Two-Dimensional Frequencies Estimation Using Two-Stage Separated Virtual Steering Vector-Based Algorithm

In this paper, we develop a novel two-stage separated virtual steering vector- (SVSV-) based algorithm without association operation to estimate 2D frequencies. The key points of this algorithm are (i) in the first stage, this paper rearranges the measurement data as virtual rectangular array data m...

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Veröffentlicht in:EURASIP journal on advances in signal processing Jg. 2011; H. 1
Hauptverfasser: Liu, Ding, Liang, Junli
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.01.2011
Springer Nature B.V
Schlagworte:
Algorithms
Decomposition
Engineering
Estimates
Maximum entropy method
NMR
Nuclear magnetic resonance
Quantum Information Technology
Research Article
Science
Signal,Image and Speech Processing
Spintronics
Studies
Wireless communications
Singular Value Decomposition
Frequency Estimation
Data Matrix
Array Data
Full Article
ISSN:1687-6180, 1687-6172, 1687-6180
Online-Zugang:Volltext
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Zusammenfassung:In this paper, we develop a novel two-stage separated virtual steering vector- (SVSV-) based algorithm without association operation to estimate 2D frequencies. The key points of this algorithm are (i) in the first stage, this paper rearranges the measurement data as virtual rectangular array data matrix and obtains the propagator from the data matrix using least-squares operator. In addition, the virtual steering vector can be separated into two parts using the introduced electric angle that combines 2D frequencies (to avoid incorrect association especially when multiple 2D frequencies have the same frequency at some dimension), and thus the electric angle and the first part of separated steering vector can be estimated using the derived rank-reduction propagator method; (ii) in the second stage, this paper estimates the second part of separated steering vector using another least-squares operator and obtains 2D frequencies from the recovered steering vector. The resultant SVSV algorithm does not require spectral search or pairing parameters or singular value decomposition (SVD) of data matrix. Simulation results are presented to validate the performance of the proposed method.
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1155/2011/980349