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

Hui Cao, Yuntao Wu*, Amir Leshem

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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 $$r\,\hbox {th}$$rth 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 $$r\,\hbox {th}$$rth 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.

Original languageAmerican English
Pages (from-to)777-786
Number of pages10
JournalMultidimensional Systems and Signal Processing
Issue number3
StatePublished - 3 Jul 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2014, Springer Science+Business Media New York.


  • Eigenvalue
  • Eigenvector
  • R-D frequency estimation
  • Subspace method


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