Adaptive Detection in Compound-Gaussian Clutter with Inverse Gaussian Texture

This paper mainly deals with the detection problem of the target in the presence of the Compound-Gaussian (CG) distribution clutter with the unknown Power Spectral Density (PSD). Traditionally, the CG distributions, in particular the K distribution and the complex multivariate distribution, are the widely used models for the clutter measurements from the High Resolution (HR) radars. Recently, the CG distribution with the Inverse Gaussian (IG) texture, the specific class of CG clutter, is represented as the IG-CG distribution and validated to provide the better fit with the recorded clutter data than the mentioned two competitors. Within the IG-CG framework, the detector is here proposed in terms of the two-step Generalized Likelihood Ratio Test (GLRT) criterion, and the empirical estimation method is resorted to estimate the unknown PSD in order to adapt the realistic scenario. The proposed detector is tested on the real-life IPIX radar data, in comparison with the existing Adaptive Normalized Matched Filter (ANMF) processor, and the detection results illustrate that it outperforms ANMF.