Spatio-temporal multicomponent optimal learning state estimation of direct numerically simulated turbulent features: a smart sensing approach

Nicholas V. Scott; Robert A. Handler; Zhen Cheng; Tian-Jian Hsu

doi:10.1117/12.2559818

22 April 2020 Spatio-temporal multicomponent optimal learning state estimation of direct numerically simulated turbulent features: a smart sensing approach

Nicholas V. Scott, Robert A. Handler, Zhen Cheng, Tian-Jian Hsu

Author Affiliations +

Proceedings Volume 11420, Ocean Sensing and Monitoring XII; 114200T (2020) https://doi.org/10.1117/12.2559818
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Geo-intelligence remote sensing platforms situated over spatially diverse areas are often tasked with geo-intelligence surveillance and adversarial monitoring for military organizations. Limited resources disallow continuous sampling of local areas at the same time, necessitating a need for smart sensing of diverse environments according to a rational evidence-based rule. Such algorithms should not only provide insight into which local region should be focused on, but should also facilitate decisions as to which environmental features should be measured over time once a local site has been selected. Multicomponent optimal learning observational arrays are demonstrated using numerically simulated data of turbulent flow to show not only the feasibility of how individual observational platforms should be chosen in a Bayesian sense, but also how goal state directed sampling of complex systems or turbulent processes over local regions can be accomplished. A Bayesian amalgamation algorithm guides which observational arrays perform knowledge gradient policy based optimal learning to smartly sample observations in local regions. Machine learning and operations research algorithms function as data agnostic, Bayesian processors demonstrating how geo-intelligence information can be efficiently captured to help solve data-driven problems.

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Nicholas V. Scott, Robert A. Handler, Zhen Cheng, and Tian-Jian Hsu "Spatio-temporal multicomponent optimal learning state estimation of direct numerically simulated turbulent features: a smart sensing approach", Proc. SPIE 11420, Ocean Sensing and Monitoring XII, 114200T (22 April 2020); https://doi.org/10.1117/12.2559818

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KEYWORDS

Statistical analysis

Computer simulations

Data acquisition

Environmental sensing

Remote sensing

Signal processing

Environmental monitoring

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