In-Motion Initial Alignment Method Based on Vector Observation and Truncated Vectorized <i>K</i>-Matrix for SINS
Haoqian Huang, Jiaying Wei, Di Wang, Li Zhang, Bing Wang
Abstract
In this paper, 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 paper proposes an in-motion coarse alignment method using the vector observation and truncated vectorized <i>K</i>-matrix (VO-TVK) for autonomous underwater vehicles. 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 <i>K</i>-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.