By Topic

Levinson-like and Schur-like fast algorithms for solving block-slanted Toeplitz systems of equations arising in wavelet-based solution of integral equations

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Joshi, R.R. ; Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA ; Yagle, A.E.

The Wiener-Hopf integral equation of linear least-squares estimation of a wide-sense stationary random process and the Krein integral equation of one-dimensional (1-D) inverse scattering are Fredholm equations with symmetric Toeplitz kernels. They are transformed using a wavelet-based Galerkin method into a symmetric “block-slanted Toeplitz (BST)” system of equations. Levinson-like and Schur-like fast algorithms are developed for solving the symmetric BST system of equations. The significance of these algorithms is as follows. If the kernel of the integral equation is not a Calderon-Zygmund operator, the wavelet transform may not sparsify it. The kernel of the Krein and Wiener-Hopf integral equations does not, in general, satisfy the Calderon-Zygmund conditions. As a result, application of the wavelet transform to the integral equation does not yield a sparse system matrix. There is, therefore, a need for fast algorithms that directly exploit the (symmetric block-slanted Toeplitz) structure of the system matrix and do not rely on sparsity. The first such O(n2) algorithms, viz., a Levinson-like algorithm and a Schur (1917) like algorithm, are presented. These algorithms are also applied to the factorization of the BST system matrix. The Levinson-like algorithm also yields a test for positive definiteness of the BST system matrix. The results obtained are directly applicable to the problem of constrained deconvolution of a nonstationary signal, where the locations of the smooth regions of the signal being deconvolved are known a priori

Published in:

Signal Processing, IEEE Transactions on  (Volume:46 ,  Issue: 7 )