|تعداد مشاهده مقاله||2,478,836|
|تعداد دریافت فایل اصل مقاله||1,746,997|
|Journal of Electrical and Computer Engineering Innovations (JECEI)|
|مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 19 مهر 1402 اصل مقاله (1.56 M)|
|نوع مقاله: Original Research Paper|
|شناسه دیجیتال (DOI): 10.22061/jecei.2023.10062.675|
|E. Heydari1؛ M. Shams Esfand Abadi* 1؛ S.M. Khademiyan2|
|1Electrical Engineering Department, Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.|
|2Department of Mathematics, Faculty of Science, Shahid Rajaee Teacher Training University, Tehran, Iran|
|تاریخ دریافت: 31 تیر 1402، تاریخ بازنگری: 25 شهریور 1402، تاریخ پذیرش: 19 مهر 1402|
|Background and Objectives: In order to improve the performance of normalized subband adaptive filter algorithm (NSAF) for identifying the block-sparse (BS) systems, this paper introduces the novel adaptive algorithm which is called BSNSAF. In the following, an improved multiband structured subband adaptive filter (IMSAF) algorithms for BS system identification is also proposed. The BS-IMSAF has faster convergence speed than BS-NSAF. Since the computational complexity of BS-IMSAF is high, the selective regressor (SR) and dynamic selection (DS) approaches are utilized and BS-SR-IMSAF and BS-DS-IMSAF are introduced. Furthermore, the theoretical steady-state performance analysis of the presented algorithms is studied.|
Methods: All algorithms are established based on the 𝐿2,0-norm constraint to the proposed cost function and the method of Lagrange multipliers is used to optimize the cost function.
Results: The good performance of the proposed algorithms is demonstrated through several simulation results in the system identification setup. The algorithms are justified and compared in various scenarios and optimum values of the parameters are obtained. Also, the computational complexity of different algorithms are studied. In addition, the theoretical steady state values of mean square error (MSE) values are compared with simulation values.
Conclusion: The BS-NSAF algorithm has better performance than NSAF for BS system identification. The BSIMSAF algorithm has better convergence speed than BS-NSAF. To reduce the computational complexity, the BS-SR-IMSAF and BS-DSR-IMSAF
|SparseL_2,0-norm IMSAF, Selective Regressors؛ Dynamic Selection|
 B. Widrow, S. D. Stearns, Adaptive Signal Processing, Englewood Cliffs, NJ: Prentice-Hall, 1985.
 J. R. Treichler, C. R. Johnson, M. G. Larimore, Theory and Design of Adaptive Filters, Wiley, 1987.
 K. Ozeki, T. Umeda, “An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties, Electron. Commun. Jpn., 67-A: 19–27, 1984.
 K. A. Lee, W. S. Gan, “Improving convergence of the NLMS algorithm using constrained subband updates,” IEEE Signal Process. Lett., 11 (9): 736–739, 2004.
 F. Yang, M. Wu, P. Ji, J. Yang, “An improved multiband-structured subband adaptive filter algorithm,” IEEE Signal Process. Lett., 19: 647–650, 2012.
 F. Yang, M. Wu, P. Ji, J. Yang, “Low-complexity implementation of the improved multiband-structured subband adaptive filter algorithm,” IEEE Trans. Signal Process., 63: 5133–5148, 2015.
 K. Y. Hwang, W. J. Song, “An affine projection adaptive filtering algorithm with selective regressors,” IEEE Trans. Circuits Syst. II Express Briefs, 54(1): 43–46, 2007.
 S. J. Kong, K. Y. Hwang, W. J. Song, “An affine projection algorithm with dynamic selection of input vectors,” IEEE Signal Process. Lett., 14(8): 529–532, 2007.
 M. S. E. Abadi, J. H. Husoy, M. J. Ahmadi, “Two improved multiband structured subband adaptive filter algorithms with reduced computational complexity,” Signal Process., 154: 15–29, 2019.
