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为了减小随机误差和野值对车载激光多普勒测速仪测速精度的影响,提出了一种自适应卡尔曼滤波算法。以“当前”统计模型为基础,结合车载测速仪实际特点建立了系统的状态空间模型,并利用速度观测值与预测值之间的偏差进行加速度方差自适应调整,同时根据卡尔曼滤波算法中新息的正交特性和速度估计误差,给出了能够剔除野值并实时反映路面特征的观测噪声方差自适应算法。仿真结果表明该算法的滤波收敛速度和估计精度都明显优于“当前”统计模型算法,实验结果证明该算法能够显著提高测速仪的测速精度与稳健性。
In order to reduce the influence of random error and outliers on the speed accuracy of laser Doppler velocimetry, an adaptive Kalman filter algorithm is proposed. Based on the “current ” statistical model, the state space model of the system is established based on the actual characteristics of the vehicle speedometer, and the variance of the acceleration variance is adaptively adjusted by using the deviation between the speed observation value and the predicted value. Meanwhile, according to the Kalman filter algorithm The orthogonality of the new interest rate and the speed estimation error, an adaptive noise variance estimation algorithm which can remove the outliers and reflect the road surface characteristics in real time is given. The simulation results show that the proposed algorithm has much better filtering convergence rate and estimation accuracy than the “current” statistical model algorithm. The experimental results show that this algorithm can significantly improve the speed accuracy and robustness of the speedometer.