This research addresses the challenge of detecting radar range-spread targets in compound-Gaussian clutter environments. In such scenarios, the target signals occupy unknown coordinates within a subspace, while the clutter is modeled as compound-Gaussian distribution involving inverse Gamma textures and complex Gaussian speckles with an unknown persymmetric covariance matrix. Additionally, we assume the availability of training data for estimating clutter covariance matrix. Utilizing a two-step approach, three detectors are proposed according to the Gradient, Rao, and Wald tests, by taking advantage of the persymmetry of the clutter covariance matrix. Theoretical analyses demonstrate that th... More >