The paper will present a simulation testbed in which a scenario can be setup, simulated and evaluated and where
planning tools, electronic warfare (EW) components and command and control (C2) functionality can be integrated. The
testbed is HLA (high level architecture) compliant, allows for a distributed simulation with dynamically configurable
federates, and can also be used for testing actual equipment in a simulated scenario. One of the key components in the
testbed is a set of planning tools that can be used to show ranges for sensors, jamming and communication systems.
These tools can be used not only for planning the mission (e.g. best route) but can also be used during the mission to
show the location of possible threats or the range of own equipment (sensor, jamming, communication) in different
situations. During a mission these tools can be used to support the decisions of what actions to take in different
situations. One goal with developing the planning tools in the testbed is to learn how to use planning tools in real life
scenarios. Therefore, the planning tools are constantly developed and tested with respect to technical and tactical use.
Also technical and tactical aspects of current and future EW and C2 equipment can be tested and developed in the testbed.
This paper describes a method where it is possible to configure and simulate an entire dynamic scenario with several platforms in a network and where electronic warfare (EW) is an integrated part. The method utilizes a multispectral (radio, radar, electro-optics) framework, EWSim (Electronic Warfare Simulation interface model), for distributed EW simulations. In the framework it is possible to design dynamic scenarios which can assess the few-against-few duel, in a single user mode or in an assessment duel where teams can compete against each other. The EWSim method is also compared to simpler methods where events on a timeline is studied to draw conclusions about EW systems in a network and also to more advanced methods where system specific models are used with a high level of fidelity.
An example of how gray-level co-occurrence matrix (GLCM) trackability metric (TM) can be used to evaluate a concealing countermeasure is presented. In this example the effect of multispectral waterfog as a countermeasure for an armored vehicle is evaluated. The results are also compared to results from a correlation tracker. A careful analysis of GLCM TM results showed that multispectral waterfog is effective against a target tracker. This analysis includes a comparison to an actual tracker simulation, a contrast analysis, and an analysis of the uncertainty histograms used in the GLCM TM calculation.
Infrared Search and Track (IRST) systems are designed to automatically detect, locate, and track infrared objects and targets. A generic method for predicting the detection performance of an IRST is presented in which the effect of a 3D cluttered background can be considered. The performance predictor includes models of a target, background, atmospheric attenuation, IR sensor of the IRST, and it also includes algorithms for target detection.
A model for Image Generation in Optronic (electro-optical) Sensor Systems (IGOSS) has been developed at the Defence Research Establishment in Sweden. The aim of this model is to study and evaluate different optronic sensor systems, and to use it as a tool in technical duel simulations. The model can operate on any input image, or on a sequence of images, and the user can see the resulting image after it has been processed by the different components in the system. It is also possible to use the model for calculations of the probability of detection as a function of distance for a target with a given size. To describe the performance characteristics of an electro-optical sensor system, IGOSS models the effects of vibrations, the optics, the detector, the processing unit, the display, the eye, and transversal movement of the sensor platform and/or a separate target in the background image. The performance characteristics is as far as possible described by modulation transfer functions (MTF) that are either calculated or read from a file. However, several effects are included in the model where MTFs can not be used. Example of these effects are: vignetting, sampling and a non-ideal fill factor of detector elements in the detector, detector generated noise, non- uniformity of the detector elements, and automatic gain control. The user can at run time decide what components or processes to use when the system is modeled. It is also possible to describe a particular component using different parameter values or type of transfer function.