Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The mismatches of signal and array geometry will seriously degrade the performance of adaptive beamformer. ,  propose two methods for robust adaptive beamforming based on the conjugate gradient (CG) algorithm. The proposed beamformers offer a significant improvement in the computational complexity while providing the same performance of the best robust beamformers at present. The first method belongs to the diagonal loading technique.  derive a diagonal loading CGLS algorithm (CG applied to normal equations) and propose a simple method to choose the loading level based on a coarse estimation of the desired signal por. This parameter-free method can effectively reduce the signal self-cancellation at high signal-to-noise ratio. The second method belongs to the regularization technique. Since the CG algorithm has a regularizing effect with iteration number being the regularization parameter, the stopping criterion plays an important role on the robustness.  develop three fast stopping criteria for CG iteration, which reduce the stopping complexity from   $O(N)$    or   $O(N^2)$   to    $O(1)$  . The former two are the fast versions of existing methods and the later one is new. Moreover, the new criterion based on fast Ritz value estimation has better performance than others.
['Ming Zhang', 'Anxue Zhang', 'Qingqing Yang']
I am writing a research paper on a idea Robust Adaptive Beamforming Based on Conjugate Gradient Algorithms
[{"from": "human", "value": "I am writing a research paper on a idea Robust Adaptive Beamforming Based on Conjugate Gradient Algorithms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The mismatches of signal and array geometry will seriously degrade the performance of adaptive beamformer. ,  propose two methods for robust adaptive beamforming based on the conjugate gradient (CG) algorithm. The proposed beamformers offer a significant improvement in the computational complexity while providing the same performance of the best robust beamformers at present. The first method belongs to the diagonal loading technique.  derive a diagonal loading CGLS algorithm (CG applied to normal equations) and propose a simple method to choose the loading level based on a coarse estimation of the desired signal por. This parameter-free method can effectively reduce the signal self-cancellation at high signal-to-noise ratio. The second method belongs to the regularization technique. Since the CG algorithm has a regularizing effect with iteration number being the regularization parameter, the stopping criterion plays an important role on the robustness.  develop three fast stopping criteria for CG iteration, which reduce the stopping complexity from   $O(N)$    or   $O(N^2)$   to    $O(1)$  . The former two are the fast versions of existing methods and the later one is new. Moreover, the new criterion based on fast Ritz value estimation has better performance than others."}]