Adaptive Bayesian detection for multistatic radar in compound-Gaussian clutter with inverse Gaussian texture

This work addresses the problem of adaptive target detection in compound-Gaussian clutter for multistatic radar. We exploit a-priori knowledge of the clutter to alleviate the performance degradation due to the non-Gaussian characteristic of the clutter. Here, the inverse Gaussian distribution is adopted to describe the texture of the clutter. In addition, the speckle covariance matrix of the clutter is modeled as a random matrix following the complex inverse Wishart distribution. Using different fusion rules between the a-priori knowledge of the texture and the speckle component within the Bayesian framework, two adaptive detectors are derived based on the Generalized Likelihood Ratio Test (GLRT) criterion. Finally, the detection performance of the proposed detectors is verified using simulated data. The results show the superiority of the proposed adaptive detectors over the existing techniques.