Least Mean, Square Adaptive Filter: A Uni ed Framework
نویسندگان
چکیده
Employing a recently introduced framework, within which a large number of classical and modern adaptive lter algorithms can be viewed as special cases, a generic, variable step-size adaptive lter has been presented. Variable Step-Size (VSS) Normalized Least Mean Square (VSSNLMS) and VSS A ne Projection Algorithms (VSSAPA) are particular examples of adaptive algorithms covered by this generic variable step-size adaptive lter. In this paper, the new VSS Block Normalized Least Mean Square (VSSBNLMS) adaptive lter algorithm is introduced, based on the generic VSS adaptive lter. The proposed algorithm shows the higher convergence rate and lower steady-state mean square error compared to the ordinary BNLMS algorithm.
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