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- W2891552391 abstract "This paper aims to reduce the structural complexity and navigation errors of stable platform inertial navigation system (INS). For this purpose, a new navigation error model was proposed for the stable platform INS based on quantum neural network (QNN). After reviewing several representative QNNs, the author established a QNN-based navigation error estimation algorithm on four coordinate systems, in which the angles of acceleration error were calibrated by twelve positions. Then, the QNN-based INS error model was constructed through training and testing. Next, the established model was applied to an experiment on two flight tracks with Kalman filter (KF), fixed-interval smoothing filter (smooth) and improved smoothing filter (improved). The results show that the model can significantly improve the multi-position calibration accuracy and the self-calibration accuracy of acceleration errors. The research findings shed new light on the accuracy evaluation of stable INS." @default.
- W2891552391 created "2018-09-27" @default.
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- W2891552391 date "2018-06-24" @default.
- W2891552391 modified "2023-09-23" @default.
- W2891552391 title "Accuracy Evaluation Method of Stable Platform Inertial Navigation System Based on Quantum Neural Network" @default.
- W2891552391 doi "https://doi.org/10.14704/nq.2018.16.6.1569" @default.
- W2891552391 hasPublicationYear "2018" @default.
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