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 errors and outliers on the accuracy of vehicle laser Doppler velocimeter, an adaptive Kalman filter algorithm is proposed. Based on the "current" statistical model (CSM) and combined with the actual characteristics of vehicle velocimeter, the state-space model of system is built, and the adaptive adjustment of acceleration variance is realized by the deviation between measured and predicted value of speed. The adaptive algorithm for measuring noise variance, which can eliminate outliers and reflect the real-time characteristics of road, is given according to the orthogonal properties of innovation and speed estimation error in Kalman filter algorithm. Simulated results show that the algorithm is better than CSM algorithm in the convergence speed of filtering and estimation accuracy. Experimental results show that this algorithm can significantly improve the accuracy and robustness of velocimeter.