دانشگاه تربیت دبیر شهید رجائی
انتشارات دانشگاه تربیت دبیر شهید رجائی

  اخبار و اعلانات

 آمار

تعداد نشریات11
تعداد شماره‌ها210
تعداد مقالات2,098
تعداد مشاهده مقاله2,877,857
تعداد دریافت فایل اصل مقاله2,085,740
Habibi, Z., Zayyani, H., Shams Esfandabadi, M.. (1399). Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification. فناوری آموزش, 9(1), 115-126. doi: 10.22061/jecei.2020.7525.401
Z. Habibi; H. Zayyani; M. Shams Esfandabadi. "Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification". فناوری آموزش, 9, 1, 1399, 115-126. doi: 10.22061/jecei.2020.7525.401
Habibi, Z., Zayyani, H., Shams Esfandabadi, M.. (1399). 'Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification', فناوری آموزش, 9(1), pp. 115-126. doi: 10.22061/jecei.2020.7525.401
Habibi, Z., Zayyani, H., Shams Esfandabadi, M.. Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification. فناوری آموزش, 1399; 9(1): 115-126. doi: 10.22061/jecei.2020.7525.401

Stochastic Block NIHT Algorithm for Adaptive Block-Sparse System Identification

Journal of Electrical and Computer Engineering Innovations (JECEI)
دوره 9، شماره 1، فروردین 2021، صفحه 115-126    اصل مقاله (1.98 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2020.7525.401
نویسندگان
Z. Habibi* 1؛ H. Zayyani2؛ M. Shams Esfandabadi3
1Research Institute for Information and Communications Technologies, Academic Center for Education, Culture and Research, Tehran, Iran.
2Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran.
3Electronics Engineering Department, Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
تاریخ دریافت: 01 اردیبهشت 1399،  تاریخ بازنگری: 03 شهریور 1399،  تاریخ پذیرش: 04 آذر 1399 
چکیده
Background and Objectives: Compressive sensing (CS) theory has been widely used in various fields, such as wireless communications. One of the main issues in the wireless communication field in recent years is how to identify block-sparse systems. We can follow this issue, by using CS theory and block-sparse signal recovery algorithms.
Methods: This paper presents a new block-sparse signal recovery algorithm for the adaptive block-sparse system identification scenario, named stochastic block normalized iterative hard thresholding (SBNIHT) algorithm. The proposed algorithm is a new block version of the SSR normalized iterative hard thresholding (NIHT) algorithm with an adaptive filter framework. It uses a search method to identify the blocks of the impulse response of the unknown block-sparse system that we wish to estimate. In addition, the necessary condition to guarantee the convergence for this algorithm is derived in this paper.
Results: Simulation results show that the proposed SBNIHT algorithm has a better performance than other algorithms in the literature with respect to the convergence and tracking capability.
Conclusion: In this study, one new greedy algorithm is suggested for the block-sparse system identification scenario. Although the proposed SBNIHT algorithm is more complex than other competing algorithms but has better convergence and tracking capability performance.
کلیدواژه‌ها
Compressive Sensing (CS)؛ System Identification؛ Block-sparse System؛ Adaptive Filter
مراجع

[1] R.G. Baraniuk, “More is less: signal processing and the data deluge,” Science, 331(6018): 717-719, 2011.

[2] Z. Gao, L. Dai, Z. Lu, C. Yuen, Z. Wang, “Super-resolution sparse MIMO-OFDM channel estimation based on spatial and temporal correlations,” IEEE Commun. Lett., 18(7): 1266-1269, 2014.‏

[3] Z. Zhang, Z. Pi, B. Liu, “TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise,” IEEE Trans. Biomed. Eng., 62(2): 522-531, 2014.‏

[4] Y.C. Eldar, P. Kuppinger, H. Bolcskei, “Block-sparse signals: Uncertainty relations and efficient recovery,” IEEE Trans. Signal Process., 58(6): 3042-3054, 2010.‏

