Reduced-dimension Subspace Detector Design for FDA-MIMO Radar in Sample-starved Scenarios

This paper focuses on the detection of a point-like target in sample-starved environments with Gaussian interference, which includes strong main-lobe interference and weak thermal noise for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. At the design stage, the target signature is only partially known and assumed to lie in a known subspace. To solve the sample-starved problem, we adopt a reduced-dimension method to decrease the requirement of training data via pre-multiplying test and training data by a suitable matrix representing the signal subspace. Then, the generalized likelihood ratio test criterion is applied to come up with a reduced-dimension subspace detector. Numerical results validate the effectiveness of proposed detector.