A justification for the improved performance of the multi-split LMS algorithm

This paper presents an analysis that justifies the improved performance of the multi-split LMS algorithm. It is shown that instead of reducing the eigenvalue ratio, the multi-split operation increases the diagonalization factor of the transformed input signal autocorrelation matrix, which assists th...

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Bibliographic Details
Main Authors: Souza, R.D., Resende, L.S., Bellanger, M.G.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Autocorrelation
Computational complexity
Data preprocessing
Discrete cosine transforms
Eigenvalues and eigenfunctions
Finite impulse response filter
Least squares approximation
Performance evaluation
Robustness
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Summary:This paper presents an analysis that justifies the improved performance of the multi-split LMS algorithm. It is shown that instead of reducing the eigenvalue ratio, the multi-split operation increases the diagonalization factor of the transformed input signal autocorrelation matrix, which assists the power normalized and time-varying step-size LMS algorithm used for updating the single parameters independently. Case studies and simulation results enable us to evaluate the improved performance of the multi-split LMS algorithm.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2003.1201625