Adaptive Distributed Target Detection for FDA-MIMO Radar in Clutter Environment without Training data

This paper studies the problem of adaptive distributed target detection for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, where the target is embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model establishes distributed target detection as a summation expression, which is different from the traditional detection models in MIMO or phase array radars that discuss only point-like target. Next, according to the rules of generalized likelihood ratio tests (GLRT), Rao and Wald tests, we designed three adaptive detectors without the training data. The numerical results validate the proposed method and all theoretical analyses.