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

Brandon Crow; Richard L. Espinola; Saba Owens; Rebekah Wilson

doi:10.1117/12.2664874

14 June 2023 Novel machine learning-based EOIR sensor performance modeling for naval applications

Brandon Crow, Richard L. Espinola, Saba Owens, Rebekah Wilson

Proceedings Volume 12533, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIV; 1253303 (2023) https://doi.org/10.1117/12.2664874
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Brandon Crow, Richard L. Espinola, Saba Owens, and Rebekah Wilson "Novel machine learning-based EOIR sensor performance modeling for naval applications", Proc. SPIE 12533, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIV, 1253303 (14 June 2023); https://doi.org/10.1117/12.2664874

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