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Efficient input-selective affine projection sign algorithm with enhanced convergence performance
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DOI：10.1016/j.sigpro.2026.110599.png)
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Abstract
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This study introduces an enhanced affine projection sign algorithm that incorporates an input vector selection mechanism to achieve faster convergence. The proposed scheme selectively determines the contribution of each input vector based on a dual-threshold strategy derived from M-estimation and mean square deviation analysis. An efficient recursive formulation is further adopted to reduce computational effort without sacrificing accuracy. Simulation results demonstrate that the proposed approach achieves faster convergence and improved robustness compared to existing methods under impulsive noise environments.
Keywords:
Affine projection sign algorithm
Mean-square deviation
M-estimate
Input vector selection
Impulsive noise
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