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ItemVision-based Target Tracking with Adaptive Target State Estimator(Georgia Institute of Technology, 2007-08) Sattigeri, Ramachandra J. ; Johnson, Eric N. ; Calise, Anthony J. ; Ha, Jin-CheolThis paper presents an approach to vision-based target tracking with a neural network (NN) augmented Kalman filter as the adaptive target state estimator. The vision sensor onboard the follower (tracker) aircraft is a single camera. Real-time image processing implemented in the onboard flight computer is used to derive measurements of relative bearing (azimuth and elevation angles) and the maximum angle subtended by the target aircraft on the image plane. These measurements are used to update the NN augmented Kalman filter. This filter generates estimates of the target aircraft position, velocity and acceleration in inertial 3D space that are used in the guidance and flight control law to guide the follower aircraft relative to the target aircraft. Applications of the presented approach include vision-based autonomous formation flight, pursuit and autonomous aerial refueling. The NN augmenting the Kalman filter estimates the target acceleration and hence provides for robust state estimation in the presence of unmodeled target maneuvers. Vision-in-the-loop simulation results obtained in a 6DOF real-time simulation of vision-based autonomous formation flight are presented to illustrate the efficacy of the adaptive target state estimator design.
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ItemReal-Time Vision-Based Relative Aircraft Navigation(Georgia Institute of Technology, 2007-03) Johnson, Eric N. ; Calise, Anthony J. ; Watanabe, Yoko ; Ha, Jin-Cheol ; Neidhoefer, James C.This paper describes two vision-based techniques for the navigation of an aircraft relative to an airborne target using only information from a single camera fixed to the aircraft. These techniques are motivated by problems such as "see and avoid", pursuit, formation flying, and in-air refueling. By applying an Extended Kalman Filter for relative state estimation, both the velocity and position of the aircraft relative to the target can be estimated. While relative states such as bearing can be estimated fairly easily, estimating the range to the target is more difficult because it requires achieving valid depth perception with a single camera. The two techniques presented here offer distinct solutions to this problem. The first technique, Center Only Relative State Estimation, uses optimal control to generate an optimal (sinusoidal) trajectory to a desired location relative to the target that results in accurate range-to-target estimates while making minimal demands on the image processing system.The second technique, Subtended Angle Relative State Estimation, uses more rigorous image processing to arrive at a valid range estimate without requiring the aircraft to follow a prescribed path. Simulation results indicate that both methods yield range estimates of comparable accuracy while placing different demands on the aircraft and its systems.
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ItemVisual Search Automation for Unmanned Aerial Vehicles(Georgia Institute of Technology, 2005-01) Johnson, Eric N. ; Proctor, Alison A. ; Ha, Jin-Cheol ; Tannenbaum, Allen R.This paper describes the design, development, and testing of an Unmanned Aerial Vehicle (UAV) with automated capabilities: searching a prescribed area, identifying a specific building within that area based on a small sign located on one wall, and then identifying an opening into that building. This includes a description of the automated search system along with simulation and flight test results. Results include successful evaluation at the McKenna Military Operations in Urban Terrain flight test site.
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ItemDevelopment and Test of Highly Autonomous Unmanned Aerial Vehicles(Georgia Institute of Technology, 2004-12) Johnson, Eric N. ; Proctor, Alison A. ; Ha, Jin-Cheol ; Tannenbaum, Allen R.This paper describes the design, development, and testing of Unmanned Aerial Vehicles (UAV) with highly automated search capabilities. Here, systems are able to respond on their own in the presence of considerable uncertainty utilizing an image processor, tracker/mapper, mission manager, and trajectory generation; and are used to complete a realistic benchmark reconnaissance mission. Subsequent to the selection of the search area, all functions are automated and human operator assistance is not required. The applications of these capabilities include reduction of operator workload in operational UAV systems, new UAV or guided-munition missions conducted without the assistance or availability of human operators, or the enhancement/augmentation of human search capabilities. The resulting system was able to search the 15-building village automatically with speed comparable to a human operator searching on foot or with a conventional remotely piloted vehicle. It was successful in 6 of 7 actual flights over the McKenna Military Operations in Urban Terrain test site over two different days and a variety of lighting conditions and choice of desired building.