SCIENCE CHINA Information Sciences, Volume 60 , Issue 3 : 032205(2017) https://doi.org/10.1007/s11432-016-0405-2

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

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  • ReceivedJul 14, 2016
  • AcceptedAug 25, 2016
  • PublishedFeb 8, 2017


Funded by

National Natural Science Foundation of China(61503013)

National Basic Research Program of China(973)

"source" : null , "contract" : "2014CB744206"}]

National Natural Science Foundation of China(61233005)



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.


[1] Devereux W, Moskowitz S. Trans-stellar space navigation. AIAA J, 1968, 6: 1021-1029 CrossRef 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 CrossRef 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 CrossRef 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 CrossRef 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 CrossRef 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 CrossRef Google Scholar

[13] Bellantoni J, Dodge K. A square root formulation of the Kalman-Schmidt filter. AIAA J, 1967, 5: 1309-1314 CrossRef Google Scholar

[14] Simon D. Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. Hoboken: John Wiley & \linebreak Sons, 2006. 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 CrossRef 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 CrossRef 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 CrossRef 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 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 CrossRef Google Scholar

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

[35] Jacobson R A. The orbits of the Martian satellites. Bull Am Astron Soc, 2008, 40: 481-44 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 CrossRef 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 CrossRef Google Scholar

[38] Koch K R, Kusche J. Regularization of geopotential determination from satellite data by variance components. J Geodesy, 2002, 76: 259-268 CrossRef Google Scholar

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

[43] Karlsson R, Schon T, Gustafsson F. Complexity analysis of the marginalized particle filter. IEEE Trans Signal Process, 2004, 53: 4408-4411 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 CrossRef Google Scholar