一种新型模糊自适应Kalman滤波器在组合导航中的应用

针对光纤陀螺捷联惯导(FOG SINS)/GPS组合导航系统实际工作环境中,由于系统噪声与量测噪声模型发生变化而带来的滤波器发散的问题,提出一种新型模糊自适应Kalman滤波器(FSHAKF).通过引入IMU精度因子与GPS水平精度因子,构造模糊推理系统(FIS),实时更新自适应参数,有效地解决了传统Sage-Husa自适应滤波器(SHAKF)估计模型不准确、系统噪声与量测噪声无法同时估计以及滤波器长时间易发散的问题.仿真实验表明,本文提出的FSHAKF算法相较于SHAKF算法,估计精度得到明显提高,且避免了滤波器的发散.

Application of a New Fuzzy Adaptive Kalman Filter to Integrated Navigation

In order to solve the filter divergence phenomenon brought by changes of system noise model and measurement noise model, while FOG SINS/GPS integrated navigation system is working in actually environment, this paper presents a new fuzzy adaptive Kalman filter. By constructing fuzzy inference system (FIS) to update adaptive parameter in real time with the IMU accuracy factor and GPS horizontal accuracy factor, FSHAKF solve these problems of the traditional SHAKF, which contains inaccurate estimation model, system noise and measurement noise immeasurable at the same time and easy divergent filter while working for a long time. Simulation results show that, FSHAKF algorithm proposed by this paper compared to SHAKF algorithm, obtains higher estimation accuracy significantly and avoids the divergence of the filter effectively