Adaptive Kalman filter based on integer ambiguity validation in moving base RTK
Adaptive Kalman filter based on integer ambiguity validation in moving base RTK
复制标题
移动基础RTK中基于整数模糊度验证的自适应卡尔曼滤波器
DOI：
10.1007/s10291-022-01367-4
复制
发表时间：
2023-01-01
期刊：
影响因子：
4.9
通讯作者：
Fang，Kun
中科院分区：
文献类型：
--
作者：
Wang，Zhipeng;Hou，Xiaopeng;Fang，Kun
在高精度动态定位中，需要保证导航系统的定位精度和可靠性，特别是对于安全关键型应用，如智能车辆导航。面对复杂的观测环境，全球导航卫星系统（GNSS）利用载波相位观测值进行高精度相对定位时，模糊度解算会受到影响，难以估计所有的模糊度。此外，当GNSS信号质量和测量噪声水平在具有许多遮挡的环境中难以预测时，所接收的卫星观测结果易于出现非常大的误差，从而导致定位解决方案中的明显偏差。然而，传统的定位算法假设测量噪声是恒定的，这是不现实的。这将导致不正确的模糊度解算，导致米级定位误差，降低系统的可靠性，增加系统的完整性风险。提出了一种基于整周模糊度验证的自适应卡尔曼滤波器（IAVAKF），以提高整周模糊度解算效率和定位精度。采用部分模糊度解算（PAR）方法求解整周模糊度。然后，通过模糊度成功率来验证固定模糊度的准确性。以模糊度成功率为动态调整因子，在卡尔曼滤波器中对状态估计的量测噪声矩阵和方差协方差矩阵进行每一时间段的自适应调整，为滤波提供平滑效果。得到了最优卡尔曼滤波增益矩阵，提高了定位精度和可靠性。静态和动态车辆实验结果表明，IAVAKF的定位精度比KF提高了26%。通过IAVAKF，可以获得更真实的PL，并应用于评估导航系统在位置域中的完整性。在水平方向和垂直方向上，该方法可以使虚警率分别降低2.45%和1.85%。
In high-precision dynamic positioning, it is necessary to ensure the positioning accuracy and reliability of the navigation system, especially for safety–critical applications, such as intelligent vehicle navigation. In the face of a complex observation environment, when the global navigation satellite system (GNSS) uses carrier phase observations for high-precision relative positioning, ambiguity resolution will be affected, and it is difficult to estimate all ambiguities. In addition, when the GNSS signal quality and measurement noise level are difficult to predict in an environment with many occlusions, the received satellite observations are prone to very large errors, resulting in apparent deviations in the positioning solution. However, traditional positioning algorithms assume that the measurement noise is constant, which is unrealistic. This will cause incorrect ambiguity resolution, lead to meter-level positioning errors, reduce the reliability of the system, and increase the integrity risk of the system. We proposed an innovative adaptive Kalman filter based on integer ambiguity validation (IAVAKF) to improve the efficiency of ambiguity resolution (AR) and positioning accuracy. The partial ambiguity resolution (PAR) method is applied to solve the integer ambiguities. Then, the accuracy of the fixed ambiguity is verified by the ambiguity success rate. Taking the ambiguity success rate as a dynamic adjustment factor, the measurement noise matrix and variance–covariance matrix of the state estimation is adaptively adjusted at each time interval in the Kalman filter to provide a smoothing effect for filtering. The optimal Kalman filter gain matrix is obtained to improve positioning accuracy and reliability. As a result, the static and dynamic vehicle experiments show that the positioning accuracy of the proposed IAVAKF is improved by 26% compared with the KF. Through the IAVAKF, a more realistic PL can be obtained and applied to evaluate the integrity of the navigation system in the position domain. It can reduce the false alarm rate by 2.45% and 1.85% in the horizontal and vertical directions, respectively.