Millimeter-wave radar has been widely used in intelligent vehicle target detection. However, there are three difficulties in radar-based target tracking in curves. First, there are massive data association calculations with poor accuracy. Second, the lane position relationship of target-vehicle cannot be identified accurately. Third, the target tracking algorithm has poor robustness and accuracy. A target tracking algorithm framework on curved road is proposed herein. The following four algorithms are applied to reduce data association calculations and improve accuracy. (1) The data rationality judgment method is employed to eliminate target measurement data outside the radar detection range. (2) Effective target life cycle rules are used to eliminate false targets and clutter. (3) Manhattan distance clustering algorithm is used to cluster multiple data into one. (4) The correspondence between the measurement data received by the radar and the target source is identified by the nearest neighbor (NN) data association. The following three algorithms aim to derive the position relationship between the ego-vehicle and the target-vehicles. (1) The lateral speed is obtained by estimating the state of motion of the ego-vehicle. (2) An algorithm for state compensation of target motion is presented by considering the yaw motion of the ego-vehicle. (3) A target lane relationship recognition model is built. The improved adaptive extended Kalman filter (IAEKF) is used to improve the target tracking robustness and accuracy. Finally, the vehicle test verifies that the algorithms proposed herein can accurately identify the lane position relationship. Experiments show that the framework has higher target tracking accuracy and lower computational time.
Research on Target Tracking Algorithm Using Millimeter-Wave Radar on Curved Road
