Adaptive Subspace Detection of Range-Distributed Targets in Compound Gaussian Clutter with Generalized Inverse Gaussian Texture

This paper addresses adaptive subspace detection of range-distributed targets in compound Gaussian clutter with Generalized Inverse Gaussian (GIG) texture. Novel adaptive subspace detectors are developed based on the generalized likelihood ratio, Rao, and Wald test criteria, employing two-step design strategies that incorporate the persymmetric covariance structure and the GIG texture distribution to enhance detection performance. Furthermore, to achieve full adaptivity, a novel method is proposed for adaptively estimating the clutter texture parameters. The effectiveness of the proposed adaptive detectors, combined with this new texture parameter estimation method, is validated via numerical examples in various non-Gaussian clutter environments, demonstrating superior detection performance compared to several existing methods.