Extended Object Tracking Using Automotive Radar
Xiaomeng Cao, Jian Lan, X. Rong Li, Yu Liu · 2018
For automotive radar-based extended object tracking (EOT), measurements are originated from the edges of the object, which usually has a regular shape. To handle this problem, this paper proposes an EOT approach, in which the object is assumed rectangular. Since a rectangular shape can be fully captured by its vertices, modeling and estimation of the extension can be reduced to those of the vertices, which are then included in the object state. Then an object being rectangular can be described as a quadratic equality constraint on the state. A measurement model is proposed with the scattering centers being assumed uniformly distributed over the observable edges of the object. It is further assumed that measurements at each time correspond to at most two adjacent boundary edges. By taking advantage of this, a data association method is proposed, in which the association events are largely eliminated. Given an association, the target state can be estimated in the linear minimum mean-square-error framework with the shape constraint treated as a pseudo-observation. The estimated state is then projected into the constraint space to improve estimation performance. Simulation results of an EOT scenario using automotive radar are given to illustrate the effectiveness of the proposed approach.