School of Geomatics, Liaoning Technical University, Fuxin 123000, China.
State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China.
Sensors (Basel). 2018 Nov 6;18(11):3809. doi: 10.3390/s18113809.
Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.
最近，有人提出将惯性导航系统 (INS) 和全球定位系统 (GPS) 与具有两个天线的 GPS 接收器集成，以提高在恶劣环境中的稳定性和准确性。众所周知，状态过程噪声和测量噪声的统计数据是避免数值问题和获得稳定、准确估计的关键因素。在本文中，通过基于创新的自适应估计 (IAE) 方法，适当调整状态过程和观测噪声的统计数据，提出了一种改进的扩展卡尔曼滤波器 (EKF)。研究发现，由测量异常值产生的创新扰动会导致正反馈和数值问题。根据测量可靠性规范，基于重新建模算法对测量噪声协方差进行更新。进行了现场实验测试，以证明所提出的状态估计方法对动态模型误差和测量异常值具有鲁棒性。
