基于稀疏迭代协方差估计的缺失数据谱分析及时域重建方法

doi:10.11999/JEIT151008

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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.

Key words： Missing data reconstruction Spectral analysis Iterative Adaptive Approach (IAA) Sparse covariance- based estimation

Received: 09 September 2015 Published: 25 March 2016

PACS:

TN95

Fund:

The National Natural Science Foundation of China (61401024)

Corresponding Authors: MA Juntao E-mail: tm0508@sina.com

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	MA Juntao
	GAO Meiguo
	DONG Jian

Cite this article:

MA Juntao,GAO Meiguo,DONG Jian. Sparse Iterative Covariance Estimation-based Approach for Spectral Analysis and Reconstruction of Missing Data[J]. JEIT, 2016, 38(6): 1431-1437.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT151008 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I6/1431

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