Coakley Kevin J, Splett Jolene, Walker David, Aksoy Mustafa, Racette Paul
National Institute of Standards and Technology, Boulder, CO 80305 USA.
University at Albany, State University of New York, Albany, NY 12222 USA.
IEEE J Sel Top Appl Earth Obs Remote Sens. 2020;13. doi: https://doi.org/10.1109/jstars.2020.2984004.
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.
我们使用一种新度量的稳健和非稳健实现方式，直接量化不频繁校准对微波辐射计温度测量稳定性的影响（其中，在固定时间获取未知源的功率测量值，但校准数据在更早的可变时间获取）。基于我们的新度量，我们还确定了由于不频繁校准效应导致的单次测量中不确定性的一个分量。我们将我们的度量应用于从美国国家航空航天局毫米波成像辐射计和美国国家标准与技术研究院辐射计（噪声系数辐射计 - NFRad）获取的实验地面校准数据的实验数据。基于NFRad的随机模型，我们通过蒙特卡罗方法确定我们稳定性度量的经验预测模型的随机不确定性。为了进行比较，我们还提出了一种辅助度量，用于量化在校准数据在固定时间获取，但未知源的功率测量值在更晚的可变时间获取的情况下的稳定性。
