کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6957066 | 1451915 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Real-valued root-MUSIC for DOA estimation with reduced-dimension EVD/SVD computation
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Real-valued root-MUSIC for DOA estimation with reduced-dimension EVD/SVD computation Real-valued root-MUSIC for DOA estimation with reduced-dimension EVD/SVD computation](/preview/png/6957066.png)
چکیده انگلیسی
A novel real-valued formulation of the popular root multiple signal classification (root-MUSIC) direction of arrival (DOA) estimation technique with substantially reduced computational complexity is developed. The proposed real-valued root-MUSIC (RV-root-MUSIC) algorithm reduces the computational burden mainly in three aspects. First, it exploits the eigenvalue decomposition or the singular value decomposition (EVD/SVD) of a real-valued covariance matrix to extract a real-valued noise subspace, which reduces the complexity by a factor about four as compared to root-MUSIC. Next, based on the bisymmetric or the anti-bisymmetric structure of the real-valued covariance matrix, the real-valued EVD/SVD in RV-root-MUSIC is optimized to be equivalently performed on two sub-matrices with reduced dimensions of about half sizes, which further reduces the complexity by another factor about four as compared to most state-of-the-art real-valued estimators including unitary root-MUSIC (U-root-MUSIC). Finally, the eigenvectors and the singular vectors of those sub-matrices are found of centrosymmetrical or anti-centrosymmetrical structures while the roots of RV-root-MUSIC are proven to appear in conjugate pairs with the form a+jb,aâjb, which also allows fast coefficient computation and real-valued rooting using Bairstow's method. Numerical simulations illustrate that with significantly reduced complexity, the proposed technique is able to provide good root mean square errors (RMSEs) close to the Cramér-Rao Lower Bound (CRLB).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 152, November 2018, Pages 1-12
Journal: Signal Processing - Volume 152, November 2018, Pages 1-12
نویسندگان
Feng-Gang Yan, Liu Shuai, Jun Wang, Jun Shi, Ming Jin,