EURASIP Journal on Audio, Speech, and Music Processing
Subband affine projection algorithm for acoustic echo cancellation system
EURASIP Journal on Applied Signal Processing
A unified framework for adaptive filter algorithms with variable step-size
Computers and Electrical Engineering
Robust Source Separation with Simple One-Source-Active Detection
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Derivation of Excess Mean-Square Error for Affine Projection Algorithms Using the Condition Number
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
IEEE Transactions on Signal Processing
An affine projection algorithm using the inner product of input vectors
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Distributed estimation over an adaptive incremental network based on the affine projection algorithm
IEEE Transactions on Signal Processing
A variable regularization method for affine projection algorithm
IEEE Transactions on Circuits and Systems II: Express Briefs
Mean-square convergence analysis of ADALINE training with minimum error entropy criterion
IEEE Transactions on Neural Networks
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in theory and methods for nonstationary signal analysis
Affine projection algorithm with selective projections
Signal Processing
An affine projection algorithm with variable step size and projection order
Digital Signal Processing
| Hi-index | 35.69 |
Affine projection algorithms are useful adaptive filters whose main purpose is to speed the convergence of LMS-type filters. Most analytical results on affine projection algorithms assume special regression models or Gaussian regression data. The available analysis also treat different affine projection filters separately. This paper provides a unified treatment of the mean-square error, tracking, and transient performances of a family of affine projection algorithms. The treatment relies on energy conservation arguments and does not restrict the regressors to specific models or to a Gaussian distribution. Simulation results illustrate the analysis and the derived performance expressions.