基于时间序列分析的卡尔曼滤波组合导航算法

GPS广泛用于农业机械导航研究中，其定位误差信号一般存在明显的自相关性，不能满足组合导航中常用的卡尔曼滤波算法观测噪声为高斯白噪声的要求。为此，建立了GPS定位误差AR模型，结合卡尔曼估计结果来预测和修正GPS定位误差，再将修正后的GPS定位信息应用于组合导航中的卡尔曼滤波过程。试验结果表明，无论GPS接收机是在静止还是在运动条件下，处理后的定位误差信号自相关性都明显降低，近似为白噪声；目标路径直线时的最大跟踪误差约为0.15 m，为曲线时，最大跟踪误差约为0.3 m。该方法为低精度GPS应用于农业机械导航提供了可行途径。

Kalman filtering for integrated navigation based on time series analysis

GPS is applied widely in autonomous navigation of the agricultural machinery. Its positioning error, however, is characterized by autocorrelation, can not satisfy the requirement of Kalman filtering, which is the base of the integrated navigation system of the agricultural machinery. So the characteristic of GPS positioning error was described as AR model with the time series analysis. Then the method to predict and modify the GPS positioning error with AR model and optimal estimation of Kalman filtering was introduced. And the corrected GPS positioning data were applied in the Kalman filtering for the integrated navigation of the agricultural machinery. The experimental results showed that the autocorrelation between neighboring positioning error data was decreased dramatically, and being similar to the white noise, no matter the GPS receiver was static or not. And when the tracked path was straight and curve, the maximum tracking error was about 0.15 m and 0.3 m respectively. This method can provide a viable way to achieve high-accuracy navigation with low-accuracy GPS for the agricultural machinery.