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This work presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. At first, we introduce an orthogonal Krylov subspace projection mismatch estimation (OKSPME) method, in which a general linear equation is considered in large dimensions which aims to solve for the steering vector mismatch with known information, then we employ the idea of the full orthogonalization...
This paper presents a low-complexity robust data-dependent dimensionality reduction based on a modified joint iterative optimization (MJIO) algorithm for reduced-rank beamforming and steering vector estimation. The proposed robust optimization procedure jointly adjusts the parameters of a rank-reduction matrix and an adaptive beamformer. The optimized rank-reduction matrix projects the received signal...
We develop an adaptive beamforming algorithm based on the worst-case criterion and the constrained constant modulus approach. Similarly to the existing worst-case optimization-based beamformer with the minimum variance design, the problem can be reformulated as a second-order cone (SOC) program and solved with interior point methods. We also show some relations which are useful for the choice of the...
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