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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Interval Predictor Models for Data with Measurement Uncertainty

An interval predictor model (IPM) is a computational model that predicts the range of an output variable given input-output data. This paper proposes strategies for constructing IPMs based on semidefinite programming and sum of squares (SOS). The models are optimal in the sense that they yield an interval valued function of minimal spread containing all the observations. Two different scenarios are considered. The first one is applicable to situations where the data is measured precisely whereas the second one is applicable to data subject to known biases and measurement error. In the latter case, the IPMs are designed to fully contain regions in the input-output space where the data is expected to fall. Moreover, we propose a strategy for reducing the computational cost associated with generating IPMs as well as means to simulate them. Numerical examples illustrate the usage and performance of the proposed formulations.

Lacerda, Marcio J.↗
NASA NTRS2017

Quantifying Uncertainties in Navigation and Orbit Propagation Analyses

A tool used to calculate dilution of precision (DOP) was created in order to assist the Space Communications and Navigation (SCaN) program to analyze current and future user missions. The SCaN Center for Engineering, Networks, Integration, and Communication (SCENIC) is developing a new user interface (UI) to augment and replace the capabilities of currently used commercial software, such as Systems Tool Kit (STK). The DOP tool will be integrated in the SCENIC UI and will be used to analyze the accuracy of navigation solutions. This tool was developed using MATLAB and free and open-source tools to save cost and to use already existing orbital software libraries. GPS DOP data was collected and used for validation purposes. The similarities between the DOP tool results and GPS data show that the DOP tool is performing correctly. Additional improvements can be made in the DOP tool to improve its accuracy and performance in analyzing navigation solutions.

dilution of precision↗
NASA NTRS2010

Application of Ensemble Detection and Analysis to Modeling Uncertainty in Non Stationary Process

Characterization of non stationary and nonlinear processes is a challenge in many engineering and scientific disciplines. Climate change modeling and projection, retrieving information from Doppler measurements of hydrometeors, and modeling calibration architectures and algorithms in microwave radiometers are example applications that can benefit from improvements in the modeling and analysis of non stationary processes. Analyses of measured signals have traditionally been limited to a single measurement series. Ensemble Detection is a technique whereby mixing calibrated noise produces an ensemble measurement set. The collection of ensemble data sets enables new methods for analyzing random signals and offers powerful new approaches to studying and analyzing non stationary processes. Derived information contained in the dynamic stochastic moments of a process will enable many novel applications.

Racette, Paul↗
NASA NTRS2014

Accurately Measuring the Color of the Ocean on Earth and from Space: Uncertainties Revisited and A Report from the Community-Led Spectral Absorption Workshop to Update and Revise the NASA Inherent Optical Properties Protocol

The color of the ocean (apparent optical properties or AOPs) is determined by the spectral scattering and absorption of light by its dissolved and particulate constituents.The absorption and scattering properties of the water column are the so-called inherent optical properties.

protocol↗