SINS/视觉组合导航系统融合算法

捷联惯性导航系统(SINS)/视觉组合导航系统的融合算法主要是卡尔曼滤波,卡尔曼滤波实现最优估计的前提是系统的模型必须准确已知。对于SINS/视觉组合导航系统,获取量测信息需经图像处理、特征点提取和匹配等过程,使量测噪声统计模型不完全可知,这会导致卡尔曼滤波器的估计精度下降。因此,该文提出一种改进的自适应两级卡尔曼滤波,根据求解遗传因子的不同方法对传统自适应两级卡尔曼滤波进行改进。改进后的算法分别适用于系统噪声统计模型和量测噪声统计模型不准确可知两种情况,且二者具有统一的滤波框架。仿真结果表明,改进的自适应两级卡尔曼滤波比卡尔曼滤波精度高,有效解决了SINS/视觉组合导航系统因噪声统计模型不准确导致的精度下降问题。

Fusion Algorithm of SINS/Vision Integrated Navigation System

The fusion algorithm of SINS/vision integrated navigation system is mainly based on Kalman filter. The prerequisite for the optimal estimation of Kalman filter is that the system model has to be accurately known. To obtain measurement informations, SINS/vision integrated navigation system needs to process images, extract and match feature points and so on. It makes the measurement noise statistical model inaccurately know, thus causing decrease of the Kalman filter estimation accuracy. In order to solve this problem, an improved adaptive two stage kalman filter is proposed according to the method of solving genetic factor. The improved algorithm respectively applies in two cases, one is that the system noise statistical model cannot be accurately known and the other is that the measurement noise statistical models cannot be accurately known while both have a unified filter framework. The simulation results show that the improved adaptive two stage kalman filter is more accurate than Kalman filter and it can effectively solve the accuracy decline problem of SINS/vision integrated navigation system caused by the inaccurate noise model.