A Coupling Method of Geomagnetic Aided Inertial Attitude Errors

Due to use of low-cost inertial sensors, the attitude calculation results of guided projectiles are inaccurate. A coupling method of geomagnetic aided inertial attitude errors is proposed in this paper. The method combines principle of geomagnetic attitude measurement to couple inertial/geomagnetic...

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Veröffentlicht in:IEEE sensors journal Jg. 20; H. 23; S. 14282 - 14289
Hauptverfasser: Deng, Zhihong, Wang, Jinwen, Liang, Xinyu, Liu, Ning
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
attitude errors coupling
Attitudes
Back propagation networks
Backpropapagation
BP neural network
Coupling
geomagnetic attitude measurement
Geomagnetism
Guided projectiles
inertial attitude calculation
Inertial sensing devices
Magnetic sensors
Magnetoresistance
Neural networks
Newton method
Projectiles
ISSN:1530-437X, 1558-1748
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Zusammenfassung:Due to use of low-cost inertial sensors, the attitude calculation results of guided projectiles are inaccurate. A coupling method of geomagnetic aided inertial attitude errors is proposed in this paper. The method combines principle of geomagnetic attitude measurement to couple inertial/geomagnetic attitude. The inertial attitude errors coupling model is constructed, and back propagation (BP) neural network is introduced to solve the model to compensate inertial attitude errors. The simulation results show that the proposed method can reduce inertial attitude maximum error angle (AMEA) by more than 45%, and attitude root mean square error (ARMSE) by more than 50% compared with inertial attitude results. The experiment results show that the proposed method can reduce AMEA and ARMSE by more than 25% compared with inertial attitude results. The proposed method has strong environmental adaptability and anti-noise ability, and is suitable for attitude calculation of guided projectiles.
Bibliographie:ObjectType-Article-1
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.3007210