Attitude-Induced error modeling and compensation with GRU networks for the polarization compass during UAV orientation. (28th February 2022)

• Record Type: Journal Article
• Authors: Zhao, Donghua; Liu, Yueze; Wu, Xindong; Dong, Hao; Wang, Chenguang; Tang, Jun; Shen, Chong; Liu, Jun
• Is Part Of: Measurement. Volume 190(2022)
• Publication Date: 2022-02-28
• Subjects: UAV orientation -- Polarization navigation -- Error processing -- Neural Networks; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.110734
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A heading error analysis about variable attitude angles of a compass is presented. A heading error modeling method for the attitude-change of a compass is developed. GRU is used to model the heading error resulted in the attitude-change of a compass. Abstract: The polarization compass used for unmanned aerial vehicle (UAV) navigation is usually hypothesized to be arranged horizontally in conventional heading measurement, which leads to a noticeable heading error due to the inevitable tilts called the pitch angle and the roll angle during UAV flight process. In addition, we found that the coupling of the angle between the solar meridian and the body axis of a carrier (A-SMBA) and tilted-angles will produce more remarkable heading errors. Consequently, we first introduce a comprehensive analysis of heading error in terms of variable attitude angles of the compass including the A-SMBA, the pitch angle and the roll angle. A novel heading error modeling and compensation for attitude-changed of the polarization compass by gated recurrent unit (GRU) neural network is developed subsequently. The experimental results demonstrate the proposed heading error modeling and compensation method performs best compared to state-of-the-art algorithms in predicting the UAV orientation.