GNSS receiver autonomous integrity monitoring with a dynamic model
GNSS receiver autonomous integrity monitoring with a dynamic model
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DOI：
10.1017/s0373463307004134
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发表时间：
2007-05-01
影响因子：
2.4
通讯作者：
Wang, Jinling
中科院分区：
文献类型：
--
作者：
Hewitson, Steve;Wang, Jinling
传统的GNSS接收机自主完整性监测是基于单历元解的。在卡尔曼滤波中，将可用的动态信息与GNSS距离测量信息融合在一起，可以显著提高雷达目标识别精度。然而，尽管卡尔曼滤波技术被广泛接受用于动态平台导航参数的最优估计，但假设状态模型和观测模型正确，它仍然容易受到未建模误差的影响。此外,意义重大。对于动态系统，也可能出现与假定模型的偏差。因此，有必要用能够识别测量和建模误差的有效可靠的完整性措施来补充状态估计过程。本文描述了动态GNSS定位导航系统中有效检测和识别异常点所需的基本方程以及可靠性和可分离性措施。这些质量措施是使用高斯-马尔可夫模型制定的卡尔曼滤波程序实现的，其中状态估计是从最小二乘原理推导出来的。已经进行了详细的模拟和分析，以评估动态信息对GNSS RAIM在异常值检测和识别、可靠性和可分离性方面的影响。本文还研究了RAIM算法检测和识别动态建模误差的能力。
Traditionally, GNSS receiver autonomous integrity monitoring (RAIM) has been based upon single epoch solutions. RAIM can be improved considerably when available dynamic information is fused together with the GNSS range measurements in a Kalman filter. However, while the Kalman filtering technique is widely accepted to provide optimal estimates for the navigation parameters of a dynamic platform, assuming the state and observation models are correct, it is still susceptible to unmodelled errors. Furthermore, significant. deviations from the assumed models for dynamic systems may also occur. It is therefore necessary that the state estimation procedure is complemented with effective and reliable integrity measures capable of identifying both measurement and modelling errors. Within this paper, fundamental equations required for the effective detection and identification of outliers in a kinematic GNSS positioning and navigation system are described together with the reliability and separability measures. These quality measures are implemented using a Kalman filtering procedure formulated with Gauss-Markov models where the state estimates are derived from least squares principles. Detailed simulations and analyses have been performed to assess the impact of the dynamic information on GNSS RAIM with respect to outlier detection and identification, reliability and separability. The ability of the RAIM algorithms to detect and identify dynamic modelling errors is also investigated.