Kronecker robust adaptive beamformer for large array
To solve the problems of the requirement of a large number of samples and high computational complexity for large array,a Kronecker robust adaptive beamformer is proposed in this paper.Firstly,the steering vector of the desired signal is decomposed into the Kronecker product of two low-dimension steering vectors,and the original steering vector mismatch problem is transformed into the corresponding two low-dimension steering vectors mismatch problem.Secondly,the bi-quadratic cost function is established based on the worst-case performance optimization principle,which is then solved by using the bi-iterative algorithm(BIA).Only two low-dimension second-order cone programming(SOCP)problems need to be solved in per iteration.Theoretic analysis and simulations results show that compared with the conventional full-dimension robust algorithms,the samples required and computational complexity are reduced efficiently in the proposed approach.In addition,the higher output signal to interference plus noise ratio(SINR)is obtained for the higher degrees of freedom(DoFs)compared with the existing reduced-dimension robust algorithms.
large arrayKronecker productreduced-dimension robust beamformerbi-iterative algorithm(BIA)second-order cone programming(SOCP)
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