ON THE EQUIVALENCE OF A REDUCED-COMPLEXITY RECURSIVE POWER NORMALIZATION ALGORITHM AND THE EXPONENTIAL WINDOW POWER ESTIMATION

摘要

The transform-domain least-mean-square (TD-LMS) al- gorithm provides significantly faster convergence than the LMS algorithm for coloured input signals. However, a major disadvantage of the TD-LMS algorithm is the large computational complexity arising from the unitary transform and power normalization operations. In this paper we establish the equivalence of a recently pro- posed recursive power normalization algorithm and the traditional exponential window power estimation algo- rithm. The proposed algorithm is based on the matrix inversion lemma and is optimized for implementation on a digital signal processor (DSP). It reduces the num- ber of divisions from N to one for a TD-LMS adaptive filter with N coecients. This provides a significant reduction in computational complexity for DSP imple- mentations. The equivalence of the reduced-complexity algorithm and the exponential window power estimation algorithm is demonstrated in simulation examples.