基于双卡尔曼滤波及概率最近邻数据关联的道路坡度实时估计

Real Time Estimation of Road Slope Based on Dual Kalman Filter and Probabilistic Nearest Neighbor Data Association

道路坡度的估计对于车辆的精确控制、行驶环境的构建分析具有重要意义。在以往的坡度估计中未考虑坡度变化率,估计值难以实时跟随道路坡度的变化。同时基于单一估计方法的估计结果会受到刹车、换档、急加速等车辆状态的影响,难以保证坡度估计的可靠性及准确性。因此,提出了一种基于双卡尔曼滤波及概率最近邻数据关联滤波器的道路坡度估计方法。根据不同的车辆状态将基于动力学与基于运动学的两种子估计坡度值进行全局融合,避免单个子估计坡度值的误差。同时,对于每种子估计方法提出了一种基于双(无迹)卡尔曼滤波的坡度变化率及坡度分层估计算法。仿真和试验结果表明,所提出的子估计方法能够更好地跟随道路坡度的变化,提高估计精度。而全局融合方法能够在子估计方法的基础上进一步提高估计精度,避免某一子估计值的较大误差,提高坡度估计的准确性及可靠性。

The estimation of road slope is of great significance for the accurate control of vehicles and the construction and analysis of driving environment.In the previous slope estimation,the slope change rate is not considered,so it is difficult for the estimated value to follow the change of road slope in real time.And,the estimation results based on a single estimation method will be affected by braking,shifting,rapid acceleration and other vehicle states,so it is difficult to ensure the reliability and accuracy of slope estimation.Therefore,a road slope estimation method based on dual Kalman filter and probabilistic nearest neighbor data association filter is proposed.According to different vehicle states,the two sub estimated slope values based on dynamics and kinematics are fused globally to avoid the error of single sub-estimated slope value.And,for each sub estimation method,a slope change rate and slope hierarchical estimation algorithm based on dual(unscented)Kalman filter is proposed.Simulation and experimental results show that the proposed sub-estimation method can better follow the change of road slope and improve the estimation accuracy.The global fusion method can further improve the estimation accuracy on the basis of the sub-estimation method,avoid the large error of a sub estimation,and improve the accuracy and reliability of slope estimation.