Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors | ||
| Journal of Engineering Management and Soft Computing | ||
| مقاله 7، دوره 9، شماره 1 - شماره پیاپی 16، بهمن 2023، صفحه 90-107 اصل مقاله (2.04 M) | ||
| نوع مقاله: Original Article | ||
| نویسندگان | ||
| Saman Rezaeizadeh1؛ Mehdi Bekrani* 2 | ||
| 1MSc. in Telecommunications, Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran. Email: rezaeizadeh.s@qut.ac.ir | ||
| 2Assistant Prof., Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran. Email: bekrani@qut.ac.ir | ||
| چکیده | ||
| Minimum Variance Beamforming methods, have a weak performance in situation where error is available in covariance matrix estimation of noise and interference. The presence of the desired signal components in the estimated noise and interference vectors is of important factors of error which significantly reduces the output SINR level of the beamformer. In this paper, in order to make the beamformer robust to the incorrect estimation of the data covariance matrix, a covariance matrix reconstruction method using the orthogonal steer vectors obtained by the Gram Schmidt algorithm along with a diagonal loading is employed. Simulation results show the superiority of the proposed method in the improvement of beam pattern, angle estimation of interferences, and output SINR level, compared to the counterparts. | ||
| کلیدواژهها | ||
| beamforming؛ covariance matrix reconstruction؛ interference؛ minimum variance | ||
| عنوان مقاله [English] | ||
| -شکلدهی پرتو کمینه واریانس مبتنی بر بازسازی ماتریس کواریانس با بردارهای متعامد | ||
| نویسندگان [English] | ||
| سامان رضایی زاده1؛ مهدی بکرانی2 | ||
| 1کارشناسی ارشد، گروه مخابرات و الکترونیک، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران. رایانامه: rezaeizadeh.s@qut.ac.ir | ||
| 2استادیار گروه مخابرات و الکترونیک، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران. رایانامه: bekrani@qut.ac.ir | ||
| چکیده [English] | ||
| روشهای شکلدهی پرتو مبتنی بر کمینه واریانس، در حالتی که خطا در تخمین ماتریس کواریانس نویز و تداخل وجود داشته باشد، عملکرد ضعیفی دارند. از جمله عوامل خطا در تخمین ماتریس کواریانس، وجود مؤلفههای سیگنال مطلوب در بردارهای تخمینی نویز و تداخل است که سبب کاهش سطح سیگنال به نویز و تداخل در خروجی شکلدهنده پرتو میشود. در این مقاله برای مقاومسازی الگوریتم شکلدهی در مقابل خطای تخمین ماتریس کواریانس نویز و تداخل، از بازسازی ماتریس کواریانس با استفاده از بردارهای متعامد حاصل از الگوریتم گرام اشمیت همراه با بارگذاری قطری بهرهگیری میشود. نتایج شبیهسازی نشاندهنده برتری روش پیشنهادی در بهبود الگوی پرتو، تخمین زوایای تداخل و همچنین بالا بردن سطح توان سیگنال به نویز و تداخل در مقایسه با الگوریتمهای همتا است. | ||
| کلیدواژهها [English] | ||
| شکلدهی پرتو, کمینه واریانس, بازسازی ماتریس کواریانس, تداخل | ||
| مراجع | ||
|
Abualhayja'a, M. & Hussein, M. (2021). Comparative study of adaptive beamforming algorithms for smart antenna applications. International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 1-5. https://doi.org/10.1016/j.trb.2017.04.003 Ai, X. & Gan, L. (2019). Robust adaptive beamforming with subspace projection and covariance matrix reconstruction. IEEE Access, 7: 102149-102159. https://doi.org/1097/j.trb.2023.35.15 Cox, H. & Zeskind, R. & Owen, M. (1987). Robust adaptive beamforming. