Asymptotically optimal linear shrinkage of sample LMMSE and MVDR filters
Visualitza/Obre
Asymptotically Optimal Linear Shrinkage of Sample LMMSE and MVDR Filters (2,151Mb) (Accés restringit)
Sol·licita una còpia a l'autor
Què és aquest botó?
Aquest botó permet demanar una còpia d'un document restringit a l'autor. Es mostra quan:
- Disposem del correu electrònic de l'autor
- El document té una mida inferior a 20 Mb
- Es tracta d'un document d'accés restringit per decisió de l'autor o d'un document d'accés restringit per política de l'editorial
Cita com:
hdl:2117/24612
Tipus de documentArticle
Data publicació2014-07-15
Condicions d'accésAccés restringit per política de l'editorial
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Conventional implementations of the linearminimum mean-square (LMMSE) and minimum variance distortionless response (MVDR) estimators rely on the sample matrix inversion (SMI) technique, i.e., on the sample covariance matrix (SCM). This approach is optimal in the large sample size regime. Nonetheless, in small sample size situations, those sample estimators suffer a large performance degradation. Thus, the aim of this paper is to propose corrections of these sample methods that counteract their performance degradation in the small sample size regime and keep their optimality in large sample size situations. To this aim, a twofold approach is proposed. First, shrinkage estimators are considered, as they are known to be robust to the small sample size regime. Namely, the proposed methods are based on shrinking the sample LMMSE or sample MVDR filters towards a variously called matched filter or conventional (Bartlett) beamformer in array processing. Second, random matrix theory is used to obtain the optimal shrinkage factors for large filters. The simulation results highlight that the proposed methods outperform the sample LMMSE and MVDR. Also, provided that the sample size is higher than the observation dimension, they improve classical diagonal loading (DL) and Ledoit-Wolf (LW) techniques, which counteract the small sample size degradation by regularizing the SCM. Finally, compared to state-of-the-art DL, the proposed methods reduce the computational cost and the proposed shrinkage of the LMMSE obtains performance gains.
CitacióSerra, J.; Najar, M. Asymptotically optimal linear shrinkage of sample LMMSE and MVDR filters. "IEEE transactions on signal processing", 15 Juliol 2014, vol. 62, núm. 14, p. 3552-3564.
ISSN1053-587X
Versió de l'editorhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6827237
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
Asymptotically ... LMMSE and MVDR Filters.pdf | Asymptotically Optimal Linear Shrinkage of Sample LMMSE and MVDR Filters | 2,151Mb | Accés restringit |