Irma multisensor predictive signature model

John S. Watson; Michael R. Wellfare; David B. Chenault; Sunjay E. Talele; Bradley T. Blume; Mike Richards; Lee Prestwood

doi:10.1117/12.276673

20 June 1997 Irma multisensor predictive signature model

John S. Watson, Michael R. Wellfare, David B. Chenault, Sunjay E. Talele, Bradley T. Blume, Mike Richards, Lee Prestwood

Author Affiliations +

Proceedings Volume 3062, Targets and Backgrounds: Characterization and Representation III; (1997) https://doi.org/10.1117/12.276673
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

Development of target acquisition and target recognition algorithms in highly cluttered backgrounds in a variety of battlefield conditions demands a flexible, high fidelity capability for synthetic image generation. Cost effective smart weapons research and testing also requires extensive scene generation capability. The Irma software package addresses this need through a first principles, phenomenology based scene generator that enhances research into new algorithms, novel sensors, and sensor fusion approaches. Irma was one of the first high resolution synthetic infrared target and background signature models developed for tactical air-to-surface weapon scenarios. Originally developed in 1980 by the Armament Directorate of the Air Force Wright Laboratory, the Irma model was used exclusively to generate IR scenes for smart weapons research and development. in 1987, Nichols Research Corporation took over the maintenance of Irma and has since added substantial capabilities. The development of Irma has culminated in a program that includes not only passive visible, IR, and millimeter wave (MMW) channels but also active MMW and ladar channels. Each of these channels is co-registered providing the capability to develop algorithms for multi-band sensor fusion concepts and associated algorithms. In this paper, the capabilities of the latest release of Irma, Irma 4.0, will be described. A brief description of the elements of the software that are common to all channels will be provided. Each channel will be described briefly including a summary of the phenomenological effects and the sensor effects modeled in the software. Examples of Irma multi- channel imagery will be presented.

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John S. Watson, Michael R. Wellfare, David B. Chenault, Sunjay E. Talele, Bradley T. Blume, Mike Richards, and Lee Prestwood "Irma multisensor predictive signature model", Proc. SPIE 3062, Targets and Backgrounds: Characterization and Representation III, (20 June 1997); https://doi.org/10.1117/12.276673

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KEYWORDS

Sensors

Extremely high frequency

LIDAR

Human-machine interfaces

Reflectivity

Algorithm development

Image sensors

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