空间再入飞行体多传感观测融合仿真研究  

Simulation study of multi-sensor observation fusion of space reentry vehicle

作　　者：韩涛[1] 朱耀麟[1] 卢健[1] 

机构地区：[1]西安工程大学电信学院,陕西西安710048

出　　处：《物联网技术》2012年第10期38-42,共5页Internet of things technologies

基　　金：国家自然科学基金项目(61040055);陕西省科技厅科技发展计划项目(2011K06-01)

摘　　要：空间飞行器再入飞行的可靠观测无疑是一个值得关注和探讨的问题。弹道导弹是一种典型的空间再入飞行器,其沿着预定弹道飞行,速度快,且可发生机动。对于弹道导弹的运动可以用较为简单和基本的运动模型的交互来近似,也可以用地面雷达和光学传感器来观测。将近似运动模型和观测值代入专门针对非线性估计问题的无味卡尔曼滤波(UKF)框架中,再利用数据融合算法,可以实现弹道导弹的航迹估计。文中首先描述了弹道导弹的运动模型,继而给出了不同传感器的量测方程和运动体的基本运动模型,然后阐述了交互多模型(IMM)、UKF及多传感数据融合算法,最后在仿真系统中实现航迹并得出结论。There is no doubt that the reliable observation of a space reentry vehicle is an issue worth deliberating and discussing. The ballistic missile is a typical space reentry vehicle, and it travels along a destined trajectory with a high speed and maneuvers. The motion of the ballistic mission can be approximated by the interaction of the basic motion models that have simpler forms, and can be observed through the use of ground radars and optical sensors. The approximate motion models and observations are brought into the framework of unscented Kalman filter （UKF） who aims at the nonlinear estimation problems specially, and then the fusion algorithms are utilized so that the estimate tracks of the ballistic missile can be implemented. This paper describes the motion model of the ballistic missile firstly, and then the measurement equations of the different sensors and the basic models of the motion body are given out, and then the algorithms of interacting multiple model（IMM）, UKF and multi-sensor data fusion are expatiated on, and finally the estimate tracks are achieved in a simulation system and a conclusion is drawn.

关 键 词：弹道导弹 非线性估计 无味卡尔曼滤波 交互多模型 多传感融合 仿真 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]