In-Motion Initial Alignment Method Based on Vector Observation and Truncated Vectorized K-Matrix for SINS

Haoqian Huang, Jiaying Wei, Di Wang, Li Zhang, Bing Wang

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In this article, an improved in-motion coarse alignment method is proposed for the strapdown inertial navigation system (SINS) aided by the global positioning system (GPS). Traditional in-motion alignment methods suffer from complex noises contained in the outputs of inertial sensors and GPS. To solve this problem, this article proposes an in-motion coarse alignment method using the vector observation and truncated vectorized K-matrix (VO-TVK) for autonomous underwater vehicles (AUVs). The contributions of this study are twofold. Firstly, a new simplified model can be applied to the in-motion alignment process by employing the zero-trace and symmetry of the K-matrix. Secondly, the proposed VO-TVK algorithm can make up for the optimal-REQUEST algorithm’s drawbacks, where the optimal-REQUEST algorithm has the conservative covariance matrix and the scalar gain. The simulation, vehicle test, and lake trial results illustrate that the proposed VO-TVK algorithm can efficiently reduce the effects of noises contained in the vector observation and achieve better accuracy than the compared algorithms.
Original languageEnglish
Article number3000415
JournalIEEE Transactions on Instrumentation and Measurement
Publication statusPublished - 4 Aug 2022

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