Normalized Subband Adaptive Filtering Algorithm With Reduced Computational Complexity

Subband structures are suitable for improving convergence properties of adaptive filtering algorithms, particularly for colored input signals. This brief proposes a new subband adaptive algorithm with sparse adaptive subfilters, which employs the principle of minimal disturbance with multiple-constraint optimization. A performance analysis is carried out, resulting in an expression for the steady-state mean-square error. It is shown that the proposed algorithm, under some particular parameter choices, presents the same performance as that of the normalized subband adaptive filter, but with reduced computational complexity.