Road-map assisted standoff tracking of moving ground vehicle using nonlinear model predictive control

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 road-constrained 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 the state-vector fusion technique using cooperative UAVs. Lastly, a nonlinear model predictive control standoff tracking guidance is explained briefly. To verify the feasibility and benefits of the proposed approach, numerical simulations are performed using a realistic car trajectory data in a city traffic.