On the Use of Gaussian Random Processes for Probabilistic Interpolation of CubeSat Data in the Presence of Geolocation Error

Ruan, Wenjie; Milstein, Adam B.; Blackwell, William J.; Miller, Eric L.

doi:10.1109/jstars.2016.2577583

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…This approach yields an ARIMA(2,1,3) model. Hence, our initial model for z is the (25) following nonstationary time-series model:…”

Section: Appendix Stochastic Model For Nfrad Datamentioning

confidence: 99%

“…(The initial estimates of the variances of additive noise terms for each reference source were similarly obtained.) We estimate the standard deviation of the additive noise terms in our model [see (25)] for V off ,σ e off , as σ e off = α σ e off (26) where α is an adjustable scaling factor. Later, we scale e t in our (24) by replacing e t withẽ t = κe t where κ is adjustable.…”

Section: Appendix Stochastic Model For Nfrad Datamentioning

confidence: 99%

“…Based on a nonstationary stochastic model for the NFRad instrument, we determine the random uncertainty of our stability metrics by a Monte Carlo method. We expect our methods to apply to other instruments, including the new generation of small satellites [19]- [24], [25].…”

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confidence: 99%

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Microwave Radiometer Instability Due to Infrequent Calibration

Coakley

Splett

Walker

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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We directly quantify the effect of infrequent calibration on the stability of microwave radiometer temperature measurements (where a power measurement for the unknown source is acquired at a fixed time, but calibration data are acquired at variable earlier times) with robust and nonrobust implementations of a new metric. Based on our new metric, we also determine a component of uncertainty in a single measurement due to infrequent calibration effects. We apply our metric to experimental data acquired from experimental ground-based calibration data acquired from a NASA millimeter-wave imaging radiometer and a NIST radiometer (Noise Figure Radiometer-NFRad). Based on a stochastic model for the NFRad, we determine the random uncertainty of an empirical prediction model of our stability metric by a Monte Carlo method. For comparison purposes, we also present a secondary metric that quantifies stability for the case where calibration data are acquired at a fixed time, but power measurements for the unknown source are acquired at variable later times.

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“…This approach yields an ARIMA(2,1,3) model. Hence, our initial model for z is the (25) following nonstationary time-series model:…”

Section: Appendix Stochastic Model For Nfrad Datamentioning

confidence: 99%