Abstract: The Target tracking based on onboard millimeter-wave radar inevitably associates with surrounding environment clutter, leading to a decline in the stability of target tracking accuracy. To address this issue, a grid-based unscented Kalman filter (UKF) target tracking algorithm is proposed. First, since the millimeter-wave radar can directly measure the target's velocity, the grid-occupancy method can be used to precisely describe the grid occupancy and dynamic/static states of the environment space. Then, the occupancy information of the grid is used to weight the association points of the target, reducing the weight of possible clutter originating from the surrounding environment. The point trajectory information and its weight are then sequentially input into the unscented Kalman filter, and the target state is iteratively updated. Simulation experiments and real-data tests show that the proposed algorithm can effectively reduce the influence of environmental clutter on target tracking and effectively improve the accuracy and stability of target tracking.