Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/17225057-B514-43E1-B1C8-FB5718D80A07 | - |
| Identifier | http://www.telecom.tuc.gr/~karystinos/paper_TSP1.pdf | - |
| Identifier | https://doi.org/10.1109/78.902111 | - |
| Language | en | - |
| Extent | 10 | en |
| Title | An iterative algorithm for the computation of the MVDR filter | en |
| Creator | Pados Dimitris A. | en |
| Creator | Karystinos Georgios | en |
| Creator | Καρυστινος Γεωργιος | el |
| Publisher | Institute of Electrical and Electronics Engineers | en |
| Description | Δημοσίευση σε επιστημονικό περιοδικό | el |
| Content Summary | Statistical conditional optimization criteria lead to the development of an iterative algorithm that starts from the matched filter (or constraint vector) and generates a sequence of filters that converges to the minimum-variance-distortionless-response (MVDR) solution for any positive definite input autocorrelation matrix. Computationally, the algorithm is a simple, noninvasive, recursive procedure that avoids any form of explicit autocorrelation matrix inversion, decomposition, or diagonalization. Theoretical analysis reveals basic properties of the algorithm and establishes formal convergence. When the input autocorrelation matrix is replaced by a conventional sample-average (positive definite) estimate, the algorithm effectively generates a sequence of MVDR filter estimators; the bias converges rapidly to zero and the covariance trace rises slowly and asymptotically to the covariance trace of the familiar sample-matrix-inversion (SMI) estimator. In fact, formal convergence of the estimator sequence to the SMI estimate is established. However, for short data records, it is the early, nonasymptotic elements of the generated sequence of estimators that offer favorable bias covariance balance and are seen to outperform in mean-square estimation error, constraint-LMS, RLS-type, orthogonal multistage decomposition, as well as plain and diagonally loaded SMI estimates. An illustrative interference suppression example is followed throughout this presentation | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-10-23 | - |
| Date of Publication | 2001 | - |
| Subject | Adaptive filters | en |
| Subject | algorithms | en |
| Subject | code division multiaccess | en |
| Subject | estimation | en |
| Subject | interference suppression | en |
| Subject | iterative methods | en |
| Subject | least mean square methods | en |
| Bibliographic Citation | D. A. Pados and G. N. Karystinos, “An iterative algorithm for the computation of the MVDR filter,” IEEE Transactions on Signal Processing, vol. 49, no. 2, pp. 290-300, Feb. 2001. doi: 10.1109/78.902111 | en |
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