A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors
AbstractIn 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. View Full-Text
Share & Cite This Article
Xu, X.; Xu, X.; Zhang, T.; Li, Y.; Wang, Z. A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors. Sensors 2017, 17, 709.
Xu X, Xu X, Zhang T, Li Y, Wang Z. A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors. Sensors. 2017; 17(4):709.Chicago/Turabian Style
Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Wang, Zhicheng. 2017. "A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors." Sensors 17, no. 4: 709.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.