Skip to main content
SpringerLink
Log in

Recursive adaptive filter using current innovation for celestial navigation during the Mars approach phase

火星探测器接近段递推自适应卡尔曼滤波方法

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Celestial navigation is a commonly used autonomous navigation technique for deep space navigation. A nonlinear filter such as the unscented Kalman filter (UKF) is typically applied in a celestial navigation system (CNS). However, on account of being subject to a number of factors, such as ephemeris errors and centroid determination, the measurement model error of a CNS cannot be accurately determined. The analysis conducted in this study also shows that the measurement model error is time-variant during the Mars approach phase. This implies that covariance matrix of the measurement error R is usually inaccurate, which may induce large estimation errors that even result in filter divergence. Some adaptive methods are able to address this issue. However, traditional adaptive filters, for scaling R, usually require a sequence of innovation and are affected by the statistic window size. A new recursive adaptive UKF (RAUKF) is proposed in this paper, which only uses current innovation to scale R. The navigation performance of the proposed RAUKF method is compared with some traditional adaptive filters through simulations. The results show that this method is better than traditional adaptive filters in a CNS during the Mars approach phase.

创新点

  1. 1.

    本文对火星卫星由于质心提取引起的量测误差和火星卫星星历误差引起的量测误差进行了分析, 分析结果表明, 在火星探测器接近段, 量测模型误差是未知的时变噪声。

  2. 2.

    针对量测模型噪声方差阵(R)的时变特性, 本文提出了一种递推的自适应卡尔曼滤波方法(RAUKF), 该方法仅利用当前时刻的新息信息即可对R阵进行调节。

  3. 3.

    本文将RAUKF方法与传统无迹卡尔曼滤波(UKF), Sage-Husa滤波, 及基于新息的自适应滤波方法(AUKF)的导航精度进行了对比, 仿真结果表明, 与其他三种方法对比, 该方法可以获得最高的导航精度。

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Devereux W, Moskowitz S. Trans-stellar space navigation. AIAA J, 1968, 6: 1021–1029

    Article  Google Scholar 

  2. Prestage J D, Weaver G L. Atomic clocks and oscillators for deep-space navigation and radio science. Proc IEEE, 2007, 95: 2235–2247

    Article  Google Scholar 

  3. Crouse B, Zanetti R, D’Souza C, et al. Autonomous optical lunar navigation. In: Proceedings of AAS/AIAA 19th Space Flight Mechanics Conference, Georgia, 2009. 1–3

    Google Scholar 

  4. Takashi K, Tatsuaki H, Shujiro S, et al. An autonomous navigation and guidance system for MUSES-C asteroid landing. Acta Astronaut, 2003, 52: 125–131

    Article  Google Scholar 

  5. Riedel J E, Bhaskaran S, Synnott S P, et al. Navigation for the new millennium: autonomous navigation for Deep Space 1. In: Proceedings of 12nd International Symposium on Space Flight Dynamics, Darmstadt, 1997. 303–320

    Google Scholar 

  6. Bhaskaran S, Desai S D, Dumont P J, et al. Orbit determination performance evaluation of the Deep Space 1 autonomous navigation system. In: Prceedings of the AAS/AIAA Spaceflight Mechanics Meeting, Monterey, 1998. 1295–1314

    Google Scholar 

  7. Mastrodemos N, Kubitschek D G, Synnott S P. Autonomous navigation for the deep impact mission encounter with comet Tempel 1. Space Sci Rev, 2005, 117: 95–121

    Article  Google Scholar 

  8. Frauenholz R B, Bhat R S, Chesley S R, et al. Deep impact navigation system performance. J Spacecraft Rockets, 2008, 45: 39–56

    Article  Google Scholar 

  9. Bhaskaran S. Optical navigation for stardust wild 2 encounter. In: Proceedings of AAS/AIAA Space Flight Mechanics Conference, Maui, 1998. 455–460

