Abstract
A new reweighted proportionate affine projection algorithm (RPAPA) with memory and row action projection (MRAP) is proposed in this paper. The reweighted PAPA is derived from a family of sparseness measures, which demonstrate performance similar to mu-law and the l0 norm PAPA but with lower computational complexity. The sparseness of the channel is taken into account to improve the performance for dispersive system identification. Meanwhile, the memory of the filter's coefficients is combined with row action projections (RAP) to significantly reduce computational complexity. Simulation results demonstrate that the proposed RPAPA MRAP algorithm outperforms both the affine projection algorithm (APA) and PAPA and has performance similar to l0 PAPA and mu-law PAPA, in terms of convergence speed and tracking ability. Meanwhile, the proposed RPAPA MRAP has much lower computational complexity than PAPA, mu-law PAPA, and l0 PAPA, etc., which makes it very appealing for real-time implementation.
Recommended Citation
J. Liu et al., "A Low Complexity Reweighted Proportionate Affine Projection Algorithm with Memory and Row Action Projection," Eurasip Journal on Advances in Signal Processing, vol. 2015, no. 1, pp. 1 - 12, article no. 99, SpringerOpen; European Association for Signal Processing, Dec 2015.
The definitive version is available at https://doi.org/10.1186/s13634-015-0280-4
Department(s)
Electrical and Computer Engineering
Publication Status
Open Access
Keywords and Phrases
Adaptive filter; Proportionate affine projection algorithm; Row action projection; Sparse system identification
International Standard Serial Number (ISSN)
1687-6180; 1687-6172
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Dec 2015
