Abstract
Inertial based intelligent navigation technology is an effective way to solve the problem of “how to realize high-precision intelligent navigation of unmanned vehicles when satellites are unavailable”, and it also represents the future development direction. Identifying and understanding a variety of motion behavior modes of inertial navigation is the core problem and key step to complete error matching correction, which has important practical significance. At present, the basic methods of trajectory feature classification and extraction usually rely on a certain mathematical model. The parameter threshold in the extraction process usually adopts estimated or empirical values, which can not truly and accurately identify and extract its features. An inertial navigation trajectory behavior extraction method based on permutation entropy features under road constraints is proposed, starting from the road environment of vehicles, based on the basic inertial navigation trajectory feature elements such as speed and heading angle, the “permutation entropy” is added as a supplementary feature element to describe the trajectory feature classification. At the same time, a one-dimensional convolutional neural network is constructed to train and extract the feature of the original inertial navigation trajectory data, the experimental results show that this method can improve the accuracy of inertial navigation trajectory behavior feature classification and extraction, and can effectively reduce the feature interference caused by random events.
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Li, X., Liu, W., Liu, X., Li, J. (2022). Feature Extraction and Marking Method of Inertial Navigation Trajectory Based on Permutation Entropy Under Road Constraints. In: Wu, H., et al. Spatial Data and Intelligence. SpatialDI 2022. Lecture Notes in Computer Science, vol 13614. Springer, Cham. https://doi.org/10.1007/978-3-031-24521-3_4
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DOI: https://doi.org/10.1007/978-3-031-24521-3_4
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