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35 (10): 1365-1376. https://doi.org/1083/j.trb.2018.16.79 Ge, Q. & Zhang, Y. & Wang, Y. & Zhang, D. (2020). Multi-constraint adaptive beamforming in the presence of the desired signal. IEEE Communications Letters, 24 (11): 2594-2598. https://doi.org/1082/j.trb.2020.10.44 Huang, Y. & Zhou, M. & Vorobyov, S. A. (2019). New designs on MVDR robust adaptive beamforming based on optimal steering vector estimation. IEEE Transactions on Signal Processing, 14 (67): 3624-3638. https://doi.org/1080/j.trb.2019.32.111 Igambi, D. & Yang, X. & Jalal, B. (2018). Robust adaptive beamforming based on desired signal power reduction and output power of spatial matched filter. IEEE Access, 6: 50217-50228. https://doi.org/1031/j.trb.2018.24.100 Jiang, B. (2008). Low-complexity implementation for worst-case optimization-based robust adaptive beamforming. 5th IEEE Sensor Array and Multichannel Signal Processing Workshop, Darmstadt. https://doi.org/1035/j.trb.2003.4.137 Jalal, B. & Yang, X. & Liu, Q. & Long, T. & Sarkar, T. K. (2020). Fast and robust variable step-size LMS algorithm for adaptive beamforming. IEEE Antennas and Wireless Propagation Letters, 19 (7): 1206-1210. https://doi.org/1060/j.trb.2009.27.68 Ke, Y. & Zheng, C. & Peng, R. & Li, X. (2017). Robust adaptive beamforming using noise reduction preprocessing-based fully automatic diagonal loading and steering vector estimation. IEEE Access, 5 (1): 12974-12987. https://doi.org/1037/j.trb.2009.27.88 Khabbazibasmenj, A. & Vorobyov, S. A. & Hassanien, A. (2012). Robust adaptive beamforming based on steering vector estimation with as little as possible prior information. IEEE Transactions on Signal Processing, 60 (6): 2974-2987. https://doi.org/1049/j.trb.2014.13.69 Kim, S. & Magnani, A. & Mutapcic. A. & Boyd, S. P. & Luo, Z. (2008). Robust beamforming via worst-case SINR maximization. IEEE Transactions on Signal Processing, 56 (4): 1539-1547. https://doi.org/1048/j.trb.2020.20.30 Li, B. & Rong, Y. & Sun, J. & Teo, K. L. (2018). A distributionally robust minimum variance beamformer design. IEEE Signal Processing Letters, 25 (12): 105-109. https://doi.org/1074/j.trb.2014.15.85 Liu, J. & Orlando, D. & Addabbo, P. & Liu, W. (2019). SINR distribution for the persymmetric SMI beamformer with steering vector mismatches. IEEE Transactions on Signal Processing, 67 (5): 1382-1392. https://doi.org/1017/j.trb.2004.21.10 Liu, Z. & Zhao, S. & Zhang, G. & Jiao, B. (2019). Robust adaptive beamforming for sidelobe canceller with null widening. IEEE Sensors Journal, 19 (23): 11213-11220. https://doi.org/1096/j.trb.2011.18.122 Manai, H. & Slama, L. B.H. & Bouallegue, R. (2019). Interference management by adaptive beamforming algorithm in massive MIMO networks. 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco, June 24-28. https://doi.org/1089/j.trb.2023.25.45 Mohammadzadeh, S. & Nascimento, V. H. & Lamare, R. C. & Kukrer, O. (2020). Maximum entropy-based interference-plus-noise covariance matrix reconstruction for robust adaptive beamforming. IEEE Signal Processing Letters, 27: 845-849. https://doi.org/1073/j.trb.2005.18.101 Proakis, J. G. & Salehi, M. (2007). Digital communications, Boston, McGraw-Hill Education: 5th edition. https://doi.org/1056/j.trb.2007.25.98 Shen, F. & Chen, F. & Song, J. (2015). Robust adaptive beamforming based on steering vector estimation and covariance matrix reconstruction. IEEE Communications Letters, 19 (9): 1636-1639. https://doi.org/1038/j.trb.2009.24.53 Shi, Y. & Huang, L. & Qian, C. & So, H. C. (2015). Shrinkage