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Low-complexity cross-validation design of a linear estimator

Journal Article


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Abstract


  • Linear signal estimators have extensive applications. Under the minimum mean squared error (MMSE) criterion, the linear MMSE (LMMSE) estimator is optimal but requires knowledge of the covariance matrices. The sample matched filter generally performs worse but requires less a priori knowledge. A composite estimator that combines the sample LMMSE estimator and matched filter is studied, which may lead to noticeable improvements in performance. It is shown that such a gain can be achieved by low-complexity parameter tuning methods based on cross-validation using training or out-oftraining data. Numerical results are provided to demonstrate the effectiveness of the proposed approaches.

Publication Date


  • 2017

Citation


  • J. Tong, J. Xi, Q. Guo & Y. Yu, "Low-complexity cross-validation design of a linear estimator," Electronics Letters, vol. 53, (18) pp. 1252-1254, 2017.

Scopus Eid


  • 2-s2.0-85029215948

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1739&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/738

Has Global Citation Frequency


Number Of Pages


  • 2

Start Page


  • 1252

End Page


  • 1254

Volume


  • 53

Issue


  • 18

Place Of Publication


  • United Kingdom

Abstract


  • Linear signal estimators have extensive applications. Under the minimum mean squared error (MMSE) criterion, the linear MMSE (LMMSE) estimator is optimal but requires knowledge of the covariance matrices. The sample matched filter generally performs worse but requires less a priori knowledge. A composite estimator that combines the sample LMMSE estimator and matched filter is studied, which may lead to noticeable improvements in performance. It is shown that such a gain can be achieved by low-complexity parameter tuning methods based on cross-validation using training or out-oftraining data. Numerical results are provided to demonstrate the effectiveness of the proposed approaches.

Publication Date


  • 2017

Citation


  • J. Tong, J. Xi, Q. Guo & Y. Yu, "Low-complexity cross-validation design of a linear estimator," Electronics Letters, vol. 53, (18) pp. 1252-1254, 2017.

Scopus Eid


  • 2-s2.0-85029215948

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1739&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/738

Has Global Citation Frequency


Number Of Pages


  • 2

Start Page


  • 1252

End Page


  • 1254

Volume


  • 53

Issue


  • 18

Place Of Publication


  • United Kingdom