Exponential modeling with unknown model order using structured nonlinear total least norm

被引：1
|
作者：
Zhang, L
Park, H
Ben Rosen, J
机构：
[1] Verizon Labs, Waltham, MA 02451 USA
[2] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA
[3] Korea Inst Adv Study, Seoul 130012, South Korea
[4] Univ Calif San Diego, Dept Comp Sci & Engn, La Jolla, CA 92093 USA
来源：
ADVANCES IN COMPUTATIONAL MATHEMATICS | 2003年 / 19卷 / 1-3期
关键词：
exponential damped signals; model order; overdetermined linear systems; Hankel matrices; singular value decomposition; structured total least norm;
D O I：
10.1023/A:1022881907141
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A new algorithm called Structured Nonlinear Total Least Norm (SNTLN) has recently been developed for obtaining an approximate solution to the structured overdetermined non-linear system. Both theoretical justification and computational testing show that SNTLN is an efficient method for solving structured overdetermined systems. In this paper, we present a method based on SNTLN for estimating the parameters of exponentially damped sinusoidal signals in noise when the model order is unknown. It is compared to two other existing methods to show its robustness in recovering correct values of parameters when the model order is unknown, in spite of some large errors in the measured data.
引用
收藏
页码：307 / 322
页数：16
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