Xu Xiang, Xu Xiaosu, Zhang Tao, Li Yao, Wang Zhicheng
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China.
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2017 Mar 29;17(4):709. doi: 10.3390/s17040709.
In this paper, a coarse alignment method based on apparent gravitational motion is proposed. Due to the interference of the complex situations, the true observation vectors, which are calculated by the apparent gravity, are contaminated. The sources of the interference are analyzed in detail, and then a low-pass digital filter is designed in this paper for eliminating the high-frequency noise of the measurement observation vectors. To extract the effective observation vectors from the inertial sensors' outputs, a parameter recognition and vector reconstruction method are designed, where an adaptive Kalman filter is employed to estimate the unknown parameters. Furthermore, a robust filter, which is based on Huber's M-estimation theory, is developed for addressing the outliers of the measurement observation vectors due to the maneuver of the vehicle. A comprehensive experiment, which contains a simulation test and physical test, is designed to verify the performance of the proposed method, and the results show that the proposed method is equivalent to the popular apparent velocity method in swaying mode, but it is superior to the current methods while in moving mode when the strapdown inertial navigation system (SINS) is under entirely self-contained conditions.
本文提出了一种基于视在重力运动的粗对准方法。由于复杂情况的干扰，由视在重力计算得到的真实观测向量受到污染。详细分析了干扰源，然后设计了一种低通数字滤波器来消除测量观测向量的高频噪声。为了从惯性传感器的输出中提取有效观测向量，设计了一种参数识别和向量重构方法，其中采用自适应卡尔曼滤波器来估计未知参数。此外，还开发了一种基于休伯M估计理论的鲁棒滤波器，用于处理由于车辆机动而导致的测量观测向量的异常值。设计了一个包含仿真测试和物理测试的综合实验来验证所提方法的性能，结果表明，所提方法在摇摆模式下与流行的视在速度方法等效，但在捷联惯性导航系统（SINS）完全自主的情况下，在移动模式下优于当前方法。
