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脉冲星非实时平差的火星探测自主导航模型
魏二虎1, 杨洪洲1,2, 张帅1, 刘经南3, 易慧4
1.武汉大学 测绘学院, 武汉 430079;2.卡尔加里大学 测绘工程系, 加拿大 卡尔加里 T2N 1N4;3.武汉大学 卫星导航定位技术研究中心, 武汉 430079;4.北京测绘科学研究院, 北京 100830
摘要:
针对基于X射线脉冲星观测的火星探测器自主导航,研究了几种不同的实时自适应方法,包括:扩展卡尔曼滤波(EKF),自适应扩展卡尔曼滤波(AEKF)和鲁棒自适应扩展滤波(RAEKF).首先根据脉冲星导航原理,模拟了观测值:脉冲到达时刻;接着,分别利用扩展卡尔曼滤波,自适应扩展卡尔曼滤波和鲁棒自适应扩展滤波方法估算出探测器的位置和速度;最后,上述几种滤波轨道与STK模拟的标称轨道较差,然后比较它们的滤波精度发现:AEKF和RAEKF的精度相对较高,AEKF的三个轴向滤波位置精度达到:X轴优于100 m、Y和Z轴优于30 m,优于VLBI技术的km量级,和Doppler技术的精度相当.
关键词:  X射线脉冲星  自主导航  自适应扩展卡尔曼滤波  鲁棒扩展卡尔曼滤波
DOI：10.15982/j.issn.2095-7777.2014.04.009
分类号:
基金项目:国家自然科学基金资助项目(41374012)
Modeling on Autonomous Navigation of Mars Probe with Pulsars and Nonreal-Time Adjustment Methods
WEI Erhu1, YANG Hongzhou1,2, ZHANG Shuai1, LIU Jingnan3, YI Hui4
1.School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;2.Department of Geomatics Engineering, University of Calgary, Calgary T2N 1N4, Canada;3.GNSS Center, Wuhan University, Wuhan 430079, China;4.Chinese Academy of Surveying and Mapping, Beijing 100830, China
Abstract:
To investigate the autonomous navigation of probe orbiting the Mars orbit X-ray pulsars, different real-time adjustment methods are applied in this paper, including the extended Kalman filter (EKF), adaptive extended Kalman filter (AEKF) and robust adaptive extended Kalman filter (RAEKF). Firstly, the observation data, i.e., time of arrival, is simulated according to the principles of pulsars navigation. Secondly, extended Kalman filter (EKF), adaptive extended Kalman filter (AEKF) and robust adaptive extended Kalman filter (RAEKF) are used to get the position and velocity of the probe. Thirdly, the adjustment results are compared with the orbit generated by STK, the results show AEKF and RAEKF performs better than EKF, the result of AEKF is better than the km level of VLBI and can reach the precision level of triple pass Doppler, leveling at 94.1 m in X-axis, 19.5 m in Y-axis and 22.3 m in Z-axis.
Key words:  X-ray pulsars  autonomous navigation  adaptive extended Kalman filter (AEKF)  robust adaptive extended Kalman filter (RAEKF)