抗野值自适应Kalman滤波在无人机测风数据处理中的应用

Adaptive Kalman Filterwith Restraining Outliers in Wind Measurement Data Process with UAV

针对无人机风场测量值含连续野值较多,且其噪声统计先验知识不足的问题,运用一种抗野值自适应Kalman滤波算法来提高其测风精度。在对模糊自适应Kalman滤波算法分析的基础上,该算法将一个压缩影响函数加权于滤波方程的新息上,根据新息的方差和均值变化自适应调整修正权值,使修正后的新息序列能够保持原有性质。相关分析结果表明,该算法能有效地克服较大野值和成片野值对滤波的不利影响,保证滤波精度,适用于无人机风场测量。

The wind velocity data with UAV contains more continuous outliers, and the prior distribution of noise statistics is known insufficiently. To improve the precision of wind velocity, an adaptive Kalman filter algorithm with restraining outliers presented in this paper. A compressibility function integrated to new information based on analyzing the Kalman Filter algorithm. According to the of the variance and mean value of new information, the weighting factor adjusted adaptively to ensure the initial properties. Simulation and analysis indicate that the algorithm can reduce the influence of outliers, and ensure the precision. The algorithm can be applied in UAV wind measurement.