Sparse Iterative Covariance Estimation-based Approach for Spectral Analysis and Reconstruction of Missing Data

MA Juntao; GAO Meiguo; DONG Jian

doi:10.11999/JEIT151008

Volume 38 Issue 6

Jun. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(6): 1431-1437

MA Juntao, GAO Meiguo, DONG Jian. Sparse Iterative Covariance Estimation-based Approach for Spectral Analysis and Reconstruction of Missing Data[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1431-1437. doi: 10.11999/JEIT151008

Citation:

MA Juntao, GAO Meiguo, DONG Jian. Sparse Iterative Covariance Estimation-based Approach for Spectral Analysis and Reconstruction of Missing Data[J]. Journal of Electronics & Information Technology, 2016, 38(6): 1431-1437. doi: 10.11999/JEIT151008

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Sparse Iterative Covariance Estimation-based Approach for Spectral Analysis and Reconstruction of Missing Data

doi: 10.11999/JEIT151008

1.
2.
(School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China)

Funds:

The National Natural Science Foundation of China (61401024)

Received Date: 2015-09-09
Rev Recd Date: 2016-01-29
Publish Date: 2016-06-19

Abstract

Abstract

Many researches confirmed the excellent performance of Iterative Adaptive Approach (IAA), when it is applied to spectrum analysis of missing data. Simulation results show that the IAA can use 20 percent of the data to recover the missing samples, which is superior to Gapped Amplitude and Phase EStimation (GAPES). But the reconstruction performance of IAA degrades rapidly when the missing data exceed 80%. This paper introduces a novel method of missing data spectrum analysis, and a relevant modified method of time-domain reconstruction is proposed, called Missing SParse Iterative Covariance-based Estimation(M-SPICE). This method converts the weighted missing data covariance fitting cost function to a convex optimization problem. The global convergence property is obtained by adopting cyclic minimizers. The time-domain reconstruction method is modified by renewing estimation operator, which increases the accuracy of the data reconstruction in the case of underestimation. The simulation indicates that the novel method can be used to estimate the missing data spectrum, and reconstruct missing data accurately, with even fewer valid samples, regardless of gapped or arbitrary missing patterns.
- Missing data reconstruction,
- Spectral analysis,
- Iterative Adaptive Approach (IAA),
- Sparse covariance- based estimation

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References(19)

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