Current target acquisition methodology in force on force simulations

Hixson, Jonathan G.; Miller, Brian; Mazz, John P.

doi:10.1117/12.2262724

Cited by 2 publications

(2 citation statements)

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“…The resultant sensor files had FPA sizes varying from 160x120 to 1280 x 1040 and optical fields of view varying from 20 x 15 to 80 x 60. NV-IPM was used to generate the sensor input files for use in the constructive simulation, iterating over numerous combinations of target contrasts, turbulence values, and ranges [3].…”

Section: Sensor Input File Generation and Input Into A Constructive S...mentioning

confidence: 99%

Scenario analysis using machine learning to inform sensor design

Hixson

2023

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIV

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Target detection is a key component of any constructive simulation. Detecting the target is the first step in the beginning of the constructive simulation. Detection predictions can have significant effect on the results in any constructive simulation. This paper explores the utility of advances in machine learning to inform sensor design by analyzing the outcomes of the time and detection models in constructive simulations. A simple scenario will be designed, and execution of the scenario will be performed to create datasets for analysis. Traditional metrics such as probability of detection and time to detect will be evaluated by the algorithms to determine the optimal sensor(s) designs to achieve the best possible performance across the scenario. A summary of the results and recommendations for machine learning algorithm design for this type of data analysis will then be presented.

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Section: Sensor Input File Generation and Input Into A Constructive S...mentioning

confidence: 99%

Scenario analysis using machine learning to inform sensor design

Hixson

2023

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIV

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“…Computational runtime and efficiency requirements of existing EOIR sensor models drastically limit their utility, especially in real-time tactical decision aid (TDA) applications. As such, parameterization of the models into multidimensional lookup tables have shown promise to reducing their computational requirements [4]. Recently, NRL developed a computationally efficient modeling and simulation capability that reduces the EOIR sensor model calculation into a machine learning-based lookup table.…”

Section: Introductionmentioning

confidence: 99%

Novel machine learning-based EOIR sensor performance modeling for naval applications

Crow

Espinola

Owens

et al. 2023

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIV

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The US Naval Research Laboratory (NRL) has recently developed an efficient modeling and simulation (M&S) capability to support naval surface warfare applications against a variety of EOIR sensing threats in the context of a tactical decision aid architecture. Starting with ship/ target signature, background sea clutter, and atmospheric transmission inputs obtained from high fidelity models such as ShipIR/NTCS and MODTRAN, combined with an Army CCDC RTID sensor performance metric, NRL used a novel methodology based on machine learning (ML) neural networks (NNs) to reduce large amounts of target/ environment/ sensor parameter data into an efficient network lookup table to predict target detectability. The model is currently valid for a few types of naval targets, in open ocean backgrounds as well as limited littoral scenarios for the VNIR (0.4-1 µm) and IR (3-5 and 8-12 µm) spectral regions. By using ML and NNs, the computational runtimes are short and efficient. This paper will discuss the methodology and show preliminary results produced in an integrated tactical decision aid software.

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Current target acquisition methodology in force on force simulations

Cited by 2 publications

References 0 publications

Scenario analysis using machine learning to inform sensor design

Scenario analysis using machine learning to inform sensor design

Novel machine learning-based EOIR sensor performance modeling for naval applications

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