کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4977620 | 1451929 | 2017 | 4 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A bias-compensated MUSIC for small number of samples
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The multiple signal classification (MUSIC) method is known to be asymptotically efficient, yet with a small number of snapshots its performance degrades due to bias in MUSIC localization function. In this communication, starting from G-MUSIC which improves over MUSIC in low sample support, a high signal to noise ratio approximation of the G-MUSIC localization function is derived. This approximation results in closed-form expressions of the weights applied to each eigenvector of the sample covariance matrix. A new method which consists in minimizing this simplified G-MUSIC localization function is thus introduced, and referred to as sG-MUSIC. Interestingly enough, this sG-MUSIC criterion can be interpreted as a bias correction of the conventional MUSIC localization function. Numerical simulations indicate that sG-MUSIC incur only a marginal loss in terms of mean square error of the direction of arrival estimates, as compared to G-MUSIC, and performs better than MUSIC.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 138, September 2017, Pages 117-120
Journal: Signal Processing - Volume 138, September 2017, Pages 117-120
نویسندگان
François Vincent, Frédéric Pascal, Olivier Besson,