Robust adaptive beamforming for MIMO radar

In this paper, a new robust adaptive beamforming method is developed for MIMO radar in the presence of unknown mismatches of the desired signal steering vectors. Explicit models of uncertainties in both transmitted and received signal steering vectors are considered. It is shown that the robust adaptive beamforming problem for MIMO radar can be solved by minimizing a convex quadratic cost function based on the optimization of worst-case performance when full DOFs of MIMO radar are used. Whereas, we reformulate the quadratic cost function into a bi-quadratic cost function by adopting a separable form for the weight vector and the minimum point of the new cost function can be efficiently found by combining bi-iterative algorithm (BIA) with second-order cone programming (SOCP). 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. Numerical experiments with the frequently encountered types of signal steering vector mismatches are provided to demonstrate the effectiveness of the proposed robust adaptive beamforming algorithm as compared with the other robust adaptive beamforming algorithms.