Cong Xiang

TL;DR: The proposed beamformer has lower computational complexity and faster convergence rate comparable with that of the traditional robust adaptive beamforming algorithms with full DoFs, while, at the same time, it provides better robustness to the non-ideal cases and reduces the training samples required.

TL;DR: It is shown that the proposed approach can significantly reduce the computational burden and training-data requirement of the MIMO radar space-time adaptive processing (STAP) based on reduced-dimension transformation.

TL;DR: In this paper, an airborne MIMO radar space-time dimension-reduction self-adaptive processing method based on three iterations is proposed, which solves the problems of numerous required training samples, high computation complexity and low convergence speed of a traditional space time dimension reduction technology.

TL;DR: A novel tri-iterative least-square (TI-LS) method for bearing estimation in multiple-input multiple-output (MIMO) radar system that iteratively finds two steering matrices associated with the transmit and receive arrays, and the target's bearing can be easily obtained based on prior knowledge of the transmitter and receive array manifolds.