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

Abstract

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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Correspondence to Junli Liang.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Liu, D., Liang, J. Two-Dimensional Frequencies Estimation Using Two-Stage Separated Virtual Steering Vector-Based Algorithm. EURASIP J. Adv. Signal Process. 2011, 980349 (2011). https://doi.org/10.1155/2011/980349

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Keywords

  • Singular Value Decomposition
  • Data Matrix
  • Array Data
  • Frequency Estimation
  • Full Article