Validation of ShipIR (v3.2): methodology and results

Vaitekunas, David A.

doi:10.1117/12.666790

Cited by 14 publications

(10 citation statements)

References 8 publications

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“…The previous validation work [6] used measurements from the NATO Ship Infrared Model Validation Experiment (SIMVEX) in September 2001 (Q262) and two subsequent Canadian trials (Q276 and Q280) to validate the various sub-components of ShipIR (v3.2). This paper provides a good reference for the evolution in IR modelling and IR As shown in Figure 4, a number of Run Types were configured during the NATO SIMVEX trial to permit the Quest to achieve a thermal equilibrium by following a constant speed (SOG) and course (COG) for 1/2-hour before each IR measurement at a range of 1 km from the Naval Electronic Systems Test Range Atlantic (NESTRA) facility at Osbourne Head, Nova Scotia.…”

Section: Previous Validation Experimentsmentioning

confidence: 99%

“…ShipIR/NTCS is a comprehensive software engineering tool for predicting the thermal infrared (IR) signature and IR susceptibility of naval and air ships, whose description and validation have been the subject of numerous SPIE Proceedings ( [1], [2], [3], [4], [5], [6]). Basic sensor information, geography, and meteorology (including date and time) are input to a background model utilizing MODTRAN [7] for sun and sky radiance, and atmospheric propagation, and a proprietary model for the average and root-mean-square (RMS) variance in sea radiance.…”

Section: Introductionmentioning

confidence: 99%

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Validation of ShipIR (v4.2)

Vaitekunas

Aleksandrov

2023

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

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Numerous improvements have been made to various sub-models in the NATO-standard and USN accredited naval ship infrared signature model (ShipIR) since its last validation in SPIE using ShipIR (v3.2). These include upgrades to MODTRAN5 and MODTRAN6 for the sun, sky, and atmosphere models, and various fixes and improvements to the sea model: corrections to the Fresnel sea reflectance formula (v3.3a), empirical 2nd-order hiding (v3.4), and a recent fix to the sea surface roughness slope distribution (v4.2). This paper will revisit the previous experimental results, used to validate ShipIR (v3.2), first comparing these results against the steady-state version of the thermal solver in ShipIR (v4.2) and complementing these with the transient thermal solver introduced in ShipIR (v4.0).

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Section: Previous Validation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of ShipIR (v4.2)

Vaitekunas

Aleksandrov

2023

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

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“…As a result, ShipIR has become a highly sophisticated and tightly integrated environment for modelling platform temperatures and IR signatures. Validation of ShipIR has been the topic of several research papers (Vaitekunas and Fraedrich 1999, Fraedrich et al 2003, Vaitekunas 2006). …”

Section: Introduction (Shipir)mentioning

confidence: 99%

Probability of detection using ShipIR/NVIPM

Vaitekunas

Holst²,

Ramaswamy

2015

SPIE Proceedings

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http://www.davis-eng.com b JCD Publishing, 957 Carissa Lane, Oviedo, FL 32765 phone/fax: +1 407 365 5762; ABSTRACTExisting FLIR detection models such as NVThermIP and NV-IPM, from the U.S. Army Night Vision and Electronic Sensors Directorate (NVESD), use only basic inputs to describe the target and background (area of the target, average and RMS temperatures of both the target and background). The objective of this work is to try and bridge the gap between more sophisticated FLIR detection models (of the sensor) and high-fidelity signature models, such as the NATO-Standard ShipIR model. A custom API is developed to load an existing ShipIR scenario model and perform the analysis from any user-specified range, altitude, and attack angle. The analysis consists of computing the total area of the target (m 2 ), the average and RMS variation in target source temperature, and the average and RMS variation in the apparent temperature of the background. These results are then fed into the associated sensor model in NV-IPM to determine its probability of detection (versus range). Since ShipIR computes and attenuates the spectral source radiance at every pixel, the black body source and apparent temperatures are easily obtained for each point using numerical iteration (on temperature), using the spectral attenuation and path emissions from MODTRAN (already used by ShipIR to predict the apparent target and background radiance). In addition to performing the above calculations on the whole target area, a variable threshold and clustering algorithm is used to analyse whether a sub-area of the target, with a higher contrast signature but smaller size, is more likely to be detected. The methods and results from this analysis should provide the basis for a more formal interface between the two models.

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“…Verification will be done for different environmental conditions, for a large number of segments (line between two objects) and for 2 IR spectral bands (MWIR: Middle Wave IR, LWIR: Long Wave IR). The model validation is a fundamental task found as mentioned in development reports [1], [2], [3], [4]. This paper describes the different steps of the infrared radiance contrast prediction validation and robustness study, namely : -Development and use of an infrared radiance contrast prediction model using different ONERA models (Thermal model, radiative codes, radiance calculation model…) -Realization and use of a dedicated experimentation site with several targets and instruments for radiance, temperature and weather measurements -Measurement of physical properties for all targets -Description of a validation methodology in order to compare simulated and measured radiance contrasts -Description of a large number of simulation cases (environmental, segments) to cover the application domain -Processing and analysis of simulation results and experimental data in order to estimate the infrared contrast prediction model robustness.…”

Section: Introductionmentioning

confidence: 99%

Validation methodology and robustness study of an infrared radiance contrast prediction model

Barbé

2006

Targets and Backgrounds XII: Characterization and Representation

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Infrared synthetic imagery simulators are commonly used for validation of infrared imaging terminal guidance missile performances. A high level of confidence for infrared synthetic imagery simulation is needed. The prediction fiability depends on radiance model quality and input parameters knowledge. An infrared radiance contrast prediction model was developed at ONERA to study environmental and target/background characteristics effects on contrasts in infrared scene. The aim of this study is to compare the prediction robustness in middlewave and longwave infrared spectral bands (MWIR, LWIR), and, later, to estimate input parameters uncertainties on the prediction quality. This paper presents the validation methodology and validation study results. A specific validation criterion is used to evaluate the model ability to predict the presence or the lack of contrast between two objects separated by a line (segment). Simulated radiance contrasts are compared to measured contrasts on the PIRRENE test site located 30 km south west of Toulouse, France. Model validation needs a large number of conditions to cover the application domain. The specified conditions are: 2 climatological conditions (summer, winter), 2 meteorological conditions (clear sky, cloudy sky) and 28 segments combining 7 materials and 3 geometries (horizontal / horizontal, vertical / horizontal et vertical / vertical). MWIR and LWIR radiance contrasts are simulated for each condition on complete diurnal cycle with 15-minute sampling.

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Validation of ShipIR (v3.2): methodology and results

Cited by 14 publications

References 8 publications

Validation of ShipIR (v4.2)

Validation of ShipIR (v4.2)

Probability of detection using ShipIR/NVIPM

Validation methodology and robustness study of an infrared radiance contrast prediction model

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