基于改进自适应渐消卡尔曼滤波的通用航空GNSS/微惯性组合导航算法研究

Research on GNSS/MINS integrated navigation algorithm of general aviation based on improved adaptive fading kalman filter

通用航空产业的迅速发展,对我国的经济发展和改善民生有着重要的意义。考虑到通用航空飞行器体积和成本的限制,微小型组合导航系统是其首要选择。在硬件性能无法进一步提升的条件下,使用高效的卡尔曼滤波算法是提高组合导航系统精度和稳定性的关键。针对卡尔曼滤波模型建立不准确和卫星数据响应滞后会引起常规卡尔曼滤波发散的现象,在充分考虑反馈调节时滞特性的基础上,设计了带有前馈调节的指数渐消因子自适应卡尔曼滤波器,通过调节卡尔曼滤波方程中的预测误差协方差阵和增益矩阵调整历史新息和当前新息的权重达到抑制滤波发散的目的。仿真实验表明,该算法可以有效的抑制滤波发散,并且比常规的卡尔曼滤波效果更好,同时提高了算法的及时性。

The rapid development of the general aviation industry is of great significance to China’s economic development and improvement of people’s livelihood.Considering the limitation of the size and cost of the general aviation aircraft,the micro integrated navigation system is the first choice.Under the condition that the hardware performance cannot be further improved,using an efficient Kalman filter algorithm is the key to improving the accuracy and stability of the integrated navigation system.In view of the inaccurate establishment of the Kalman filter model and the lag of satellite data response will cause the phenomenon of conventional Kalman filter divergence.Based on the full consideration of the feedback adjustment delay characteristics,an exponentially declining factor adaptive Kalman with feedforward adjustment The Mann filter is used to adjust the weight of the historical innovation and the current innovation by adjusting the prediction error covariance matrix and gain matrix in the Kalman filtering equation to achieve the purpose of suppressing the filter divergence.Simulation experiments show that the algorithm can effectively suppress the filter divergence,and it is better than the conventional Kalman filter,and the timeliness of the algorithm is improved at the same time.