| 摘要: |
| 由于大气密度、气动参数、突风和沙尘暴等因素的影响,火星探测器在进入段高速飞行的动力学模型中往往带来未知输入,这些未知输入使传统的滤波方法出现较大的偏差。研究采用一种新的自校准扩展Kalman滤波方法,对火星进入段的探测器进行状态估计,可以成功地消除这些未知输入带来的影响。数值仿真结果表明,该方法能有效提高导航精度。 |
| 关键词: 火星探测 火星进入段 未知输入 状态估计 自校准扩展Kalman滤波 |
| DOI：10.15982/j.issn.2095-7777.2015.03.006 |
| 分类号: |
| 基金项目:国家重点基础研究发展计划 (2012CB720000) |
|
| Self-Calibration Estimation of Mars Vehicle in its Entry Phase |
|
FU Huimin, LOU Taishan, XIAO Qiang
|
|
Research Center of Small Sample Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
|
| Abstract: |
| There always are unknown inputs in the dynamic model of high-speed Mars vehicle during the Mars entry phase because of the atmospheric density, aerodynamic parameters, gust and sand storm, etc. Due to the effect of the unknown inputs, the traditional filtering methods may produce greater errors. This paper estimates the states of the Mars entry vehicle by using a new self-calibration extended Kalman filter, and successfully eliminates the effect of the unknown inputs. Numerical simulation shows that this self-calibration method may effectively improve navigation accuracy. |
| Key words: Mars exploration Mars entry unknown input state estimation self-calibration extended Kalman filter |
