کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4645100 | 1632185 | 2015 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fast ESPRIT algorithms based on partial singular value decompositions
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موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات محاسباتی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Let h(x) be a nonincreasing exponential sum of order M. For N given noisy sampled values hn=h(n)+en (n=0,â¦,Nâ1) with error terms en, all parameters of h(x) can be estimated by the known ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) method. The ESPRIT method is based on singular value decomposition (SVD) of the L-trajectory matrix (hâ+m)â,m=0Lâ1,NâL, where the window length L fulfills Mâ¤Lâ¤NâM+1. The computational cost of the ESPRIT algorithm is dominated by the cost of SVD. In the case LâN2, the ESPRIT algorithm based on complete SVD costs about 218N3+M2(21N+913M) operations. Here we show that the ESPRIT algorithm based on partial SVD and fast Hankel matrix-vector multiplications has much lower cost. Especially for LâN2, the ESPRIT algorithm based on partial Lanczos bidiagonalization with S steps requires only about 18SNlog2â¡N+S2(20N+30S)+M2(N+13M) operations, where Mâ¤Sâ¤NâL+1. Numerical experiments demonstrate the high performance of these fast ESPRIT algorithms for noisy sampled data with relatively large error terms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Numerical Mathematics - Volume 88, February 2015, Pages 31-45
Journal: Applied Numerical Mathematics - Volume 88, February 2015, Pages 31-45
نویسندگان
Daniel Potts, Manfred Tasche,