基于抗差自适应Kalman滤波的车辆定位新方法（英文）

New vehicle positioning method based on robust adaptive Kalman filter

针对车辆定位的特点,提出一种基于旋转调制技术及运动学约束的车辆定位新方法。首先,对量测方程进行改进,并对量测噪声进行白化;然后,针对系统存在动态模型误差及观测异常情况,采用抗差自适应Kalman滤波算法抑制上述误差对状态参数估计的影响;最后,将该方法应用到实际的车辆定位系统中。实验结果显示,与传统Kalman滤波法的结果相比,新方法得到的东向和北向最大位置误差均小于Kalman滤波算法,定位精度较Kalman滤波算法提高26.7%,表明该方法具有很好的鲁棒性和自适应能力,能有效抑制系统模型误差及观测异常对系统的影响,从而提高车辆定位精度。

In view of the characteristics of vehicle positioning, a new vehicle positioning method based on rotation modulation technology and kinematic constraint is proposed. First, the measurement equation is improved, and the colored measurement noise is whitened. Then, aiming at the dynamic model error and observation anomaly in the system, a robust adaptive Kalman filter algorithm is applied to suppress their influences on the estimation of the state parameters. At last, the proposed method is applied into the actual vehicle positioning system. Experimental results show that, compared with traditional Kalman filter algorithm, the maximum north and east position errors of the proposed method is significantly decreased, improving the positioning accuracy by 26.7%. The proposed method has good robustness and adaptability and can effectively suppress the effects of the system model error and observation anomaly.