Probability of detection using ShipIR/NV-IPM

David A. Vaitekunas; Gerald C. Holst; Srinivasan Ramaswamy

doi:10.1117/12.2181558

12 May 2015 Probability of detection using ShipIR/NV-IPM

David A. Vaitekunas, Gerald C. Holst, Srinivasan Ramaswamy

Proceedings Volume 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI; 94520G (2015) https://doi.org/10.1117/12.2181558
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

Existing 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 (m²), 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.

Citation Download Citation

David A. Vaitekunas, Gerald C. Holst, and Srinivasan Ramaswamy "Probability of detection using ShipIR/NV-IPM", Proc. SPIE 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI, 94520G (12 May 2015); https://doi.org/10.1117/12.2181558

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KEYWORDS

Sensors

Target detection

Image sensors

Coastal modeling

Infrared signatures

Atmospheric modeling

Thermal modeling

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