Adaptive signal processing
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Acoustic signal processing for telecommunication
Acoustic signal processing for telecommunication
Adaptive Filtering: Algorithms and Practical Implementation
Adaptive Filtering: Algorithms and Practical Implementation
Error Correction Coding: Mathematical Methods and Algorithms
Error Correction Coding: Mathematical Methods and Algorithms
A time-domain feedback analysis of filtered-error adaptive gradientalgorithms
IEEE Transactions on Signal Processing
A feedback approach to the steady-state performance of fractionallyspaced blind adaptive equalizers
IEEE Transactions on Signal Processing
The behavior of LMS and NLMS algorithms in the presence ofspherically invariant processes
IEEE Transactions on Signal Processing
Convergence analysis of the binormalized data-reusing LMS algorithm
IEEE Transactions on Signal Processing
On the convergence behavior of the LMS and the normalized LMSalgorithms
IEEE Transactions on Signal Processing
A unified approach to the steady-state and tracking analyses ofadaptive filters
IEEE Transactions on Signal Processing
Transient analysis of data-normalized adaptive filters
IEEE Transactions on Signal Processing
Mean-square performance of a family of affine projection algorithms
IEEE Transactions on Signal Processing
Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
Employing a recently introduced unified adaptive filter theory, we show how the performance of a large number of important adaptive filter algorithms can be predicted within a unified way. This approach is based on energy conservation arguments and does not need to assume the specific models for the regressors. This general performance analysis can be used to evaluate the mean square and tracking performance of the least mean square (LMS) algorithm, its normalized version (NLMS), the family of affine projection algorithms (APA), the recursive least squares (RLS), the data-reusing LMS (DR-LMS), its normalized version (NDR-LMS), and the transform domain adaptive filters (TDAF). Also, we establish the general expressions for the excess mean square in the stationary and nonstationary environments for all these adaptive algorithms. Finally, we demonstrate through simulations that these results are useful in predicting the adaptive filter performance.