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Volume 38 Issue 6
Jun.  2016
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Guan Jian, He You. PERFORMANCE ANALYSIS OF TWO CFAR DETECTORS UNDER CLUTTER EDGE SITUATION[J]. Journal of Electronics & Information Technology, 1996, 18(3): 243-248.
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

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

doi: 10.11999/JEIT151008
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
  • 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.
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