基于改进卡尔曼滤波的四维飞行航迹预测模型

为解决空气动力学模型在四维飞行航迹预测中存在的参数过多、预测精度偏低等问题,提出了一种对预测模型中的系统噪声进行实时估计的改进卡尔曼滤波（IKF)算法。首先,对雷达数据进行处理,根据航空器飞行中航向、航速进行速度转换;然后,采用传统卡尔曼滤波(KF)算法和IKF算法分别建立航迹预测模型;最后通过同一实例计算,比较两种算法在X、Y、Z方向上的预测偏差,取偏差小者为优。实验结果表明：IKF算法在X、Y方向上的预测偏差比KF算法分别降低了17.65%和98.03%,而Z方向上采用KF算法有较小的预测偏差。此外,针对IKF算法进行不同时间间隔的预测分析,在进场飞行程序的保护区宽度(9.46km)范围内,预测间隔可以增大至20s。

4D flight trajectory prediction model based on improved Kalman filter

To solve the problem of too many parameters and low prediction precision in the traditional aerodynamic 4D trajectory prediction models, an Improved Kalman Filter (IKF) algorithm was proposed to estimate the 4D trajectory, which increased the accuracy of trajectory prediction through real-time estimation of system noise. First, according to the varying direction and velocity of aircraft during flight, the velocity was shifted. Then, the prediction models were set up separately by KF and IKF. Finally, by comparing the predictive deviations in X, Y and Z directions by two algorithms, the smaller one was selected. The simulation results illustrate that the deviations respectively reduce by 17.65% and 98.03% in X and Y directions by IKF; meanwhile, KF has higher accuracy in Z direction. Besides, according to the analysis of IKF in different time interval, within the width of protection zone of arrival procedure (9.46km), the time interval could be increased to 20s.