Improved double constraint robust capon beamforming algorithm

Traditional double constraint robust Capon beamforming algorithm uses Newton iterative method for solving the optimal loading, presenting the problems of low accuracy and large amount of computation. An improved Double Constraint Robust Capon Beamforming (DCRCB) algorithm is proposed in this paper. The algorithm reconstructs the signal convariance matrix, and by optimizing the projection of signal steering vector onto the noise subspace, it projects the reconstructed interference-plus-noise convariance matrix onto the noise subspace, obtaining the double constraint algorithm model based on the noise subspace. For the norm constraint is accessorial, the algorithm model can be converted into a single constraint issue and be solved into an analytical expression of optimal diagonal loading finally. Simulation results show that the improved algorithm can optimize the side lobe by adjusting the beam width of the main lobe, improve effectively the anti-vector error robustness, and reduce the amount of computation.