An Adaptive Kalman Filter for Mars Spacecraft Approach Phase
-
摘要:在深空探测器实际作业中，由于存在过程噪声与测量噪声，通常使用卡尔曼滤波作为最优估计方法。当深空探测器处于接近段时，探测器加速度急剧变化，导航系统过程噪声不确定性增大，无法准确得知过程噪声协方差。针对上述问题，提出了一种自适应调节协方差矩阵的容积卡尔曼滤波（Adaptive Cubature Kalman Filter Based on System Noise Covariance Adjustment，AQCKF）方法，综合考虑上一时刻过程噪声协方差估计值与此时的过程噪声协方差观测值，利用加权因子在线调整噪声协方差优化滤波，并以火星探测器为例进行仿真，仿真结果显示相较于容积卡尔曼滤波方法（Cubature Kalman Filter，CKF），AQCKF方法的平均位置误差10.235 9 km，平均速度误差0.322 4 m/s。该方法不仅能够解决误差发散的问题，而且还可提升导航系统的稳定性。此外，还分析了加权因子大小对导航性能的影响，有效地解决了深空探测器处于接近段时导航精度降低的问题。Abstract:Celestial navigation technology is a kind of navigation means which is suitable for deep space exploration.It has been widely used in deep space exploration field.In the practical operation of deep space detector, Kalman filter is usually used as the optimal estimation method due to the existence of process noise and measurement noise.When the deep space probe is in the approach section of the orbit, the acceleration of the probe changes sharply, which leads to the increase of the uncertainty of the navigation system process noise, so the process noise covariance cannot be accurately known.To solve these problems, adaptive Cubature Kalman Filter Based on System Noise Covariance Adjustment (AQCKF) is proposed in this paper.In this method, the estimated covariance of process noise at the last moment and the observed covariance of process noise at the present moment are considered comprehensively. The method uses the weighted factor to adjust the noise covariance online, which makes the filtering method more optimized. At the same time, the paper takes the Mars probe as an example to simulate. Simulation results show that compared with Cubature Kalman Filter (CKF), the average position error of AQCKF method is 10.2359 km, and the average velocity error is 0.3224 m/s.This method can not only solve the problem of error divergence, but also improve the stability of navigation system. In addition, the paper also analyzes the influence of weighted factor on navigation performance, which can effectively solve the problem of navigation accuracy reduction when deep space probe is in the approach segment.Highlights
● An adaptive Cubature Kalman Filter Based on System Noise Covariance Adjustment（AQCKF）is proposed by introducing weighting factor. ● It solves the problem of diverging results from CKF methods. ● The influence of weighted factor on navigation accuracy is analyzed and evaluated. -
图 5加权因子系数取值对位置和速度误差影响
Fig. 5Influence of weighting factor coefficient on position and velocity errors
表 1火星探测器轨道参数
Table 1Mars probe orbit parameters
轨道参数 数值 半长轴a’/108km 1.932 偏心率e 0.236 4 轨道倾角i/（°） 23.455 升交点赤经/（°） 0.258 近地点角距/（°） 71.347 真近角点/（°） 85.152 接近火星时间 1997年1月4日 17时03分13.000秒 
下载:
导出CSV
表 2光学传感器参数
Table 2Optical sensor parameter
传感器参数 数值 焦距f/mm 2013.4 视场角 FOV/mrad 10×10 分辨率R/（rad·pixel–1） 10 CCD平面大小/pixel 1 024×1 024 像素大小/m 21 下载:
导出CSV
表 3AQCKF的滤波结果
Table 3AQCKF filtering results
滤波方法 平均位置误差/km 最大位置误差/km 平均速度误差/(m·s–1) 最大速度误差/(m·s–1) AQCKF 10.235 9 162.985 6 0.322 4 10.066 4 下载:
导出CSV
表 4AQCKF的50次仿真结果
Table 450 simulation results of AQCKF
滤波方法 平均位置误差均值/km 最大位置误差均值/km 平均速度误差均值/(m·s–1) 最大速度误差均值/(m·s–1) AQCKF 10.235 9 162.985 6 0.322 4 10.066 4 下载:
导出CSV
表 5加权因子系数对导航性能的影响
Table 5The influence of weighted factor coefficient on navigation performance
w 平均位置误差/km 最大位置误差/km 平均速度误差/(m·s–1) 最大速度误差/(m·s–1) 1 10.007 8 164.803 1 0.342 2 10.109 6 5 10.076 1 162.766 4 0.324 9 10.071 4 10 10.235 9 162.985 6 0.322 4 10.066 4 50 10.5848 163.534 0 0.320 2 10.062 4 100 10.6502 163.641 3 0.319 0 10.061 9 300 10.6497 163.719 2 0.316 5 10.061 5 下载:
导出CSV
-