linear and widely linear complex-valued least mean squares algorithms for adaptive beamforming. IEEE Trans. Signal Process., 63 (1): 119-131. https://doi.org/1097/j.trb.2014.26.57 Somasundaram, D., (2012). Linearly constrained robust Capon beamforming. IEEE Transactions on Signal Processing, 60 (11): 5845-5856. https://doi.org/1046/j.trb.2009.31.105 Stoica, P. & Wang, Z. & Li, J. (2003). Robust capon beamforming. IEEE Signal Processing Letters, 10 (6): 172-175. https://doi.org/1024/j.trb.2017.13.41 Veen, B. D. V. & Buckley, K. M. (1988). Beamforming: a versatile approach to spatial filtering. IEEE Acoustics, Speech, and Signal Processing Magazine, 5 (2): 4-24. https://doi.org/1062/j.trb.2011.13.85 Watkins, D. S. (2008). Fundamentals of Matrix Computations. Wiley-Interscience, Pure and applied mathematics, 2ND edition. https://doi.org/1046/j.trb.2020.38.135 Yazdi, N. & Todros, K. (2020). Measure-transformed MVDR beamforming. IEEE Signal Processing Letters, 27: 1959-1963. https://doi.org/1080/j.trb.2023.33.64 Yan, L. & Yang, X. & Xi, W. & Zhang, Z. & Sarkar, T. K. (2014). Robust adaptive beamforming based on interference covariance matrix reconstruction and mismatched steering vector compensation. Proceedings of 2014 3rd Asia-Pacific Conference on Antennas and Propagation, Harbin. https://doi.org/1044/j.trb.2006.26.132 Yang, X. & Li, S. & Liu, Q. & Long, T. & Sarkar, T. K. (2020). Robust wideband adaptive beamforming based on focusing transformation and steering vector compensation. IEEE Antennas and Wireless Propagation Letters, 19 (12): 2280-2284. https://doi.org/1087/j.trb.2010.30.20 Yang, X. & Li, Y. & Liu, F. & Lan, T. & Long, T. & Sarkar, T. K. (2021). Robust adaptive beamforming based on covariance matrix reconstruction with annular uncertainty set and vector space projection. IEEE Antennas and Wireless Propagation Letters, 20 (2): 130-134. https://doi.org/1091/j.trb.2015.34.73 Yang, L. & Yang, Y. & Yang, J. (2019). Robust adaptive beamforming for uniform linear arrays with sensor gain and phase uncertainties. IEEE Access, 7 (12): 2677-2685. https://doi.org/1019/j.trb.2011.6.75 Zhang, Z. & Liu, W. & Leng, W. & Wang, A. & Shi, H. (2016). Interference-plus-noise covariance matrix reconstruction via spatial power spectrum sampling for robust adaptive beamforming. IEEE Signal Processing Letters, 23 (1): 121-125. https://doi.org/1067/j.trb.2005.21.22 Zheng, Z. & Zheng, Y. & Wang, W. & Zhang, H. (2018). Covariance matrix reconstruction with interference steering vector and power estimation for robust adaptive beamforming. IEEE Transactions on Vehicular Technology, 67 (9): 8495-8503. https://doi.org/1044/j.trb.2022.17.120 Zheng, Z. & Yang, T. & Wang, W. & So, H. C. (2019). Robust adaptive beamforming via simplified interference power estimation. IEEE Transactions on Aerospace and Electronic Systems, 55 (6): 3139-3152. https://doi.org/1090/j.trb.2017.9.107 Zhu, X. & Ye, Z. & Xu, X. & Zheng, R. (2019). Covariance matrix reconstruction via residual noise elimination and interference powers estimation for robust adaptive beamforming. IEEE Access, 7: 53262 - 53272. https://doi.org/1076/j.trb.2005.12.22 Zhu, X. & Xu, X. & Ye, Z. (2020). Robust adaptive beamforming via subspace for interference covariance matrix reconstruction. Signal Processing, 167: 1-10. https://doi.org/1088/j.trb.2021.18.46 | ||
|
آمار تعداد مشاهده مقاله: 1,183 تعداد دریافت فایل اصل مقاله: 576 |
||