893 research outputs found
Minimum Sensitivity Based Robust Beamforming with Eigenspace Decomposition
An enhanced eigenspace-based beamformer (ESB) derived using the
minimum sensitivity criterion is proposed with significantly improved robustness
against steering vector errors. The sensitivity function is defined as the squared
norm of the appropriately scaled weight vector and since the sensitivity function
of an array to perturbations becomes very large in the presence of steering vector
errors, it can be used to find the best projection for the ESB, irrespective of the
distribution of additive noises. As demonstrated by simulation results, the proposed
method has a better performance than the classic ESBs and the previously
proposed uncertainty set based approach
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