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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hubscher, P.I. Bermudez, J.C.M. |
| Copyright Year | 2004 |
| Description | Author affiliation: Nat. Inst. for Space Res., INPE, Sao Jose Dos Campos, Brazil (Hubscher, P.I.) |
| Abstract | The paper presents an analysis of the kurtosis performance surface as applied to linear estimation. The analysis concentrates on a modified kurtosis (MK) function used in implementations of the least mean kurtosis (LMK) adaptive algorithm. The MK function is shown to make the LMK algorithm applicable even for Gaussian inputs. The minimum of the MK function is derived and shown to be unique and to correspond to the Wiener solution of the mean square error (MSE) estimation problem. A quantitative comparison of the MSE and MK functions explains why the LMK adaptive algorithm is faster than MSE-based algorithms during the initial learning phase, becoming slower as it approaches steady-state. |
| File Size | 285601 |
| File Format | |
| ISBN | 0780384849 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2004.1326388 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-05-17 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive filters Adaptive algorithm Algorithm design and analysis Cost function Signal processing algorithms Least squares approximation Estimation error Steady-state Performance analysis Mean square error methods |
| Content Type | Text |
| Resource Type | Article |
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