Interference Suppression With MR-FDA-MIMO Radar Using Virtual Samples Based Beamforming
Zhixia Wu, Shengqi Zhu, Jingwei Xu, Lan Lan, Mengdi Zhang, Feilong Liu
Abstract
By adopting the transmit minimum redundancy array (MRA) in frequency diverse array multiple-input multiple-output (FDA-MIMO), extra degrees-of-freedom (DOFs) in the transmit spatial domain can be obtained owing to enlarged virtual elements. In this paper, an interference suppression method is developed with the MR-FDA-MIMO radar using data-dependent beamforming based on virtual samples. In our designed method, both the transmit and receive beamforming are explored via a two-stage approach, including: i) at the receive beamforming stage, sidelobe interferences are firstly mitigated with minimum variance distortionless response (MVDR)-based beamforming after blocking the true target and mainlobe interferences. ii) at the transmit beamforming stage, the samples from the physical array are divided into multiple subsets, while a difference co-array procedure is applied to each subsets and multiple virtual samples are obtained to construct the interference-plus-noise covariance matrix. Furthermore, such a matrix is reconstructed based on the Capon spectrum. In Addition, the errors for both the virtual samples and virtual covariance matrix are analyzed. The proposed approach outperforms the traditional sparse smooth strategies in terms of suppressible interference number regardless of the number of physical array element. The effectiveness of the proposed method for interference suppression is verified through numerical simulations.