Null broadening adaptive beamforming based on semidefinite programming

In this paper, we introduce a procedure for the null broadening algorithm design with respect to the perturbation of the interference location. This method is based on maximizing the array output signal-to-interference-plus-noise-ratio (SINR) subject to quadratic constraints. The design problem can be cast as a fractional quadratically constrained quadratic programming (QCQP) problem that can be solved efficiently using the semidefinite programming (SDP) techniques, the semidefinite relaxation can be used to obtain a lower bound on the optimal objective function. This proposed approach imposes broadened nulls towards the interference region while possesses well-maintained pattern. Theoretical analysis and numerical results demonstrate that the performance of proposed adaptive beamformer is almost always close to optimal.