Real-time simulation of rotorcraft wake

Gaston, Joshua; Grigorian, Eric; Cordell, Christopher E.; Eliason, Clark; Hope, D.; Pierson, Oliver; Stewart, John M.; Yang, Zechun

doi:10.1117/12.2619057

Cited by 2 publications

(3 citation statements)

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“…DMISE is a library written in C++ that defines the basic entities that exist within a simulation. 5,6 DMISE defines two key elements that generally define the topology of a simulated scenario: an "Actor" object and an "Evaluator" object. An Actor represents an object in a simulation that has a behavior.…”

Section: Interface Descriptionmentioning

confidence: 99%

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A unified infrared and radio frequency simulation environment

Prussing,

Cordell,

Levy

2024

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II

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It is becoming more common for search and track algorithms to need to account for observations that can arise from both radio frequency (RF) and electro-optical infrared (EO/IR) measurements in the same scenario. Development of novel algorithms for search and track applications requires measured or synthetically generated data, and frequently only considers one or the other. Historically, the synthetic data generation process for RF and EO/IR developed independent of one another and did not share a common sense of “truth” about the environment or the objects within the simulation. This lack of a common framework with a consistent environment and platform representation between the two sensing modalities can lead to errors in the algorithm development process. For example, if the RF data assumed one set of atmospheric conditions while the EO/IR assumed a different set of conditions, the RF modality could over or under perform compared to the EO/IR. To address this issue, Georgia Tech Research Institute (GTRI) has developed General High-fidelity Omni-Spectrum Toolbox (GHOST) as a plug and play simulation architecture to generate high-fidelity EO/IR and RF synthetic data for search and track algorithm development. Additionally, because GHOST is plug and play, it can potentially provide synthetic or measured result to developmental algorithms without needing to change the algorithm’s interface. This paper presents the efforts GTRI has put into extending GHOST into the RF domain and presents sample results from search and track algorithm development. It also presents a look forward into how GHOST is being adapted to accommodate measured data alongside synthetic data for improved algorithm development.

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Section: Interface Descriptionmentioning

confidence: 99%

“…5 By building on a well defined interface definition, GHOST natively supports any tool that also defines the DMISE interfaces, such as real-time rotor wake utilities. 6 This allows for both the EO/IR and RF models to query a common source of truth through the GHOST interface.…”

Section: Introductionmentioning

confidence: 99%

A unified infrared and radio frequency simulation environment

Prussing,

Cordell,

Levy

2024

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II

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“…This utility can take an take an arbitrary number of independent variables and interpolate into a look up table to return an arbitrary number of dependent variables. 3 The main restriction the utility places on the look up table is that dependent values for all combinations f independent variables must be provided. In the context of atmospheric corrections, this would require specifying atmospheric transmittance and path radiance for all combinations of observer altitude, viewing zenith angle, slant range, solar azimuth angle, and solar zenith angle.…”

Section: Algorithm Designmentioning

confidence: 99%

Lazy table building for high-dimensional look up table generation

Prussing

Cordell

2023

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications

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A common constraint in synthetic data generation is the need to evaluate time and resource intensive equations to model physical systems of interest. In fact, many times one needs to evaluate many such models to build up to the real system of interest. In some cases, it is possible to identify a key set of independent variables that govern the equations of interest, and one can build a look up table for interpolation. However, the down side to this strategy is that many computational resources will be spent computing values that may not be used during a simulation. In this paper, we present a new strategy to lazily evaluate complex calculations to build these multi-dimensional look up tables as needed. The technique relies on identifying the fact that some models are able to reuse partial calculations to generate multiple results in a single invocation. This allows generating a base table in the neighborhood of the initial point of interest. After which, the table is grown as the parameter space expands. This reduces the initial computational cost, and the resultant table can be saved for reuse if desired. In a multiprocessing environment, it would also be possible to generate additional table entries in parallel if those points of interest are known in advance. As a specific example, we apply this technique to computing atmospheric corrections for synthetic image generation.

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Real-time simulation of rotorcraft wake

Cited by 2 publications

References 0 publications

A unified infrared and radio frequency simulation environment

A unified infrared and radio frequency simulation environment

Lazy table building for high-dimensional look up table generation

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