基于Kalman滤波的空天飞行器再入制导算法

针对空天飞行器应用传统数值预测校正再入制导算法实时性不佳的问题，提出一种基于Kalman滤波的预测校正制导算法。该算法采取四阶多项式拟合速度-高度飞行剖面，利用Kalman滤波估计选定的速度点对应的高度，得到满足再入走廊及航程要求的拟合系数。在此基础上，减少一个终端约束，增加一个待估计剖面参数，可实现对再入过程飞行时间的调节。研究发现，再入过程中通过在线辨识修正不确定性参数能够提高制导指令的适应性；飞行末段利用跟踪参考剖面制导可有效避免飞行速度与终端速度接近时发生拟合系数求解发散的问题。多组不同再入条件下的算例仿真结果表明，基于Kalman滤波的空天飞行器再入制导算法实时性好，制导精度高，能够实现飞行时间可控，具有较强的鲁棒性和工程应用潜力。

Reentry guidance algorithm based on Kalman filter for aerospace vehicles

A new predictor-corrector reentry guidance algorithm based on Kalman filter is proposed to improve the real-time performance of predictor-corrector reentry guidance for aerospace vehicles. The new algorithm fits the velocity-altitude flight profile with the fourth-order polynomial. To compute the fitting coefficients satisfying the requirements of the reentry corridor and the range to be flown, the altitude corresponding to the selected speed point is estimated by Kalman filter. The reentry flight time is adjusted by reducing a terminal constraint and adding a profile parameter to be estimated in the algorithm. It is found that the adaptability of guidance instructions can be improved by correcting uncertain parameters through online identification during the reentry. At the end of the flight, the divergence of the fitting coefficients when the flight speed is close to the terminal speed can be avoided by tracking the reference profile. The simulation results of different reentry conditions demonstrate that the reentry guidance algorithm based on Kalman filter has better real-time performance, higher guidance accuracy, controllable flight time, stronger robustness and engineering application potential.