This work was designed to find a way to optimally (or near optimally) sample spatiotemporal phenomena based on limited sensing capability, and to create a model that can be run to estimate uncertainties, as well as to estimate covariances. The goal was to maximize (or minimize) some function of the overall uncertainty.

The uncertainties and covariances were modeled presuming a parametric distribution, and then the model was used to approximate the overall information gain, and consequently, the objective function from each potential sense. These candidate sensings were then cross-checked against operation costs and feasibility. Consequently, an operations plan was derived that combined both operational constraints/ costs and sensing gain.

Probabilistic modeling was used to perform an approximate inversion of the model, which enabled calculation of sensing gains, and subsequent combination with operational costs. This incorporation of operations models to assess cost and feasibility for specific classes of vehicles is unique.

This work was done by Steve A. Chien and David R. Thompson of Caltech, and Kian Hsiang Low of the National University of Singapore for NASA’s Jet Propulsion Laboratory. NPO-48664

This Brief includes a Technical Support Package (TSP).
Adaptive Sampling of Spatiotemporal Phenomena With Optimization Criteria

(reference NPO-48664) is currently available for download from the TSP library.

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