Abstract:

In order to solve the problems of Kalman filter dispersion and inability to accurately track the error changes in the vehicle combination navigation system， an improved adaptive filtering algorithm is designed. By analyzing the judging condition of filter divergence in principle， combining Sage-Husa filtering with multiple fading filtering algorithm， the fading factor is introduced in the filtering process. The filtering state and updating strategy are switched by the divergence condition， so that the state of filter returns to normal in time when divergence occurs. The improved algorithm is subjected to Jetlink inertial guidance/odometer combination navigation simulation tests and track of vehicle experiment. Simulation and experimental results are presented that the divergence of filter can be suppressed， and in the meanwhile， the adaptability and stability of the filter are enhanced to a certain extent.

Key words: integrated navigation, Sage-Husa adaptive Kalman filter, multiple fading filtering algorithm, odometer, filtering divergence

摘要：

为解决车载组合导航系统中卡尔曼滤波器发散、无法准确跟踪误差变化等问题，设计了一种改进的自适应滤波算法。算法从原理上推导了滤波发散判断依据，结合了Sage-Husa滤波与渐消因子算法，在滤波过程中引入了新息衰减因子，通过对新息协方差进行判断，切换滤波器均方误差阵的更新策略，当出现滤波发散时采用多重渐消因子算法使滤波状态回归正常。对改进后的算法进行了捷联惯导/里程计组合导航仿真试验及实际跑车试验，试验结果表明改进后的自适应滤波算法可以抑制组合导航滤波发散，滤波器适应能力与稳定性得到增强。

关键词: 组合导航, Sage-Husa自适应滤波, 多重渐消因子算法, 里程计, 滤波发散