Hui Lv

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: A new bi-quadratic cost function is introduced, whose minimum point can be efficiently found by a bi-iterative algorithm (BIA) and the convergence of the BIA is analyzed by the well-known LaSalle invariance principle.

TL;DR: Simulation results show that the output SINR of MIMO-STAP can be improved considerably by the proposed method, as compared to that of uncorrelated waveforms.

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: A novel constrained biased Cramer-Rao bound (CRB) based method is proposed to optimize the waveform covariance matrix and biased estimator and shows that the performance of parameter estimation can be improved considerably by the proposed method compared to uncorrelated waveforms.