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Direction/Distance/Velocity Measurements Deeply Integrated Navigation for Venus Capture Period

Published online by Cambridge University Press:  05 February 2018

Jin Liu ,
Xiao-lin Ning ,
Xin Ma ,
Jian-cheng Fang  and
Gang Liu
Jin Liu*
Affiliation:
(College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, People's Republic of China) (School of Instrumentation Science & Opto-electronics Engineering, Beihang University (BUAA), Beijing 100191, People's Republic of China)
Xiao-lin Ning
Affiliation:
(School of Instrumentation Science & Opto-electronics Engineering, Beihang University (BUAA), Beijing 100191, People's Republic of China)
Xin Ma
Affiliation:
(School of Instrumentation Science & Opto-electronics Engineering, Beihang University (BUAA), Beijing 100191, People's Republic of China)
Jian-cheng Fang
Affiliation:
(School of Instrumentation Science & Opto-electronics Engineering, Beihang University (BUAA), Beijing 100191, People's Republic of China)
Gang Liu
Affiliation:
(School of Instrumentation Science & Opto-electronics Engineering, Beihang University (BUAA), Beijing 100191, People's Republic of China)
*
(E-mail: liujin@wust.edu.cn)
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Abstract

In the Venus capture period, it is difficult for celestial autonomous navigation to satisfy the requirement of high precision. To improve autonomous navigation performance, a Direction, Distance and Velocity (DDV) measurements deeply integrated navigation method is proposed. The “deeply” integrated navigation reflects the fact that the direction and velocity measurements suppress the Doppler effects in the pulsar signals. In the pulsar observation period, the direction and velocity measurements are utilised to compensate for Doppler effects in the pulsar signals. By these means, the residual effects can be ignored. When the direction, distance or velocity measurements are obtained, they are fused to improve the navigation performance. Simulation results demonstrate that the DDV measurements deeply integrated navigation filter converges very well, and provides highly accurate position estimation without a high quality requirement on navigation sensors.

