Zhu Yongyun, Zhu Yaohui, Wei Xinhua, Cui Bingbo, Liu Shede
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
School of Instrument Science & Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2024 Dec 11;24(24):7916. doi: 10.3390/s24247916.
To solve the problem of slow convergence seen in the traditional fine alignment algorithm based on linear Kalman filtering, a forward-forward backtracking fine alignment algorithm for SINS is proposed after reanalyzing the fine alignment model in this paper. First, the forward-forward backtracking fine alignment model in initial navigation frame was derived. The displacement vector of the carrier in the initial navigation frame solved by GNSS positioning was utilized as the observation of the fine alignment model. Second, under the premise of storing only part of the navigation data, the initial alignment convergence speed was improved by backtracking and reusing the navigation data. The experimental results of the simulation and vehicle tests showed that each backtracking alignment can improve the accuracy of the fine alignment to the performance requirements of the initial alignment, which proved the effectiveness and feasibility of the backtracking fine alignment algorithm proposed in this paper.
为解决传统基于线性卡尔曼滤波的精对准算法收敛速度慢的问题，本文在重新分析精对准模型后，提出了一种适用于捷联惯性导航系统（SINS）的前向-后向回溯精对准算法。首先，推导了初始导航坐标系下的前向-后向回溯精对准模型。将通过全球导航卫星系统（GNSS）定位求解得到的载体在初始导航坐标系下的位移矢量作为精对准模型的观测量。其次，在仅存储部分导航数据的前提下，通过回溯和复用导航数据提高了初始对准的收敛速度。仿真和车载试验的结果表明，每次回溯对准都能将精对准精度提高到初始对准的性能要求，证明了本文提出的回溯精对准算法的有效性和可行性。
