Physics accurate layered sensing model

This paper will look at using open source tools (Blender © [1], LuxRender © [2], Open CV [3], SUMO [4], and Python © [5]) to build an image processing model for exploring combinations of sensors/platforms for any given image resolution. The model produces camera position, camera attitude, and synthetic camera data that can be used for exploitation purposes. We focus on electro-optical (EO) visible sensors to simplify the rendering but this work could be extended to use other rendering tools that support different modalities. Open Computer Vision (Open CV) is used to generate a camera model for the intrinsics of the virtual camera. This camera model is then used to geo-project the images from pixel space into a world coordinate system storing the output in National Imagery Transmission Format (NITF) for display with standard Intelligence Analysis (IA) tools such as RYA's Pursuer [6]. We also demonstrate Simulation of Urban Mobility (SUMO) software to simulate complex traffic patterns. The key idea of the paper is to provide an architecture for layered sensing simulation which is modular in design and constructed on open-source off-the-shelf software yielding a physics accurate virtual model of the world. This architecture shows how leveraging existing open-source software allows for practical layered sensing modeling to be rapidly assimilated and utilized in real-world applications. In this paper we demonstrate our model output is automatically exploitable by using generated data with an innovative geo-registration algorithm and real time display.