data, the optimum coherent detector is computationally unrealizable. Therefore, a near-optimum coherent detector with a new structure named $\alpha$ matched filter ($\alpha$-MF) detector and its adaptive version are proposed, which decreases the computational cost. The proposed near-optimum coherent detector contains two common detectors, the GLRT-LTD and the $\alpha$-MF detector in K-distributed clutter, and has a comparable detection performance of the near-optimum detector in compound-Gaussian clutter with inverse Gaussian texture, which was proposed before. Theoretical analysis and numerical experiments illustrate that the proposed two detectors for CG-GIG clutter have the constant false alarm ratio property relative to the estimate of the speckle covariance matrix and the Doppler steering vector. Moreover, the detection performance of the two coherent detectors are evaluated by the simulated and real data.