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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
