论文部分内容阅读
以配平攻角状态再入的不变结构半弹道式再入飞行器(Semi-ballistic reentry vehicle,SBRV)不同于传统的弹道式再入飞行器(Ballistic reentry vehicle,BRV)和机动再入飞行器(Maneuvering reentry vehicle,MaRV),本文分析了该飞行器再入特征,提出了新的模型并分析了该模型与传统再入模型间的关系.对该再入问题的多模型混合状态估计器引入了F-均匀模型集与期望模式补偿(Expected-mode augmentation,EMA)集.根据SBRV的圆柱体状模式空间的需求,文中扩展了现有的方法以设计F-均匀模型集,进而提出一种EMA集的实现形式.前者在分布最小失配意义下使估计器最优;后者相比于前者具有更高的估计精度.仿真结果表明,相比于传统Monte-Carlo法生成的模型集,在模型集势相当的情况下这两种模型集对不变结构SBRV再入的初始阶段有更高的模式估计的精度,在该飞行器状态变化剧烈时有更高的混合状态估计精度.
The semi-ballistic reentry vehicle (SBRV), which reentry at a flat angle of attack, is different from the traditional ballistic reentry vehicle (BRV) and Maneuvering reentry This paper analyzes the reentry characteristics of the aircraft, proposes a new model and analyzes the relationship between the model and the traditional reentry model. The multi-model hybrid state estimator of the reentry problem introduces the F-uniform Model set and Expected-mode augmentation (EMA) set.According to the requirement of SBRV cylinder-like pattern space, we expand the existing methods to design F-uniform model sets, and then propose an EMA set implementation Form.The former optimizes the estimator in the sense of minimum mismatch of distribution.The latter has higher estimation accuracy than the former.The simulation results show that compared with the model set generated by the traditional Monte-Carlo method, Equivalently, both models have higher accuracy of pattern estimation for the initial stage of invariant structure SBRV reentry and have higher accuracy of hybrid state estimation when the aircraft state changes dramatically .