Electro-Optical (EO) systems are designed for purposes such as detection/recognition/identification and tracking of object(s). In order to design the systems in an optimum manner, there are many processes involved in generation of the images of targets and background at the system detector output, and these components should be carefully examined for various conditions. The image chain starts with a ray originating from target space (object space) and after propagating through atmosphere and EO system; the final position of this wave is the focal plane (image plane) where the detector is placed. EO system designs require optimization of many different system parameters for a given task; therefore, there is a need for an end-to-end imaging system simulator which models cascaded image chain blocks from object space to the detector output. An image-based system performance prediction tool has been developed for generating synthetic data in order to be used for estimation and design/optimization of EO system performance. This paper introduces this image-based performance prediction tool/scene generator in a system designer point of view, and demonstrates some properties of the tool which may be useful for system analyzers/designers for optimization. The synthetic scenes can be generated either via parametric models and/or radiometric measurements for EO system, environment, and object signature. Also, this tool has a user-friendly graphical user interface (GUI) which takes either measurement and/or system/environmental/object space parameters as inputs. The user can observe/obtain the output raw images/videos together with various system design parameters as well as image degrading effects such as modulation transfer function (MTF) and noise. In addition, this tool can be used for generating synthetic data via constructing a big data set for traditional and learning based algorithms.
KEYWORDS: Electro optical modeling, Data modeling, Mathematical modeling, Systems modeling, Performance modeling, Background noise, Cameras, Signal to noise ratio
Electro-Optical (EO) system performance prediction requires physically validated system models, atmosphere/clutter models, and target signature models. These models in general include radiometric equations and measured data. The main model of such a performance prediction simulation is EO model. EO model’s subsystem models are optical system and detector system models. One approach for EO modeling is parametric model approach which is constructed via solving radiometric equations by using optics and detector model parameters. The other approach is to use the measured system data in order to simulate the systems. The measurement includes system temporal/spatial noise, intensity response, and spatial response. In this paper, these two methods are compared via measurements collected with a generic electro-optical camera. The results show that the measured data methodology is reliable for a system simulation, whereas parametric model approach can be reliable provided that the parameters are correctly defined in their physical boundaries.
For design, testing and optimization of infrared systems, generation of big physics based synthetic data is very important, since it is impossible to collect data with experiments only. In order to create such a big radiometric data, an end-to-end synthetic simulation approach is so useful. For generating radiometric data in imaging chain from object space to the system detector output considering environmental and system effects; the parameter space in rendering pipeline can be scanned throughout imaging chain. Therefore, target, environment, electro-optical/infrared system parameter space and related radiometric data outputs of the sensor construct the big physics based data all together. Also, relative motion between the observed object and the observer is another source of physical data. In this paper, the main components and parameter space of the radiometric data are described and some example complex background scene outputs which are generated with 3D rendering are demonstrated. In addition, results of a laboratory (experimental) validation effort are discussed, which show the validity of the mathematical approach applied in generation of the radiometric data. These laboratory experiments show that when the inputs are correctly defined, the data can be generated very close to real measurements, i.e. physical reality can be synthetically generated at acceptable levels of error.
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