Frequency Estimation of Irregularly Sampled Data Using a Sparsity Constrained Weighted Least-Squares Approach

Authors

  • A. Zahedi School of Electrical Engineering, Iran University of Science and Technology, Iran
  • M. H. Kahaei School of Electrical Engineering, Iran University of Science and Technology, Iran
Volume: 3 | Issue: 1 | Pages: 368-372 | February 2013 | https://doi.org/10.48084/etasr.187

Abstract

In this paper, a new method for frequency estimation of irregularly sampled data is proposed. In comparison with the previous sparsity-based methods where the sparsity constraint is applied to a least-squares fitting problem, the proposed method is based on a sparsity constrained weighted least-squares problem. The resulting problem is solved in an iterative manner, allowing the usage of the solution obtained at each iteration to determine the weights of the least-squares fitting term at the next iteration. Such an appropriate weighting of the least-squares fitting term enhances the performance of the proposed method. Simulation results verify that the proposed method can detect the spectral peaks using a very short data record. Compared to the previous one, the proposed method is less probable to miss the actual spectral peaks and exhibit spurious peaks.

Keywords:

basis pursuit, sparse representation, overcomplete dictionaries, irregular sampling, spectrum estimation

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How to Cite

[1]
A. Zahedi and M. H. Kahaei, “Frequency Estimation of Irregularly Sampled Data Using a Sparsity Constrained Weighted Least-Squares Approach”, Eng. Technol. Appl. Sci. Res., vol. 3, no. 1, pp. 368–372, Feb. 2013.

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