Robust Detector Designing for FDA-MIMO Radar With Training Data in Gaussian Noise

This article presents the design of robust detectors for frequency diverse array multiple-input multiple-output radar in the presence of Gaussian noise with an unknown covariance matrix. It aims to address the challenge of detecting the target of a coherent return from an appointed cell under test. Our primary focus is on the case of signal mismatch, specifically considering the 2-D mismatch, including the target and the Doppler steering vectors. To tackle these issues, we introduce a fictitious signal with a specified structure in addition to the nominal steering vector, thereby forming a new hypothesis testing model. By making use of this model, we further derive robust detectors utilizing the generalized likelihood ratio test (GLRT) and the Wald test in the presence of training data. Besides, a parametric detector is implemented, which includes robust GLRT as a particular case. The proposed detectors own constant false alarm rate properties under the null hypothesis. The effectiveness of designed algorithms is verified by comparing their performance with those of their given counterparts. Moreover, numerical examples illustrate that the functionality of presented detectors is better compared to GLRT, adaptive matched filter, Wald, and multiple-input multiple-output Wald detectors in terms of robustness against signal mismatches.