Bayesian Detection of Distributed Targets for FDA-MIMO Radar in Gaussian Interference

In this letter, we propose a frequency diverse array multiple-input multiple-output radar detection architecture for distributed targets embedded in Gaussian interference with unknown but stochastic covariance matrix. At the design stage, we model distributed targets within one range cell as a linear combination of several contributions and assume that the interference covariance matrix obeys the inverse complex Wishart distribution. Then, we devise an adaptive decision rule by jointly exploiting the maximum likelihood approach and the Bayesian framework. Unlike existing contributions in this context, the proposed detector does not require the conventional set of training data to estimate the interference covariance matrix. The numerical examples validate the effectiveness of the proposed method also in comparison with suitable counterparts.