Nonlinear constrained tracking of targets on roads

Ground targets are constrained to move on the Earth's surface and are most likely to travel along a road network. For targets on road, their interaction with the environment and with each other particularly at intersections is more structured, thus useful to tracking algorithms. Indeed, the knowledge of terrain database and road maps can be used as constraints and incorporated into the tracking algorithms. In this paper, we set forth a nonlinear formulation of tracking multiple interacting targets along road networks and apply nonlinear filtering techniques (namely, the extended Kalman filter, the unscented Kalman filter, the particle filter, and a hybrid Kalman particle filter) to the problem for such scenarios as air-to-ground surveillance. Simulation results are present to illustrate the performance of the nonlinear filters with the nonlinear formulation as compared to conventional ones.