Set-membership constrained widely linear beamforming algorithms

This paper proposes a widely linear (WL) constrained minimum variance beamforming algorithms based on the set-membership filtering (SMF) framework. The proposed SMF-WL algorithms have the advantages of the widely linear processing concept and keep the computational cost low with the SMF technique. We present two versions: one using least-mean square (LMS) and another using recursive least squares (RLS) recursions. It is shown that the proposed algorithms have better steady state and convergence performances with a lower computational cost when compared with existing methods.