In the military, Command and Control Information Systems (C2ISs) are used for issuing commands to subordinate units. In training or decision support, simulations are used instead of live military forces. The Coalition Battle Management Language (C-BML) is currently being developed as an interface language between C2ISs, simulations and robotic forces, and Norway (FFI) and the Netherlands (TNO) are working towards extending their national C2ISs and a COTS simulation system with a C-BML interface. One of the challenges encountered during this work is the fact that the orders issued by the C2IS are at company level and above, while most available simulation systems are designed to execute platoon and single platform tasks. Both FFI and TNO are investigating using a multi-agent system for decomposing orders to lower level task by simulating national tactics and doctrine. This paper presents and compares our development approaches and agent modeling paradigms.
The problem of estimating ai¡craft pose information from mono-ocular image data is considered using a Fourier descriptor based algorithm. The dependence of pose estimation accuracy on image resolution and aspect aagle is investigated through simulations using sets of synthetic aircraft images. Figure 1. In this paper we discuss the design, implementation, and testing of an algorithm capable of extracting aircraft pose information from camera images. Fi¡st, a short int¡oduction to pose estimation techniques is given. Next, the details of the implemented pose estimation algorithm are explained. Finally, the results from simulations and real-world experiments are discussed.Most methods of image based aircraft pose estimation a¡e based on a two step approach. In the first step of this approach, global rotation, translation, and scale inva¡iant image features are used to compute the aircraft rotation about two axes that are perpendicular to the optical axis ofthe camera. In the second step, global t¡anslation and scale invariant image features are used to compute the aircraft rotation about the optical axis of the camera. This two step strategy is the basis of our pose estimation algorithm.
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