Keywords

Venus explorationDirection/Distance/Velocity

Information

Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 4 , July 2018 , pp. 861 - 877
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Bucy, R.S. and Renne, K.D. (1971). Digital synthesis of nonlinear filter. Automatica, 7, 287–289.CrossRefGoogle Scholar
Emadzadeh, A.A. and Speyer, J.L. (2011). X-ray pulsar-based relative navigation using epoch folding. IEEE Transactions on Aerospace and Electronic Systems, 47, 2317–2328.Google Scholar
Fang, B.D., Wu, M.P. and Zhang, W. (2015). Venus gravity capture dynamic model and error analysis. Chinese Journal of Theoretical and Applied Mechanics, 47, 15–23.Google Scholar
Guo, Y. (1999). Self-contained autonomous navigation system for deep space mission. AAS/AIAA Space Flight Mechanics Meeting, Breckenridge, CO, USA.Google Scholar
Hamption, D.L, Baer, J.W., Huisjen, M.A, Varner, C.C., Delamere, A., Wellnitz, D.D., Ahearn, M.F. and Klaasen, K.P. (2005). An Overview of the Instrument Suite for the Deep Impact Mission. Space Science Reviews, 117, 43–93.Google Scholar
Li, T., Liu, J., Huang, Z. and Qin, H. (2010). Observability of HEO satellite autonomous navigation system using GPS[C]//Multimedia Technology (ICMT), 2010 International Conference on. IEEE: 1–4.Google Scholar
Liechty, D. (2007). Aeroheating analysis for the Mars reconnaissance orbiter with comparison to flight data. Journal of Spacecraft and Rocket, 44, 1226–1231.CrossRefGoogle Scholar
Liever, P., Habchi, S., Burnell, S. and Lingard, J.S. (2003). Computational fluid dynamics prediction of the Beagle 2 aerodynamic database. Journal of Spacecraft and Rocket, 40, 632–638.Google Scholar
Liu, J., Fang, J.C., Kang, Z.W., Wu, J., and Ning, X.L. (2015). Novel algorithm for X-ray pulsar navigation against Doppler effects. IEEE Transactions on Aerospace and Electronic Systems, 51, 228–241.CrossRefGoogle Scholar
Liu, J., Fang, J.C., Ning, X.L., Wu, J. and Kang, Z.W. (2014). Closed-loop EKF-based pulsar navigation for Mars explorer with Doppler effects. Journal of Navigation, 67, 776–790.Google Scholar
Liu, J., Kang, Z.W., White, P., Ma, J. and Tian, J.W. (2011). Doppler/XNAV-integrated navigation system using small-area X-ray sensor, IET Radar, Sonar and Navigation. 5, 1010–1017Google Scholar
Long, A.C., Leung, D., Folta, D. and Gramling, C. (2000). Autonomous navigation of high-earth satellites using celestial objects and Doppler measurements. AIAA/AAS Astrodynamics Specialist Conf., Denver, CO, USA, 1–9.Google Scholar
Lu, F.J. (2009). The Hard X-ray modulation Telescope (HXMT) Mission. AAPPS Bulletin, 19(2), 36–38.Google Scholar
Milsom, D. and Peterson, G. (2001). Stray Light Design and Analysis of JPL's Optical Navigation Camera. BRO Report, 4800.Google Scholar
Ning, X.L. and Fang, J.C. (2005). A New Autonomous Celestial Navigation Method for Deep Space Probe and Its Observability Analysis. Chinese Journal of Space Science, 25, 286–292.Google Scholar
Ning, X.L. and Fang, J.C. (2007). An autonomous celestial navigation method for LEO satellite based on unscented Kalman filter and information fusion. Aerospace Science and Technology, 11, 222–228.Google Scholar
Qiao, L., Liu, J., Zheng, G. and Xiong, Z. (2009). Augmentation of XNAV system to an ultraviolet sensor-based satellite navigation system. IEEE Selected Topics in Signal Processing, 3, 777–785.Google Scholar
Riedel, J.E., Bhaskaran, S., Desai, S., Hand, D., Kennedy, B., McElrath, T. and Ryne, M. (2000). Autonomous optical navigation (AutoNav) DS1 technology validation report. JPL Publication. 00–10.Google Scholar
Sheikh, S.I., Pines, D.J. (2006). Recursive estimation of spacecraft position and velocity using X-ray pulsar time of arrival measurement. Navigation: Journal of The Institute of Navigation, 53(3), 149–166.Google Scholar
Sheikh, S.I., Pines, D.J., Ray, P.S., Wood, K.S., Lovellette, M.N. and Wolff, M.T. (2006). Spacecraft navigation using X-ray pulsars. Journal of Guidance, Control and Dynamics, 29(1), 49–63.Google Scholar
Simon, D. (2006). Optimal state estimation: Kalman, H∞, and nonlinear approaches. Wiley, USA.CrossRefGoogle Scholar
Smith, R.S. and Hadaegh, F.Y. (2005). Control of Deep-Space Formation-Flying Spacecraft; Relative Sensing and Switched Information. Journal of Guidance, Control, and Dynamics, 28, 106–114.Google Scholar
Steiner III, T.J. (2012). A unified vision and inertial navigation system for planetary hoppers. Dissertation, Massachusetts Institute of Technology.Google Scholar
Sunahara, Y. (1970). An approximate method of state estimation for nonlinear dynamical systems. Transactions of the ASME, Series D, Journal of Basic Engineering, 92(2), 385–393.Google Scholar
Xiong, K., Wei, C.L. and Liu, L.D. (2015). Robust multiple model adaptive estimation for spacecraft autonomous navigation. Aerospace Science and Technology, 42(1), 249–258.Google Scholar
Xu, W.M., Cui, H.T., Cui, P.Y. and Liu, Y.F. (2007). Selection and planning of asteroids for deep space autonomous optical navigation. Acta Aeronauticaet Astronautica Sinica, 28(4), 891–896.Google Scholar
Zhang, X.W., Wang, D.Y., and Huang, X.Y. (2009). Study on the selection of the beacon asteroids in autonomous optical navigation for interplanetary exploration. Journal of Astronautics, 30(3), 947–952.Google Scholar