-شکل‌دهی پرتو کمینه واریانس مبتنی بر بازسازی ماتریس کواریانس با بردارهای متعامد | ||
| مدیریت مهندسی و رایانش نرم | ||
| مقاله 7، دوره 9، شماره 1 - شماره پیاپی 16، فروردین 1402، صفحه 90-107 اصل مقاله (2.04 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| سامان رضایی زاده1؛ مهدی بکرانی* 2 | ||
| 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 | ||
| 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 | ||
| شکل‌دهی پرتو؛ کمینه واریانس؛ بازسازی ماتریس کواریانس؛ تداخل | ||
| عنوان مقاله [English] | ||
| Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors | ||
| نویسندگان [English] | ||
| Saman Rezaeizadeh1؛ Mehdi Bekrani2 | ||
| 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 [English] | ||
| 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 [English] | ||
| beamforming, covariance matrix reconstruction, interference, minimum variance | ||
| مراجع | ||
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