A integrated Navigation Filtering Method Based on Wavelet Neural Network

Aiming at the problems of the difficulty to establish the model and the large data dimension for the traditional integrated navigation method, a method of using the wavelet neural network to directly predict the position and velocity error information is proposed. Get rid of the mathematical model e...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1302; no. 4; pp. 42014 - 42020
Main Authors Tao, Zhu, Gao, Saisai, Huang, Ying
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.08.2019
Subjects
Kalman filters
Mathematical models
Navigation systems
Neural networks
Physics
Velocity errors
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Summary:Aiming at the problems of the difficulty to establish the model and the large data dimension for the traditional integrated navigation method, a method of using the wavelet neural network to directly predict the position and velocity error information is proposed. Get rid of the mathematical model establishment, void introducing new errors in the model establishment, and adopt multiple parallel networks to reduce the dimensionality of the data, which greatly reduces the amount of calculation. The Kalman filter is used as a reference for simulation experiment. The results show that the proposed method can effectively improve the accuracy and real-time performance of the integrated navigation system, and provides a new feasible path for combined navigation filtering.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1302/4/042014