GLRT-Based Adaptive Target Detection for FDA-MIMO Radar in Mainlobe Deceptive Jamming
Bang Huang, Danilo Orlando, Wen-Qin Wang, Jiangwei Jian, Yizhen Jia, Wenkai Jia, Weijian Liu
Abstract
This article focuses on enhancing target detection in the presence of deceptive jamming within the mainlobe, complicated by Gaussian noise, by leveraging frequency diverse array multiple-input multiple-output (FDA-MIMO) radar and existing training data. A comprehensive model for the received signal in FDA-MIMO radar is first developed, accounting for targets, jamming, and noise. The detection challenge, particularly with multiple false targets within the mainlobe, is then formulated. To improve the robustness of the subspace detector, an innovative approach is proposed, assuming the transmit and receive steering vectors of true and false targets belong to distinct but known subspaces, even though their exact coordinates are unknown. Using the generalized likelihood ratio test (GLRT) criterion, two adaptive detectors are introduced: the one-step GLRT (OGLRT) and the two-step GLRT (TGLRT), both of which leverage the available training data. Simulations show that both detectors exhibit a constant false alarm rate (CFAR) with respect to the noise covariance matrix. Moreover, the OGLRT outperforms TGLRT with limited training data and matched signals, while TGLRT is more effective than OGLRT in handling steering vector mismatches.