深空探测器接近段自适应滤波方法研究

宁晓琳; 李卓; 黄盼盼; 杨雨青

doi:10.11728/cjss2017.03.322

深空探测器接近段自适应滤波方法研究

doi: 10.11728/cjss2017.03.322

1. 北京航空航天大学仪器科学与光电工程学院北京 100191;
2. School of Civil and Environmental Engineering, University of New South Wales, Sydney 2052, Australia

基金项目:

国家自然科学基金项目资助（61233005，61503013）

详细信息

中图分类号: P353
计量
- 文章访问数: 1094
- HTML全文浏览量: 94
- PDF下载量: 1105
- 被引次数: 0
出版历程
- 收稿日期: 2016-04-23
- 修回日期: 2016-11-11
- 刊出日期: 2017-05-15

Comparison of Adaptive Filter for Celestial Navigation during Approach Phase

1. School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191;
2. School of Civil and Environmental Engineering, University of New South Wales, Sydney 2052, Australia

摘要

摘要: 天文导航是一种广泛应用于深空探测任务的全自主导航方法.基于状态模型和量测模型的非线性卡尔曼滤波方法在天文导航中被广泛使用.卡尔曼滤波要求状态和量测模型误差是高斯白噪声且先验协方差信息已知，但在深空探测器天文导航系统中，状态模型和量测模型噪声通常不能精确知道且是时变的.因此，自适应卡尔曼滤波器广泛用于解决状态和量测模型误差未知且时变的问题.本文首先结合火星探测器接近段的实际情况分析了火星探测器接近段模型噪声的时变特性，然后对三种常用的在线调节自适应滤波方法在火星探测接近段的滤波表现进行了仿真实验.
- 天文导航 /
- 自适应滤波 /
- 深空探测接近段
Abstract: Celestial navigation is an autonomous navigation method for deep space mission, which has been wildly researched. In celestial navigation, nonlinear Kalman filters such as Unscented Kalman Filter (UKF) are usually used. However, for traditional nonlinear Kalman filter, the system model errors should be Gaussian noise and their statistical characteristics should be known. However, the information is usually unknown and the system model errors are usually time-varying in real system. Hence, many adaptive filters have been studied to solve this problem. In this paper, three adaptive filters based on adjusting the predicted state covariance matrix are compared by simulation. Before this, the system model errors of celestial navigation are also analyzed for the approach phase.
- Celestial navigation /
- Adaptive filter /
- Approach phase

HTML全文

参考文献(20)

[1]	BHASKARAN S, DESAI S D, DUMONT P J, et al. Orbit determination performance evaluation of the Deep Space 1 autonomous navigation system[C]//Spaceflight Mecha-nics. Monterey, California, 1998:1295-1314
[2]	BHASKARAN S. Optical Navigation for Stardust Wild 2 Encounter[C]//AAS/AIAA Spaceflight Mechanics Conference. Maui, Hawaii: AIAA, 1998
[3]	FRAUENHOLZ R B, BHAT R S, MASTRODEMOS N, et al. Deep impact navigation system performance[J]. J. Spacecraft Rockets, 2008, 45(1):39-56
[4]	NING Xiaolin, HUANG Panpan, FANG Jiancheng. A new celestial navigation method for spacecraft on a gravity assist trajectory[J]. Math. Prob. Eng., 2013, 2013(1):950675
[5]	LJUNG L. Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems[J]. IEEE Trans. Autom. Cont., 1979, 24(1):36-50
[6]	JULIER S J, UHLMANN J K. New extension of the Kalman filter to nonlinear systems[C]//Proceeding of SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI. Orlando: International Society for Optics and Photonics, 1997:182-193
[7]	NING X, FANG J. Spacecraft autonomous navigation using unscented particle filter-based celestial/Doppler information fusion[J]. Meas. Sci. Technol., 2008, 19(9): 095203
[8]	MA P, JIANG F, BAOYIN H. Autonomous navigation of Mars Probes by combining optical data of viewing Martian Moons and SST data[J]. J. Navig., 2015, 68(6):1019-1040
[9]	HANLON P D, MAYBECK P S. Multiple-model adaptive estimation using a residual correlation Kalman filter bank[J]. IEEE Trans. Aerospace Electron. Syst., 2000, 36(2):393-406
[10]	GENG Y, WANG J. Adaptive estimation of multiple fa-ding factors in Kalman filter for navigation applica-tions[J]. GPS Solut., 2008, 12(4):273-279
[11]	CAI X, QIU A, QIAN W X, et al. Research on MEMS gyro random drift restraining based on simplified Sage-Husa adaptive filter algorithms[J]. Adv. Mater. Res., 2011, 403-408:127-131
[12]	LU P, ZHAO L, CHEN Z. Improved Sage-Husa adaptive filtering and its application[J]. J. Syst. Simul., 2007, 15:3503-3505
[13]	XU J S, QIN Y Y, PENG R. New method for selecting adaptive Kalman filter fading factor[J]. Syst. Eng. Electron., 2004, 11:006
[14]	YANG Y, GAO W. Comparison of adaptive factors in Kalman filters on navigation results[J]. J. Navig., 2005, 58(3):471-478
[15]	DING W, WANG J, KINLYSIDE C R D. Improving adaptive Kalman estimation in GPS/INS integration[J]. J. Navig., 2007, 60(3):517-529
[16]	HU C, CHEN Y, CHEN W, et al. Adaptive Kalman filtering for DGPS positioning[C]//International Symposium on Kinematic Systems in Geodesy, Geomatics and Navigation, 2001
[17]	KOCH K R, KUSCHE J. Regularization of geopotential determination from satellite data by variance components[J]. J. Geod., 2002, 76(5):259-268
[18]	JACOBSON R A. The orbits and masses of the Martian satellites and the libration of Phobos[J]. Astron. J., 2010, 139(2):668
[19]	KAASALAINEN M, TANGA P. Photocentre offset in ultraprecise astrometry: implications for barycentre determination and asteroid modeling[J]. Astron. Astrophys., 2004, 416(1):367-373
[20]	SIMON D. Optimal State Estimation: Kalman, H Infi-nity, and Nonlinear Approaches[M]. John Wiley & Sons, 2006