期刊
PHYSICAL REVIEW D
卷 93, 期 12, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.93.122007
关键词
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资金
- ONERA
- CNES
- UnivEarthS Labex program at Sorbonne Paris Cite [ANR-10-LABX-0023, A00NR-11-IDEX-0005-02]
We present a Gaussian regression method for time series with missing data and stationary residuals of unknown power spectral density (PSD). The missing data are efficiently estimated by their conditional expectation as in universal Kriging based on the circulant approximation of the complete data covariance. After initialization with an autoregressive fit of the noise, a few iterations of estimation/reconstruction steps are performed until convergence of the regression and PSD estimates, in a way similar to the expectation-conditional-maximization algorithm. The estimation can be performed for an arbitrary PSD provided that it is sufficiently smooth. The algorithm is developed in the framework of the MICROSCOPE space mission whose goal is to test the weak equivalence principle (WEP) with a precision of 10(-15). We show by numerical simulations that the developed method allows us to meet three major requirements: to maintain the targeted precision of the WEP test in spite of the loss of data, to calculate a reliable estimate of this precision and of the noise level, and finally to provide consistent and faithful reconstructed data to the scientific community.
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