Optimization and Processing of Helicopter Axis Trajectory Measurement Data

• Published in: 2022 IEEE International Conference on Unmanned Systems (ICUS) pp. 670 - 675
• Main Authors: He, Hongli, Wang, Yuanliang, Ge, Quanbo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.10.2022
• Subjects: axis trajectory; box graph theory; Data integration; Filtering algorithms; Filtering theory; Graph theory; Helicopters; kalman smoothing; median filtering; sequential fusion; Smoothing methods; Trajectory
• DOI: 10.1109/ICUS55513.2022.9986873

In order to improve the validity of the measured data of the axis trajectory, a data optimization algorithm combining multiple algorithms is proposed in this paper. Firstly, the box graph theory is used to detect the abnormal data, and then the use of median filter is improved. For the abnormal values detected, the improved strong tracking filter algorithm is inserted into the Kalman smoothing algorithm to realize the data optimization. At the same time, a strong tracking sequential fusion algorithm is used to realize the data fusion. Finally, through the algorithm to optimize the real axis trajectory measurement data, it is found that the algorithm can accurately detect and eliminate abnormal values, and can also smooth and optimize the data to achieve effective data fusion and improve the authenticity of the data.