Optimization and Processing of Helicopter Axis Trajectory Measurement Data

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 valu...

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Bibliographic Details
Published in2022 IEEE International Conference on Unmanned Systems (ICUS) pp. 670 - 675
Main Authors He, Hongli, Wang, Yuanliang, Ge, Quanbo
Format Conference Proceeding
LanguageEnglish
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
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Summary: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.
ISSN:2771-7372
DOI:10.1109/ICUS55513.2022.9986873