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In this study, we propose an adaptive set membership constant modulus (SM-CM) algorithm with a generalized sidelobe canceler (GSC) structure for blind beamforming. We develop a stochastic gradient (SG) type algorithm based on the concept of SM filtering to facilitate an adaptive implementation. The filter weights are updated only if the constraint cannot be satisfied. In addition, we also propose...
In this work, we propose an adaptive set-membership (SM) reduced-rank filtering algorithm using the constrained constant modulus (CCM) criterion for beamforming. We develop a stochastic gradient (SG) type algorithm based on the concept of SM technique for adaptive implementation. The filter weights are updated only if the bounded constraint cannot be satisfied. In addition, we also propose a scheme...
We introduce a new linearly constrained minimum variance (LCMV) beamformer that combines the set-membership (SM) technique with the conjugate gradient (CG) method, and develop a low-complexity adaptive filtering algorithm for beamforming. The proposed algorithm utilizes a CG-based vector and a variable forgetting factor to perform the data-selective updates that are controlled by a time-varying bound...
In this work, we present a novel adaptive filtering scheme that builds on and advances the method of joint iterative optimization (JIO) of reduced-rank filters proposed in [1]. The scheme applies the theory of error bounded set-membership filtering to both the adaptation of the bank of full-rank filters that form the projection matrix, and the reduced-rank adaptive filter that operates in the lower...
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