Abstract
As an important branch of array signal processing, robust adaptive beamforming (RAB) has been widely used in many fields. In the conventional robust adaptive beamformers, a common problem is that the estimated manifold vector of the signal of interest (SOI) may converge to the interference subspace. By using the data dependent constraints, such problem can be mitigated. In this paper, first we reconstruct the covariance matrices for the desired signal. Then a novel RAB is proposed in which the data dependent constraints are formed on the basis of the reconstructed signal covariance matrices. Moreover, the optimal solution to the proposed RAB can be found by the Lagrange multiplier methodology and the semi-definite generalized eigen- decomposition. Simulation results demonstrate the effectiveness of our proposed RAB.
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