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In this paper, we propose a new method for estimating the steering vector under uncertainties, which is utilized for improving the robustness of beamforming. We show that the desired steering vector can be estimated in closed form from a convex optimization problem by making use of the subspace principle. As this method is developed based on an extended version of the orthonormal PAST (OPAST), the...
This brief proposes a new transform-domain (TD) regularized M-estimation (TD-R-ME) algorithm for a robust linear estimation in an impulsive noise environment and develops an efficient QR-decomposition-based algorithm for recursive implementation. By formulating the robust regularized linear estimation in transformed regression coefficients, the proposed TD-R-ME algorithm was found to offer better...
The sequential partial update LMS (S-LMS)-based algorithms are efficient adaptive filtering algorithms for reducing the high arithmetic complexity in acoustic and related applications. A limitation of the algorithms is the degraded convergence speed. In this paper, a new family of sequential partial update switch-mode noise-constrained NLMS (S-SNC-NLMS) algorithms is proposed. These algorithms use...
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