    Google Scholar 

  10. Gillam S D, Owen W, Vaughan A, et al. Optical navigation for the Cassini/Huygens Mission. Adv Astronaut Sci, 2008, 129: 3–6

    Google Scholar 

  11. Owen W M, Duxbury T C, Acton C H, et al. A brief history of optical navigation at JPL. Adv Astronaut Sci, 2008, 131: 329–348

    Google Scholar 

  12. Patricia M B, James A C. Guidance, navigation, and control technology assessment for future planetary science mission. J Guid Control Dynam, 2015, 38: 1165–1186

    Article  Google Scholar 

  13. Bellantoni J, Dodge K. A square root formulation of the Kalman-Schmidt filter. AIAA J, 1967, 5: 1309–1314

    Article  Google Scholar 

  14. Simon D. Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. Hoboken: John Wiley & Sons, 2006

    Book  Google Scholar 

  15. Julier S J, Uhlmann J K. New extension of the Kalman filter to nonlinear systems. In: Proceedings of SPIE, the International Society for Optical Engineering. Bellingham: SPIE, 1997. 182–193

    Google Scholar 

  16. Arasaratnam I, Haykin S. Cubature Kalman filters. IEEE Trans Automat Contr, 2009, 54: 1254–1269

    Article  MathSciNet  MATH  Google Scholar 

  17. Tang X, Liu Z, Zhang J. Square-root quaternion cubature Kalman filtering for spacecraft attitude estimation. Acta Astronaut, 2012, 76: 84–94

    Article  Google Scholar 

  18. Zhang L, Yang H, Lu H, et al. Cubature Kalman filtering for relative spacecraft attitude and position estimation. Acta Astronaut, 2014, 105: 254–264

    Article  Google Scholar 

  19. Doody D. Deep Space Craft: an Overview of Interplanetary Flight. New York: Springer Berllin Heidelberg, 2010

    Google Scholar 

  20. Zhao H, Gao S, He Z, et al. Identification of nonlinear dynamic system using a novel recurrent wavelet neural network based on the pipelined architecture. IEEE Trans Ind Electron, 2014, 61: 4171–4182

    Article  Google Scholar 

  21. Lu L, Zhao H, Chen B. Improved variable forgetting factor recursive algorithm based on the logarithmic cost for Volterra system identification. IEEE Trans Circ Syst II, 2016, 63: 588–592

    Google Scholar 

  22. Mohamed A, Schwarz K. Adaptive Kalman filtering for INS/GPS. J Geodesy, 1999, 73: 193–203

    Article  MATH  Google Scholar 

  23. Hide C, Moore T, Smith M. Adaptive Kalman filtering for low-cost INS/GPS. J Navigation, 2003, 56: 143–152

    Article  Google Scholar 

  24. Cheng Z, He X, Jiang H, et al. Research on initial alignment for large azimuth misalignment angle with Sage Husa adaptive filtering. In: Proceedings of 25th Chinese Control and Decision Conference, Guiyang, 2013. 1744–1749

    Google Scholar 

  25. Xu T, Jiang N, Sun Z. An improved adaptive Sage filter with applications in GEO orbit determination and GPS kinematic positioning. Sci China Phys Mech Astron, 2012, 55: 892–898

    Article  Google Scholar 

  26. Ding W, Wang J, Rizos C, et al. Improving adaptive Kalman estimation in GPS/INS integration. J Navigation, 2007, 60: 517–529

    Article  Google Scholar 

  27. Ning X, Huang P, Fang J, et al. An adaptive filter method for spacecraft using gravity assist. Acta Astronaut, 2015, 109: 103–111

    Article  Google Scholar 

  28. Li Z. Error Analysis and Treatment for Deep Space Celestial Navigation during Approach Phase. Beijing: Beihang University Press, 2016

    Google Scholar 

  29. McMahon J W, Scheeres D J. New solar radiation pressure force model for navigation. J Guid Contr Dynam, 2010, 33: 1418–1428

