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Exponential Modeling with Unknown Model Order Using Structured Nonlinear Total Least Norm

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Abstract

A new algorithm called Structured Nonlinear Total Least Norm (SNTLN) has recently been developed for obtaining an approximate solution to the structured overdetermined nonlinear 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.

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Authors and Affiliations

  1. Verizon Labs, 40 Sylvan Rd., Waltham, MA, 02451, USA

    Lei Zhang

  2. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Haesun Park & J. Ben Rosen

  3. Korea Institute for Advanced Study, 207-43 Cheongryangri-dong, Dongdaemun-gu, Seoul, 130-012, Korea

    Haesun Park

  4. Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, 92093, USA

    J. Ben Rosen

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  1. Lei Zhang
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  2. Haesun Park
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  3. J. Ben Rosen
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Zhang, L., Park, H. & Rosen, J.B. Exponential Modeling with Unknown Model Order Using Structured Nonlinear Total Least Norm. Advances in Computational Mathematics 19, 307–322 (2003). https://doi.org/10.1023/A:1022881907141

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  • DOI: https://doi.org/10.1023/A:1022881907141

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