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 $$r\,\hbox {th}$$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 language | English |
|---|---|
| Pages (from-to) | 777-786 |
| Number of pages | 10 |
| Journal | Multidimensional Systems and Signal Processing |
| Volume | 26 |
| Issue number | 3 |
| DOIs | |
| State | Published - 3 Jul 2015 |
Bibliographical note
Publisher Copyright:© 2014, Springer Science+Business Media New York.
Funding
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).
| Funders | Funder number |
|---|---|
| National Natural Science Foundation of China | |
| Hubei Provincial Department of Education | T201206 |
| National Natural Science Foundation of China | 61172156 |
| Program for New Century Excellent Talents in University |
Keywords
- Eigenvalue
- Eigenvector
- R-D frequency estimation
- Subspace method