Fusion of Tracks with Road Constraints

This paper is concerned with tracking of ground targets on roads and investigates possible ways to improve target state estimation via fusing a target’s track with information about the road along which the target is traveling. A target track is estimated using a surveillance radar whereas a digital map provides the road network of the region under surveillance. When the information about roads is as accurate as (or even better than) radar measurements, it is desired naturally to incorporate such information (fusion) into target state estimation. In this paper, roads are modeled with analytic functions and their fusion with a target track is cast as linear or nonlinear state constraints in an optimization procedure. The constrained optimization is then solved with the Lagrangian multiplier, leading to a closed-form solution for linear constraints and an iterative solution for second-order nonlinear constraints. Geometric interpretations of the solutions are provided for special cases. Compared to other methods, the track-to-road fusion using the constrained optimization technique can be easily implemented as an add-on module without changes to an existing tracker. For curved roads with coarse waypoints, the nonlinear constrained solution outperforms the piecewise linearized constrained approach. Computer simulation results are presented to illustrate the algorithms.