Null Broadening and Side lobe Control Algorithm via Multi-Parametric Quadratic Programming for Robust Adaptive Beamforming

摘要

Adaptive beamforming algorithm can automatically optimize the array pattern by adjusting the elemental control weights until a prescribed objective function is satisfied. Unfortunately, it is possible that the mismatch occurs between adaptive weights and data, due to the perturbation of the interference location when the antenna platform vibrates or interference moves quickly. Besides, the traditional beamformers may have unacceptably high sidelobes when few samples are available. To solve these problems, an effective robust adaptive beamforming method is presented. In the proposed method, firstly, a tapered covariance matrix is constructed to broaden the width of nulls for interference signal sources. Secondly, multiple additional quadratic inequality constraints outside the mainlobe beampattern area are used to guarantee that the sidelobe level is strictly lower than the prescribed threshold value. Finally, the beamforming optimization problem is formulated as a multi-parametric quadratic programming problem, such that the optimal weight vector can be easily obtained by real-valued computation. Simulation results are shown to demonstrate the efficiency of the proposed approach.