基于栅格占据UKF的车载雷达目标跟踪算法OA

Target tracking based on automotive millimeter-wave radars is inevitably interfered by environmental clutter points in sur-rounding areas,and the accuracy and stability of target tracking are thus degraded.To address this issue,a target tracking algo-rithm based on grid occupancy unscented Kalman filter(UKF)is proposed in this paper.First,because target velocity can be di-rectly measured by millimeter-wave radar,the grid occupancy method is adopted to finely characterize the occupancy,static and dynamic states of spatial grids in the environment.Then,the occupancy information of the grids is used to weight the associated target points,so that the association weights of clutter points originating from surrounding environments can be reduced.Finally,the measurement information of points and their corresponding weights are sequentially fed into the unscented Kalman filter,and the target states are updated iteratively.Simulations and real dataset tests are conducted,and the results demonstrate that the pro-posed algorithm can effectively reduce the influence of environmental clutter on target tracking and significantly improve tracking accuracy and stability.Under typical scenarios such as guardrail interference and low-speed driving past stationary vehicles,smal-ler errors are achieved by the proposed algorithm in terms of evaluation metrics including maximum position deviation,average po-sition deviation,maximum velocity deviation and average velocity deviation,in comparison with the sequential UKF algorithm and the grid occupancy-based particle dual-weight target tracking algorithm.