 M. S. E. Abadi, M. J. Ahmadi, “Weighted improved multiband-structured subband adaptive filter algorithms,” IEEE Trans. Circuits Syst. II Express Briefs, 2019.
 M. S. E. Abadi, M. J. Ahmadi, “Diffusion improved multiband-structured subband adaptive filter algorithm with dynamic selection of nodes over distributed networks,” IEEE Trans. Circuits Syst. II Express Briefs, 66(3): 507–511, 2018.
 M. S. E. Abadi, H. Mesgarani, S. M. Khademiyan, “The wavelet transform-domain LMS adaptive filter employing dynamic selection of subband-coefficients,” Digital Signal Process., 69: 94–105, 2017.
 D. L. Duttweiler, “Proportionate normalized least-meam-squares adaptation in echo cancellers,” IEEE Trans. Speech Audio Process., 8(5): 508–518, 2000.
 A. Steingass, A. Lehner, F. Perez-Fontan, E. Kubista, B. Arbesser-Rastburg, “Characterization of the aeronautical satellite navigation channel through high-resolution measurement anf physical optics simulation,” Int. J. Satell. Commun. Netw., 269: 1–305, 2008.
 Y. Gu, J. Jin, S. Mei, “ norm constraint LMS algorithm for sparse system identification,” IEEE Signal Process. Lett., 16(9): 774–777, 2009.
 Y. Yu, H. Zhao, B. Chen, “Sparse normalized subband adaptive filter algorithm with -norm constraint,” J. Franklin Inst., 353(18): 5121–5136, 2016.
 M. Lima, W. Martins, P. S. R. Diniz, “Affine projection algorithms for sparse system identification,” in Proc. ICASSP: 5666–5670, 2013.
 M. Lima, T. Ferreira, W. Martins, P. S. R. Diniz, “Sparsity-aware data-selective adaptive filters,” IEEE Trans. Signal Process., 62 (17): 4557–4572, 2014.
 L. Ji, J. NiK., “Sparsity-aware normalized subband adaptive filters with jointly optimized parameters,” J. Franklin Instit., 357(17): 13144–13157, 2020.
 Y. Yu, T. Yang, H. Chen, R. Lamare, Y. Li, “Sparsity-aware SSAF algorithm with individual weighting factors: Performance analysis and improvements in acoustic echo cancellation,” Signal Process., 178(1): 1–16, 2021.
 Y. Yu, H. Zho, R. Lamare, L. Lu, “Sparsity-aware subband adaptive algorithms with adjustable penalties,” Digital Signal Process., 84(1): 93–106, 2019.
 Z. Habibi, H. Zayyani, M. S. E. Abadi, “A robust subband adaptive filter algorithm for sparse and block-sparse systems identification,” J. Syst. Eng. Electron., 32(2): 487–497, 2021.
 E. Heydari, M. S. E. Abadi, S. M. Khademiyan, “Improved multiband structured subband adaptive filter algorithm with -norm regularization for sparse system identification,” Digital Signal Process., 122(4): 1–14, 2022.
 S. Jiang, Y. Gu, “Block-sparsity-induced adaptive filter for multi-clustering system identification,” IEEE Trans. Signal Process., 63(20): 5318–5330, 2015.
 J. Liu, S. L. Grant, “Proportionate adaptive filtering for block-sparse system identification,” IEEE/ACM Trans. Audio Speech Lang. Process., 24(4): 623–529, 2016.
 Z. Zhang, H. Zhao, “Affine projection M-estimate subband adaptive filters for robust adaptive filtering in impulsive noise,” Signal Process., 120 (3): 64–70, 2016.
 H. C. Shin, A. H. Sayed, “Mean-Square performance of a family of affine projection algorithms,” IEEE Trans. Signal Process., 52(1): 90–102, 2004.
تعداد مشاهده مقاله: 63
تعداد دریافت فایل اصل مقاله: 1