Article

Optimum block adaptive filtering algorithms using the preconditioning technique

Dept. of Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Seoul
IEEE Transactions on Signal Processing (Impact Factor: 2.81). 04/1997; DOI: 10.1109/78.558502
Source: IEEE Xplore

ABSTRACT We propose three block adaptive algorithms using the
preconditioning technique. The Toeplitz-preconditioned optimum block
adaptive (TOBA) algorithm employs a preconditioner assumed to be
Toeplitz, the symmetric successive overrelaxation (SSOR)-preconditioned
optimum block adaptive (SOBA) algorithm uses a product of triangular
matrices as a preconditioner, and the circulant-preconditioned OBA
(COBA) algorithm is based on a circulant preconditioner. It is also
shown that their tracking properties and convergence rates are superior
to those of the OBA algorithm, the self-orthogonizing block adaptive
filter (SOBAF), and the normalized frequency-domain OBA (NFOBA)
algorithm

0 Bookmarks
 · 
38 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: A unified view of algorithms for adaptive transversal FIR filtering and system identification has been presented. Wiener filtering and stochastic approximation are the origins from which all the algorithms have been derived, via a suitable choice of iterative optimization schemes and appropriate design parameters. Following this philosophy, the LMS algorithm and its offspring have been presented and interpreted as stochastic approximations of iterative deterministic steepest descent optimization schemes. On the other hand, the RLS and the quasi-RLS algorithms, like the quasi-Newton, the FNTN, and the affine projection algorithm, have been derived as stochastic approximations of iterative deterministic Newton and quasi-Newton methods. Fast implementations of these methods have been discussed. Block-adaptive, and block-exact adaptive filtering have also been considered. The performance of the adaptive algorithms has been demonstrated by computer simulations
    IEEE Signal Processing Magazine 08/1999; · 3.37 Impact Factor