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Analysis and design of an attitude calculation algorithm based on elman neural network for SINS

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Abstract

In view of the shortcomings of strapdown inertial measurement unit (IMU) such as large noise and error accumulation, poor precision of traditional attitude calculation algorithms and poor adaptability to environment, this paper proposes an attitude calculation algorithm aided by Elman neural network. For multi-sensor information fusion, not every neural network is applicable, but the Elman neural network structure contains a receiving layer, which stores the information of the previous hidden layer, this structural feature enables the Elman neural network to predict in continuous signal. The simulation results show the effectiveness of the algorithm and improve the accuracy and adaptability of the algorithm.

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Acknowledgements

The authors would like to thank Prof. Wei Guo at National Key Laboratory of Science and Technology on Communications of UESTC for help, and Prof. Long Jin and Prof. Yonglun Luo at Research Institute of Electronic Science and Technology of UESTC for them assistance in SINS. The author also wants to thank Research Institute of Electronic Science and Technology and Key Laboratory of Integrated Electronic System, Ministry of Education for their support of this research. However, the opinions expressed in this paper are solely those of the authors.

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Authors and Affiliations

  1. National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China

    Chengjun Guo

  2. Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    Jiahan Yan & Zhong Tian

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  1. Chengjun Guo
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  2. Jiahan Yan
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  3. Zhong Tian
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Correspondence to Chengjun Guo.

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Guo, C., Yan, J. & Tian, Z. Analysis and design of an attitude calculation algorithm based on elman neural network for SINS. Cluster Comput 22 (Suppl 6), 15267–15272 (2019). https://doi.org/10.1007/s10586-018-2562-8

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  • DOI: https://doi.org/10.1007/s10586-018-2562-8

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