基于改进Kalman滤波器的无人机高度信息融合

UAV Altitude Information Fusion Based on Improved Kalman Filter

在无人机飞行控制系统中,针对无人机采用单一高度传感器测量精度不高以及传统Kalman滤波器易发散的问题,提出一种改进的Kalman滤波融合方法。首先利用ARIMA模型算法对3种传感器的原始测量数据降噪处理,然后利用Kalman滤波算法对降噪后的传感器高度信息实现第一次融合,最后借助递推加权最小二乘法将第一次融合结果与差分GPS降噪后的数据进行第二次融合。计算分析得到,该算法相比于传统Kalman滤波方法,高度估计值的均方根误差减小39.6%,最大偏差减小31.7%。仿真结果表明,所得结果在垂直方向上的定位精度得到有效改善,并且初步具备对异常情况的处理能力,保证了无人机飞行系统的准确性与可靠性。

In the UAV flight control systemwhen the UAV adopts a single height sensorthe measurement accuracy is lowand the traditional Kalman filter is prone to be divergent.To solve the problema method of fusing UAV altitude information of different sensors is proposed based on the improved Kalman filter.Firstlythe noise reduction algorithm based on ARIMA model is used to reduce the noise of the original measurement data of the three kinds of sensors.After the noise reductionthe height information of the sensors is fused for the first time by using the Kalman filter algorithm.Thenthe fusion result is fused for the second time with the noise-reduced differential GPS data by using the method of recursively weighted least squares.Computational analysis shows thatcompared with the traditional Kalman filter algorithmthe Root Mean Square Error(RMSE)of the height estimation is reduced by 39.6%and the maximum deviation is reduced by 31.7%.The simulation results show that the positioning accuracy of the obtained results in the vertical direction is effectively improvedand the preliminary ability to deal with abnormal conditions is guaranteedwhich ensures the accuracy and reliability of the UAV flight control system.