Adaptive Target Detection in Partially Homogeneous Environment for FDA-MIMO Radar

This paper investigates the adaptive target detection problem in FDA-MIMO radar embedded in Gaussian noise with an unknown covariance matrix, considering the scenario of mismatched steering vectors. Firstly, we assume that the covariance matrix of the test data and training data has the same structure with different magnitude levels. Next, the subspace model is adopted to enhance the robustness of the detector against mismatched signals. Furthermore, TS-GLRT detector is designed based on the generalized likelihood ratio test (GLRT), then we validated its constant false alarm rate (CFAR) property. Finally, simulation results demonstrate the effectiveness of the proposed approach.