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Road-Map Assisted Standoff Tracking of Moving Ground Vehicle Using Nonlinear Model Predictive Control

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This paper presents a road-map assisted standoff tracking of a ground vehicle using nonlinear model predictive control. In model predictive control, since the prediction of target movement plays an important role on tracking performance, this paper focuses on utilising road-map information to enhance the estimation accuracy. For this, a practical road approximation algorithm is firstly proposed using constant curvature segments, and then nonlinear roadconstrained Kalman filtering is followed. To address nonlinearity from road constraints and provide good estimation performance, both extended Kalman filter and unscented Kalman filter are implemented along with state-vector fusion technique for cooperative UAVs. Lastly, a nonlinear model predictive control standoff tracking guidance is given. To verify the feasibility and benefits of the proposed approach, numerical simulations are performed using realistic car
trajectory data in a city traffic. 

Hyondong Oh

Field of Interest

“The field of interest shall be the organization, systems engineering, design, development, integration, and operation of complex systems for space, air, ocean, or ground environments. These systems include but are not limited to navigation, avionics, mobile electric power and electronics, radar, sonar, telemetry, military, law-enforcement, automatic test, simulators, and command and control."


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