Adaptive Signal Detection of Range-Spread Targets in Persymmetric Compound Gaussian Clutter

To improve the detection performance, this paper investigates adaptive detection of range-spread targets in compound Gaussian clutter. The texture component of the clutter follows an inverse gamma distribution, and its speckle component possesses a persymmetric covariance matrix. We derive a two-step generalized likelihood ratio test (2S-GLRT) under the considered signal model, which effectively suppresses interference and performs target detection, while also exhibiting good performance under mismatch conditions. Simulation and experimental results demonstrate superior performance over traditional detectors.