[5] R.G. Baraniuk, V. Cevher, M.F. Duarte, C. Hegde, “Model-based compressive sensing,” IEEE Trans. Inf. Theory, 56(4): 1982-2001, 2010.‏

[6] D. Zhang, Y. Zhang, G. Zheng, C. Feng, J.  Tang, “Fast super-resolution estimation of DOA and DOD in bistatic MIMO radar with off-grid targets,” Int. J. Electron., 105(5): 866-881, 2018.‏

[7] D.L. Donoho, Y. Tsaig, I. Drori, J.L. Starck, “Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit,” IEEE Trans. Inf. Theory, 58(2): 1094-1121, 2012.‏

[8] B.X. Huang, T. Zhou, “Recovery of block sparse signals by a block version of StOMP,” Signal Process., 106: 231-244, 2015.‏

[9] M. Stojnic, F. Parvaresh, B. Hassibi, “On the reconstruction of block-sparse signals with an optimal number of measurements,” IEEE Trans. Signal Process., 57(8): 3075-3085, 2009.‏

[10] J. Jin, Y. Gu, S. Mei, “A stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework,”IEEE J. Sel. Top. Signal Process., 4(2): 409-420, 2010.‏

[11] J. Ren, C. Wei, L. Yu, H.  Zhang, H. Sun, “Dynamic recovery for block sparse signals,” Signal Process., 130: 197-203, 2017.‏

[12] Y. Jiao, B. Jin, X. Lu, “Group Sparse Recovery via the l0 (l2) Penalty: Theory and Algorithm,” IEEE Trans. Signal Process., 65(4): 998-1012, 2016.‏

[13] X. Zhang, W. Xu, J. Lin, and Y. Dang, “Block normalised iterative hard thresholding algorithm for compressed sensing,” Electron. Lett, 55(17): 957-959, 2019.‏

[14] S. He, Y. Lin, Y, “Cauchy distribution function-penalized LMS for sparse system identification,” Circ. Syst. Signal Process., 38(1): 470-480, 2019.‏

[15] S. Jiang, Y. Gu, “Block-sparsity-induced adaptive filter for multi-clustering system identification,” IEEE Trans. Signal Process., 63(20): 5318-5330, 2015.‏

[16] J. Liu, J. Jin, Y. Gu, “Efficient recovery of block sparse signals via zero-point attracting projection,” in Proc. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): 3333-3336, 2012.

[17] C. Ye, G. Gui, S.Y. Matsushita, L. Xu, “Block sparse signal reconstruction using block-sparse adaptive filtering algorithms,” J. Adv. Comput. Intell. Intell. Informat., 20(7): 1119-1126, 2016.‏

[18] D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, 52: 1289-1306, 2006.‏

[19] T.S. Alexander, “Adaptive signal processing: theory and applications,” Springer Science & Business Media, 2012.‏

[20] S. Foucart, “Hard thresholding pursuit: an algorithm for compressive sensing,” SIAM J. Numer. Anal., 49(6): 2543-2563, 2011.‏

[21] T. Blumensath, M.E. Davies, “Normalized iterative hard thresholding: Guaranteed stability and performance,” IEEE J. Sel. Top. Signal Process., 4(2): 298-309, 2010.‏

[22] J. Liu, S.L. Grant, “Proportionate adaptive filtering for block-sparse system identification,” IEEE/ACM Trans. Audio Speech Lang. Process., 24(4): 623-630, 2015.‏

[23] Z. Jin, X. Ding, Z.Jiang, Y. Li, “ An Improved μ-law Proportionate NLMS Algorithm for Estimating Block-Sparse Systems,” in Proc.  2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT): 205-209, 2019.

[24] Z. Jin, Y. Li, Z. Jiang, “ A Block-sparse Set-Membership PNLMS Algorithm Based    on Hybrid Zero-Attraction Penalty,” in Proc. 2019 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC): 216-219, 2019.

آمار
تعداد مشاهده مقاله: 391
تعداد دریافت فایل اصل مقاله: 429


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب