Distributed Airborne MIMO Radar Detection in Compound-Gaussian Clutter without Training Data

This paper deals with the detection problem of a moving target for the distributed airborne multi-input multi-output radar embedded in the compound-Gaussian and non-homogeneous clutters, without assuming that training data are available. A novel detector combing the Bayesian approach and the generalized likelihood ratio test is proposed, where we model the covariance of clutter as an inverse Wishart distribution with an unknown average clutter covariance matrix (ACCM). More precisely, we regard the unknown ACCM as a structured matrix with the Hadamard product form involving two independent parts, composing of the covariance matrix taper (CMT) and the Doppler spectrum component. Further, the proposed detector exploits a nonlinear processing in an iteration fashion to reconstruct sparse signals with the aim of estimating the unknown spectrum of clutters. As to the CMT component, we resort to generalized CMT model to improve estimation accuracy. Numerical simulations are provided to assess the capability of the proposed detector in different complicated scenarios.