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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Challa, D. Grant, S.L. Mohammad, A. |
| Copyright Year | 2007 |
| Description | Author affiliation: Missouri Univ., Rolla, MO, USA (Challa, D.; Grant, S.L.; Mohammad, A.) |
| Abstract | This paper introduces a variable regularization method for the fast affine projection algorithm (VR-FAP). It is inspired by a recently introduced technique for variable regularization of the classical, affine projection algorithm (VR-APA). In both algorithms, the regularization parameter varies as a function of the excitation, measurement noise, and residual error energies. Because of the dependence on the last parameter, VR-APA and VR-FAP demonstrate the desirable property of fast convergence (via a small regularization value) when the convergence is poor and deep convergence/immunity to measurement noise (via a large regularization value) when the convergence is good. While the regularization parameter of APA is explicitly available for on-line modification, FAP's regularization is only set at initialization. To overcome this problem we use noise-injection with the noise-power proportional to the variable regularization parameter. As with their fixed regularization versions, VR-FAP is considerably less complex than VR-APA and simulations verify that they have the very similar convergence properties. |
| File Size | 3785737 |
| File Format | |
| ISBN | 1424407273 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2007.366623 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Financial advantage program Convergence Projection algorithms Noise measurement Adaptive filters Computational complexity Covariance matrix Colored noise Virtual reality Energy measurement affine projections FAP APA regularization adaptive filter |
| Content Type | Text |
| Resource Type | Article |
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