基于自适应强跟踪滤波的捷联惯导/里程计组合导航方法

SINS/Odometer integrated navigation method based on adaptive strong tracking filter

针对大型轮式车行驶过程中里程计标度发生较大变化,无法满足车载定位定向系统给里程计分配的误差要求,研究了一种非线性滤波方法。以容积卡尔曼滤波为算法框架,引入强跟踪滤波渐消因子的基本理论,提出了捷联惯导/里程计组合导航的自适应强跟踪滤波算法,达到实时自适应估计并补偿里程计标度误差的目的。仿真分析和跑车试验验证了该方法的有效性,新方法比传统强跟踪滤波更进一步消除了里程计误差的影响。试验结果表明该方法使得捷联惯导/里程计组合导航的定位精度提高了两倍以上,达到了惯性元件的理论精度。

Due to that fact that the speedometer scale changes greatly in the process of driving a large wheeled vehicle, the speedometer＇s error requirements assigned by the vehicle positioning and orienting system cannot be satisfied. To solve this problem, a nonlinear filtering method is studied. By taking a cubature Kalman filter as the algorithm framework and applying the basic theory of strong tracking filter fading factor, an adaptive strong tracking filter algorithm of SINS/Odometer integrated navigation is constructed to achieve the real-time adaptive estimation and compensation of the odometer scale errors. Simulation analysis and running test verify the feasibility of this method, and show that the new method can eliminate the influence of the odometer error further than traditional strong tracking filter method. The test results demonstrate that the positioning accuracy is more than doubled, achieving the ideal precision of the inertial components.