Efficient Rank-Adaptive Least-Square Estimation and Multiple-Parameter Linear Regression Using Novel Dyadically Recursive Hermitian Matrix Inversion
Joint Authors
Chang, Shih Yu
Wu, Yiyan
Le-Ngoc, Tho
Wu, Hsiao-Chun
Source
International Journal of Antennas and Propagation
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-11-05
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Least-square estimation (LSE) and multiple-parameter linear regression (MLR) are the important estimation techniques for engineering and science, especially in the mobile communications and signal processing applications.
The majority of computational complexity incurred in LSE and MLR arises from a Hermitian matrix inversion.
In practice, the Yule-Walker equations are not valid, and hence the Levinson-Durbin algorithm cannot be employed for general LSE and MLR problems.
Therefore, the most efficient Hermitian matrix inversion method is based on the Cholesky factorization.
In this paper, we derive a new dyadic recursion algorithm for sequential rank-adaptive Hermitian matrix inversions.
In addition, we provide the theoretical computational complexity analyses to compare our new dyadic recursion scheme and the conventional Cholesky factorization.
We can design a variable model-order LSE (MLR) using this proposed dyadic recursion approach thereupon.
Through our complexity analyses and the Monte Carlo simulations, we show that our new dyadic recursion algorithm is more efficient than the conventional Cholesky factorization for the sequential rank-adaptive LSE (MLR) and the associated variable model-order LSE (MLR) can seek the trade-off between the targeted estimation performance and the required computational complexity.
Our proposed new scheme can benefit future portable and mobile signal processing or communications devices.
American Psychological Association (APA)
Wu, Hsiao-Chun& Chang, Shih Yu& Le-Ngoc, Tho& Wu, Yiyan. 2012. Efficient Rank-Adaptive Least-Square Estimation and Multiple-Parameter Linear Regression Using Novel Dyadically Recursive Hermitian Matrix Inversion. International Journal of Antennas and Propagation،Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-505887
Modern Language Association (MLA)
Wu, Hsiao-Chun…[et al.]. Efficient Rank-Adaptive Least-Square Estimation and Multiple-Parameter Linear Regression Using Novel Dyadically Recursive Hermitian Matrix Inversion. International Journal of Antennas and Propagation No. 2012 (2012), pp.1-10.
https://search.emarefa.net/detail/BIM-505887
American Medical Association (AMA)
Wu, Hsiao-Chun& Chang, Shih Yu& Le-Ngoc, Tho& Wu, Yiyan. Efficient Rank-Adaptive Least-Square Estimation and Multiple-Parameter Linear Regression Using Novel Dyadically Recursive Hermitian Matrix Inversion. International Journal of Antennas and Propagation. 2012. Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-505887
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-505887