The PICARD Scientific Mission: status of the program

Rouzé, Michel; Hauchecorne, Alain; Hochedez, J.‐F.; Irbah, Abdanour; Meftah, Mustapha; Corbard, T.; Turck‐Chièze, S.; Boumier, P.; Dewitte, Steven; Schmutz, W.

doi:10.2514/6.2014-1705

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…The first one is a "tank crackle type" window w a that is generated so that all gaps are of equal duration (0.5 second) and their positions are randomly distributed on the sample (uniform distribution with 260 gaps per orbit). The second one is a "telemetry losses type" window w b where the gaps durations are drawn from a distribution similar to the telemetry thread of the PICARD mission [27], with a standard duration of one minute. Their positions are distributed in the same way as for the first window.…”

Section: B Considered Data Setsmentioning

confidence: 99%

Regression analysis with missing data and unknown colored noise: Application to the MICROSCOPE space mission

Baghi

Métris²,

Bergé³

et al. 2015

Phys. Rev. D

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The analysis of physical measurements often copes with highly correlated noises and interruptions caused by outliers, saturation events or transmission losses. We assess the impact of missing data on the performance of linear regression analysis involving the fit of modeled or measured time series. We show that data gaps can significantly alter the precision of the regression parameter estimation in the presence of colored noise, due to the frequency leakage of the noise power. We present a regression method which cancels this effect and estimates the parameters of interest with a precision comparable to the complete data case, even if the noise power spectral density (PSD) is not known a priori. The method is based on an autoregressive (AR) fit of the noise, which allows us to build an approximate generalized least squares estimator approaching the minimal variance bound. The method, which can be applied to any similar data processing, is tested on simulated measurements of the MICROSCOPE space mission, whose goal is to test the Weak Equivalence Principle (WEP) with a precision of 10 −15 . In this particular context the signal of interest is the WEP violation signal expected to be found around a well defined frequency. We test our method with different gap patterns and noise of known PSD and find that the results agree with the mission requirements, decreasing the uncertainty by a factor 60 with respect to ordinary least squares methods. We show that it also provides a test of significance to assess the uncertainty of the measurement.

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Section: B Considered Data Setsmentioning

confidence: 99%