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- Title
Low-complexity cross-validation design of a linear estimator.
- Authors
Tong, J.; Xi, J.; Guo, Q.; Yu, Y.
- 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-of-training data. Numerical results are provided to demonstrate the effectiveness of the proposed approaches.
- Subjects
ESTIMATION theory; SIGNAL processing; RADIO filters; SIGNAL theory; BEAMFORMING
- Publication
Electronics Letters (Wiley-Blackwell), 2017, Vol 53, Issue 18, p1252
- ISSN
0013-5194
- Publication type
Article
- DOI
10.1049/el.2017.2364