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R-D Frequency estimation of multidimensional sinusoids based on eigenvalues and eigenvectors

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Abstract

In this paper, a new subspace-based algorithm is proposed for the R-D signal parameter estimations of multidimensional sinusoids. The perspective idea of the algorithm is to rearrange the R-D sampling arrays into a series of two dimensional matrix columns distributed in the first dimension and the \(r\,\hbox {th}\) dimension, and then use the obtained matrix columns to construct a set of new matrices. As a result, the two-dimensional parameters in the first dimension as well as the \(r\,\hbox {th}\) dimension, can be estimated from the eigenvalues and eigenvectors of the constructed matrix, respectively. As the matrix’s eigenvalues and eigenvectors are related, the estimated signal parameters in each dimension are automatically paired.

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Acknowledgments

The work described in this paper was jointly supported by a grant from the National Natural Science Foundation of China (Project No. 61172156), the program for New Century Excellent Talents University (NCET) and the Research Plan Project of Hubei Provincial Department of Education (No. T201206).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, 30074, China

    Hui Cao & Yuntao Wu

  2. Faculty of Engineeirng, Bar-Ilan University, Ramat-Gan, 59000, Israel

    Amir Leshem

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  1. Hui Cao
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  2. Yuntao Wu
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  3. Amir Leshem
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Correspondence to Yuntao Wu.

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Cao, H., Wu, Y. & Leshem, A. R-D Frequency estimation of multidimensional sinusoids based on eigenvalues and eigenvectors. Multidim Syst Sign Process 26, 777–786 (2015). https://doi.org/10.1007/s11045-014-0277-4

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  • DOI: https://doi.org/10.1007/s11045-014-0277-4

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