Acoustic signal processing for telecommunication
Acoustic signal processing for telecommunication
Adaptive Filtering: Algorithms and Practical Implementation
Adaptive Filtering: Algorithms and Practical Implementation
ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
Complexity considerations for transform-domain adaptive filters
Signal Processing
Selective partial update and set-membership subband adaptive filters
Signal Processing
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in theory and methods for nonstationary signal analysis
A time-domain feedback analysis of filtered-error adaptive gradientalgorithms
IEEE Transactions on Signal Processing
Convergence analysis of the binormalized data-reusing LMS algorithm
IEEE Transactions on Signal Processing
Adaptive filtering in subbands using a weighted criterion
IEEE Transactions on Signal Processing
Complexity reduction of the NLMS algorithm via selectivecoefficient update
IEEE Transactions on Signal Processing
A unified approach to the steady-state and tracking analyses ofadaptive filters
IEEE Transactions on Signal Processing
Partial-update NLMS algorithms with data-selective updating
IEEE Transactions on Signal Processing
Comparison of RLS, LMS, and sign algorithms for tracking randomlytime-varying channels
IEEE Transactions on Signal Processing
A new approach to subband adaptive filtering
IEEE Transactions on Signal Processing
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, a unified approach to mean-square performance analysis of the family of selective partial update (SPU) adaptive filter algorithms in nonstationary environment is presented. Using this analysis, the tracking performance of Max normalized least mean squares (Max-NLMS), N-Max NLMS, the various types of SPU-NLMS algorithms, SPU transform domain LMS (SPU-TD-LMS), the family of SPU affine projection algorithms (SPU-APA), the family of selective regressor APA (SR-APA), the dynamic selection of APA (DS-APA), the family of SPU-SR-APA, the family of SPU-DS-APA, SPU subband adaptive filters (SPU-SAF), and the periodic, sequential, and stochastic partial update LMS, NLMS, and APA as well as classical adaptive filter algorithms can be analyzed with a unified approach. Two theoretical expressions are introduced to study the performance. The analysis is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. We demonstrate through simulations that the derived expressions are useful in predicting the performance of this family of adaptive filters in nonstationary environment.