[1] 房建成, 宁晓琳, 田玉龙. 航天器自主天文导航原理与方法[M]. 北京: 国防工业出版社, 2006.2: 137. [2] 薛喜平,张洪波,孔德庆. 深空探测天文自主导航技术综述[J]. 天文研究与技术,2017,14(3):382-391.doi:10.14005/j.cnki.issn1672-7673.20161108.004XUE X P,ZHANG H B,KONG D Q. Celestial autonomous navigation technology for deep space exploration[J]. Astronomical Research and Technology,2017,14(3):382-391.doi:10.14005/j.cnki.issn1672-7673.20161108.004 [3] BHASKARAN S. Optical navigation for the stardust wild2 encounter[C]//Proceedings of the 18th International Symposium on Space Flight Dynamics . German: ESA, 2004. [4] 戴文战,黄晓姣,沈忱. 带遗忘因子的自适应迭代容积卡尔曼滤波算法[J]. 科技通报,2019,35(1):181-185.doi:10.13774/j.cnki.kjtb.2019.01.036DAI W Z,HUANG X J,SHEN C. Adaptive iterative cubature kalman filtering algorithm with forgetting factor[J]. Bulletin of Science and Technology,2019,35(1):181-185.doi:10.13774/j.cnki.kjtb.2019.01.036 [5] 李建锋,张慧星,闫美辰. 迭代扩展卡尔曼滤波在相对姿态估计中的应用[J]. 导弹与航天运载技术,2012(6):48-52.LI J F,ZHANG H X,YAN M C. Application of Iterative Extended Kalman filter in relative attitude estimation[J]. Missiles and Space Vehicles,2012(6):48-52. [6] JULIER S J,UHLMANN J K. Unscented filtering and nonlinear estimation[J]. Proceeding of the IEEE,2004,92(3):401-422.doi:10.1109/JPROC.2003.823141 [7] JIA B, XIN M, PHAM K, et al. Multiple sensor estimation using a high-degree cubature information filter[C]//Proceedings of Sensors and Systems for Space Applications VI. Baltimore, Maryland: SPIE, 2013. [8] ARASARATNAM I,HAYKIN S. Cubature Kalman filters[J]. IEEE Transactions on Automatic Control,2009,54(6):1254-1269.doi:10.1109/TAC.2009.2019800 [9] BAR S Y, LI X R, KIRUBARAJAN T. Estimation with application to tracking and navigation[M]. New York: John Wiley & Sons Inc, 2001. [10] 吴伟仁, 王大轶, 宁晓琳. 深空探测器自主导航原理与技术[M]. 北京: 中国宇航出版社, 2011: 122.WU W R, WANG D Y, NING X L. Principle and technology of autonomous navigation of deep space probe[M]. Beijing: China Aerospace Press, 2011: 122. [11] 张文佳,马辛. 深空探测器接近段自主导航的滑动窗口自适应滤波方法[J]. 上海交通大学学报,2022,56(11):1461-1469.doi:10.16183/j.cnki.jsjtu.2022.233ZHANG W J,MA X. A sliding window adaptive filtering method for autonomous navigation of deep space probe in approach segment[J]. Journal of Shanghai Jiaotong University,2022,56(11):1461-1469.doi:10.16183/j.cnki.jsjtu.2022.233 [12] 王广玉,窦磊,窦杰. 基于自适应卡尔曼滤波的多目标跟踪算法[J]. 计算机应用,2022,42(S1):271-275.WANG G Y,DOU L,DOU J. Multi-target tracking algorithm based on adaptive Kalman filter[J]. Journal of Computer Applications,2022,42(S1):271-275. [13] 宁晓琳,李卓,黄盼盼,等. 火星探测器捕获段自适应卡尔曼滤波方法[J]. 深空探测学报(中英文),2016,3(3):237-245.NING X L,LI Z,HUANG P P,et al. Adaptive Kalman filtering method for acquisition segment of Mars probe[J]. Journal of Deep Space Exploration,2016,3(3):237-245. [14] 丁家琳,肖建. 基于极大后验估计的自适应容积卡尔曼滤波器[J]. 控制与决策,2014,29(2):327-334.doi:10.13195/j.kzyjc.2012.1770DING J L,XIAO J. Adaptive cubature kalman filter based on maximum a posteriori estimation[J]. Control and Decision,2014,29(2):327-334.doi:10.13195/j.kzyjc.2012.1770 [15] ZHOU Q F,ZHANG H,LI Y,et al. An adaptive low cost GNSS/MEMS-IMU tightly coupled integration system with aiding measurement in a GNSS signal challenged environment[J]. Sensors,2015,15(9):23953-23982.doi:10.3390/s150923953 [16] HU C W,CHEN W. Adaptive Kalman filtering for vehicle navigation[J]. Journal of Global Positioning System,2003,2(1):227-233. [17] 张文玲,朱明清,陈宗海. 基于强跟踪UKF的自适应SLAM算法[J]. 机器人,2010,32(2):190-195.doi:10.13973/j.cnki.robot.2010.02.013ZHANG W L,ZHU M Q,CHEN Z H. Adaptive SLAM algorithm based on strong tracking UKF[J]. Robot,2010,32(2):190-195.doi:10.13973/j.cnki.robot.2010.02.013 [18] 张抒扬,董鹏,敬忠良. 变分贝叶斯自适应容积卡尔曼的SLAM算法[J]. 哈尔滨工业大学学报,2019,51(4):12-18.doi:10.11918/j.issn.0367-6234.201801013ZHANG S Y,DONG P,JING Z L. SLAM algorithm based on Bayesian adaptive volume Kalman[J]. Journal of Harbin Institute of Technology,2019,51(4):12-18.doi:10.11918/j.issn.0367-6234.201801013 [19] LEE D J, ALFRIEND K. Adaptive sigma point filtering for state and parameter estimation[C]//AIAA/AAS Astrodynamics Specialist Conference and Exhibit. Providence, Rhode Island: AIAA, 2004. [20] BUSSE F D,HOW J P,SIMPSON J. Demonstration of adaptive extended Kalman filter for low-Earth-orbit formation estimation using CDGPS[J]. Navigation,2003,50(2):79-93.doi:10.1002/j.2161-4296.2003.tb00320.x -
下载:
计量
- 文章访问数:21
- 被引次数:0
