{
"title": "Mean-Square Performance of Adaptive Filter Algorithms in Nonstationary Environments",
"authors": "Mohammad Shams Esfand Abadi, John Hakon Hus\u00f8y",
"country": null,
"institution": null,
"volume": "23",
"journal": "International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering",
"pagesStart": 2586,
"pagesEnd": 2593,
"ISSN": "1307-6892",
"URL": "http:\/\/waset.org\/publications\/9212",
"abstract": "Employing a recently introduced unified adaptive filter\r\ntheory, we show how the performance of a large number of important\r\nadaptive filter algorithms can be predicted within a general framework\r\nin nonstationary environment. This approach is based on energy conservation\r\narguments and does not need to assume a Gaussian or white\r\ndistribution for the regressors. This general performance analysis can\r\nbe used to evaluate the mean square performance of the Least Mean\r\nSquare (LMS) algorithm, its normalized version (NLMS), the family\r\nof Affine Projection Algorithms (APA), the Recursive Least Squares\r\n(RLS), the Data-Reusing LMS (DR-LMS), its normalized version\r\n(NDR-LMS), the Block Least Mean Squares (BLMS), the Block\r\nNormalized LMS (BNLMS), the Transform Domain Adaptive Filters\r\n(TDAF) and the Subband Adaptive Filters (SAF) in nonstationary\r\nenvironment. Also, we establish the general expressions for the\r\nsteady-state excess mean square in this environment for all these\r\nadaptive algorithms. Finally, we demonstrate through simulations that\r\nthese results are useful in predicting the adaptive filter performance.",
"references": null,
"publisher": "World Academy of Science, Engineering and Technology",
"index": "International Science Index 23, 2008"
}