Abstract: MEMS IMU/GPS integrated navigation system application environments have become increasingly complex, and higher and higher accuracy has been required. Using only the ordinary Kalman filter can’t meet the requirements of accuracy and stability. For this problem, the Sage-husa adaptive Kalman filter algorithm and nonholonomic constraints are applied to the forward and backward navigation filtering algorithm, and the results of the forward and back navigation filtering algorithm are weighted combination. A weighted combination filtering algorithm is proposed with nonholonomic constraint, which is used in the post-IMU/GPS combination solution, and which is used for improving navigation accuracy. And laboratory equipment is used to do vehicle experiments. The measured vehicle data solution results validate the feasibility of the method. Experimental results show that the MEMS IMU/GPS combination filtered latitude and longitude error is less than 1.4 m, and heading angle error is less than 1.0°, which meet MEMS IMU/GPS vehicle navigation system accuracy requirements.

Key words: integrated navigation, Sage-husa adaptive Kalman filter, nonholonomic constraints, forward navigation, backward navigation, weighted combination filtering algorithm

摘要： MEMS IMU/GPS组合导航系统的应用环境愈来愈复杂，对其精度的要求也愈来愈高，只使用普通卡尔曼滤波不能满足精度和稳定性要求。针对此问题，将Sage-husa自适应卡尔曼滤波算法和非完整约束应用到前向导航滤波算法和后向导航滤波算法中，并将前向滤波和后向滤波结果加权组合，提出了一种非完整约束下加权组合滤波算法，用于事后IMU/GPS联合解算中，用来提高组合导航的精度。并利用实验室设备进行车载实验，通过实测车载数据解算结果来验证该方法的可行性。实验结果表明非完整约束下加权组合滤波后的经纬度误差小于1.4 m，航向角误差小于1.0°，满足MEMS IMU/GPS车载组合导航系统的精度要求。

关键词: 组合导航, Sage-husa自适应卡尔曼滤波, 非完整约束, 前向导航, 后向导航, 加权组合滤波算法