Using GPS with a model-based estimator to estimate critical vehicle states
Using GPS with a model-based estimator to estimate critical vehicle states
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DOI：
10.1080/00423110903461347
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发表时间：
2010-06
影响因子：
3.6
通讯作者：
R. Anderson;D. Bevly
中科院分区：
文献类型：
--
作者：
R. Anderson;D. Bevly
本文演示了一种在基于模型的估计器中使用 GPS 和偏航率陀螺仪测量来估计车辆状态侧滑、偏航率和航向的方法。即使车辆模型处于中性转向或陀螺仪发生故障，使用 GPS 测量的基于模型的估计器也可以提供准确且可观察的侧滑、偏航率和航向估计。该方法还减少了由陀螺仪误差（例如陀螺仪偏差和陀螺仪比例因子）引入的估计误差。使用卡尔曼滤波器将 GPS 和惯性导航系统测量值结合起来，生成车辆状态的估计。卡尔曼滤波器的残差可帮助确定估计器模型是否正确，从而提供准确的状态估计。此外，还提出了一种预测由于估计模型中的误差而导致的估计误差的方法。使用正确和不正确的模型以及传感器错误对算法进行模拟测试。最后，利用2000雪佛兰Blazer的实验数据对估计方案进行了测试，以进一步验证算法。
This paper demonstrates a method to estimate the vehicle states sideslip, yaw rate, and heading using GPS and yaw rate gyroscope measurements in a model-based estimator. The model-based estimator using GPS measurements provides accurate and observable estimates of sideslip, yaw rate, and heading even if the vehicle model is in neutral steer or if the gyro fails. This method also reduces estimation errors introduced by gyroscope errors such as the gyro bias and gyro scale factor. The GPS and Inertial Navigation System measurements are combined using a Kalman filter to generate estimates of the vehicle states. The residuals of the Kalman filter provide insight to determine if the estimator model is correct and therefore providing accurate state estimates. Additionally, a method to predict the estimation error due to errors in the estimator model is presented. The algorithms are tested in simulation with a correct and incorrect model as well as with sensor errors. Finally, the estimation scheme is tested with experimental data using a 2000 Chevrolet Blazer to further validate the algorithms.