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Daniel Guggenheim School of Aerospace Engineering

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Now showing 1 - 6 of 6
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    Vision-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-Cheol
    This 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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    Real-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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    Adaptive Guidance and Control for Hypersonic Vehicles
    (Georgia Institute of Technology, 2006-05) Johnson, Eric N. ; Calise, Anthony J. ; Curry, Michael D.
    Guidance and control technology is recognized as an important aspect of the military, civil, and commercial goal of reliable, low-cost, aircraft-type operations into space. Here, several guidance and control methods are extended to enable integration into a single fully adaptive guidance and control system that offers a high degree of mission flexibility, fault tolerance, and autonomy. This paper summarizes the guidance and control system and several research issues related to use of adaptive guidance and control in reusable launch vehicles. Results that demonstrate the ability of the integrated system to plan and fly abort trajectories are also presented.
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    Estimation and Guidance Strategies for Vision-based Target Tracking
    (Georgia Institute of Technology, 2005-06) Calise, Anthony J. ; Johnson, Eric N. ; Sattigeri, Ramachandra J. ; Watanabe, Yoko ; Madyastha, Venkatesh
    This paper discusses estimation and guidance strategies for vision-based target tracking. Specific applications include formation control of multiple unmanned aerial vehicles (UAVs) and air-to-air refueling. We assume that no information is communicated between the aircraft, and only passive 2-D vision information is available to maintain formation. To improve the robustness of the estimation process with respect to unknown target aircraft acceleration, the nonlinear estimator (EKF) is augmented with an adaptive neural network (NN). The guidance strategy involves augmenting the inverting solution of nonlinear line-of-sight (LOS) range kinematics with the output of an adaptive NN to compensate for target aircraft LOS velocity. Simulation results are presented that illustrate the various approaches.
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    Approaches to Vision-Based Formation Control
    (Georgia Institute of Technology, 2004-12) Johnson, Eric N. ; Calise, Anthony J. ; Sattigeri, Ramachandra J. ; Watanabe, Yoko ; Madyastha, Venkatesh
    This paper implements several methods for performing vision-based formation flight control of multiple aircraft in the presence of obstacles. No information is communicated between aircraft, and only passive 2-D vision information is available to maintain formation. The methods for formation control rely either on estimating the range from 2-D vision information by using Extended Kalman Filters or directly regulating the size of the image subtended by a leader aircraft on the image plane. When the image size is not a reliable measurement, especially at large ranges, we consider the use of bearing-only information. In this case, observability with respect to the relative distance between vehicles is accomplished by the design of a time-dependent formation geometry. To improve the robustness of the estimation process with respect to unknown leader aircraft acceleration, we augment the EKF with an adaptive neural network. 2-D and 3-D simulation results are presented that illustrate the various approaches.
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    Limited Authority Adaptive Flight Control for Reusable Launch Vehicles
    (Georgia Institute of Technology, 2003-11) Johnson, Eric N. ; Calise, Anthony J.
    In the application of adaptive flight control, significant issues arise due to limitations in the plant inputs, such as actuator displacement limits, actuator rate limits, linear input dynamics, and time delay. A method is introduced that allows an adaptive law to be designed for the system without these input characteristics and then to be applied to the system with these characteristics, without affecting adaptation. This includes allowing correct adaptation while the plant input is saturated and allows the adaptation law to function when not actually in control of the plant. To apply the method, estimates of actuator positions must be found. However, the adaptation law can correct for errors in these estimates. Proof of boundedness of system signals is provided for a single hidden-layer perceptron neural network adaptive law. Simulation results utilizing the methods introduced for neural network adaptive control of a reusable launch vehicle are presented for nominal flight and under failure cases that require considerable adaptation.