Single-Road-Constrained Positioning Based on Deterministic Trajectory Geometry

We consider the single-road-constrained estimation problem for positioning a target that moves on a single, deterministic, and exactly known trajectory. Based on the geometry of the trajectory curve, we cast the constrained estimation problem as an unconstrained problem with reduced state dimension. Two approaches are devised based on a Markov transition model for unscented Kalman filtering and a continuous function of time for (weighted) least square fitting, respectively. A popular simulation model has been used for demonstrating the performance of the proposed approaches in comparison with the existing approaches.