    Article  Google Scholar 

  30. Müller T, Sekiguchi T, Kaasalainen M, et al. Thermal infrared observations of the Hayabusa spacecraft target asteroid 25143 Itokawa. Astron Astrophys, 2005, 443: 347–355

    Article  Google Scholar 

  31. Yeomans D, Barriot J P, Dunham D, et al. Estimating the mass of asteroid 253 Mathilde from tracking data during the NEAR flyby. Science, 1997, 278: 2106–2109

    Article  Google Scholar 

  32. Kaasalainen M, Tanga P. Photocentre offset in ultraprecise astrometry: implications for barycentre determination and asteroid modelling. Astron Astrophys, 2004, 416: 367–373

    Article  Google Scholar 

  33. Lowman A E, Stauder J L. Stray light lessons learned from the Mars reconnaissance orbiter’s optical navigation camera. In: Proceedings of the SPIE 49th Annual Conference on Optical Science and Technology. Bellingham: SPIE, 2004. 240–248

    Google Scholar 

  34. Jacobson R, Lainey V. Martian satellite orbits and ephemerides. Planet Space Sci, 2014, 102: 35–44

    Article  Google Scholar 

  35. Jacobson R A. The orbits of the Martian satellites. Bull Am Astron Soc, 2008, 40: 481

    Google Scholar 

  36. Jacobson R A. The orbits and masses of the Martian satellites and the libration of Phobos. Astronomical J, 2010, 139: 668–679

    Article  Google Scholar 

  37. Crocetto N, Gatti M, Russo P. Simplified formulae for the BIQUE estimation of variance components in disjunctive observation groups. J Geodesy, 2000, 74: 447–457

    Article  MATH  Google Scholar 

  38. Koch K R, Kusche J. Regularization of geopotential determination from satellite data by variance components. J Geodesy, 2002, 76: 259–268

    Article  MATH  Google Scholar 

  39. Koch K R. Parameter Estimation and Hypothesis Testing in Linear Models. New York: Springer-Verlag, 1999

    Book  MATH  Google Scholar 

  40. Deng X M, Fan M, Xie Y. Comparisons and evaluations of JPL ephemerides. Astron Astrophys Sinica, 2013, 38: 330–341

    Google Scholar 

  41. Acton C, Backman N, Elson L, et al. Extending NASA’s SPICE ancillary information system to meet future mission needs. In: Proceedings of AIAA Space Operations Conference, Houston, 2002. 1–9

    Google Scholar 

  42. Zhao H, Zhang J. A novel adaptive nonlinear filter-based pipelined feedforward second-order Volterra architecture. IEEE Trans Signal Process, 2009, 57: 237–246

    Article  MathSciNet  Google Scholar 

  43. Karlsson R, Schon T, Gustafsson F. Complexity analysis of the marginalized particle filter. IEEE Trans Signal Process, 2004, 53: 4408–4411

    Article  MathSciNet  Google Scholar 

  44. Hu G, Gao S, Zhong Y. A derivative UKF for tightly coupled INS/GPS integrated navigation. ISA Trans, 2015, 56: 135–144

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61233005, 61503013), National Basic Research Program of China (973) (Grant No. 2014CB744206). The authors express their gratitude to all the members of the Science & Technology on Inertial Laboratory, Fundamental Science on Novel Inertial Instrument & Navigation System Technology Laboratory for their valuable comments.

Author information

Authors and Affiliations

  1. School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Beijing, 100191, China

    Xiaolin Ning, Zhuo Li, Yuqing Yang, Jiancheng Fang & Gang Liu

  2. Lunar Exploration and Space Program Center, China National Space Administration, Beijing, 100037, China

    Weiren Wu

Authors
  1. Xiaolin Ning
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhuo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weiren Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuqing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiancheng Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuqing Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ning, X., Li, Z., Wu, W. et al. Recursive adaptive filter using current innovation for celestial navigation during the Mars approach phase. Sci. China Inf. Sci. 60, 032205 (2017). https://doi.org/10.1007/s11432-016-0405-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-016-0405-2

Keywords

Keywords

Search

Navigation