An Adaptive Kalman Filter for Mars Spacecraft Acquisition Phase

Abstract

Celestial navigation is an energy saving and efficient way of autonomous navigation for deep space probes. Kalman filter has been successfully applied in the Celestial navigation system. During the acquisition phase, due to the complex dynamic environment, unmolded acceleration error and the ephemeris error etc. may cause the statistics of process noise uncertainty. To overcome the problem, a method named AQSCKF based on the trend of the innovation sequences and residual sequences to scale the process noise covariance matrix is proposed in this paper. In the first place, it calculates the scale factor based on the innovation and residual separately. Then, by comparing the trend of the two factors, the scale factor of the new method is set as the average. In addition, the navigation performance of traditional SCKF, the method only using innovation or residual to scale Q and AQSCKF is also compared by simulation. The simulation results show that the new method yields better performance than the traditional methods in solving the problem caused by the uncertainty of the process noise, furthermore it also shows a good stability.

Key words： adaptive filter; celestial navigation; process noise; Kalman filter

References

[1] Bhaskaran S. Navigation of the Deep Space 1 spacecraft at Borrelly[Z]. AIAA paper,2002,4815:5-8.
[2] Bhaskaran S. Optical navigation for the stardust wild 2 encounter[C]//18th International Symposium on Space Flight Dynamics. German:[s. n.],2004.
[3] Frauenholz R B. Deep impact navigation system performance[J]. Journal of Spacecraft and Rockets,2008,45(1):39-56.
[4] Bhaskaran S. Orbit determination performance evaluation of the deep space 1 autonomous navigation system[C]//AIAA/AAS Space Flight Mechanics Meeting. Monterey:AIAA,1998.
[5] Christian J A,Lightsey E G. Integrated performance of an autonomous optical navigation system for space exploration[J]. Navigation,2010,2009:1.
[6] Woodard M. Orbit Determination of spacecraft in Earth-Moon L1 and L2 Libration Point Orbits[C]//AAS/AIAA Astrodynamics Specialist Conference.[s. l.]:AAS/AIAA,2011
[7] Julier S J,Uhlmann J K. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE,2004,92(3):401-422.
[8] Arasaratnam I,Haykin S. Cubature kalman filters[J]. Automatic Control,IEEE Transactions on. 2009,54(6):1254-1269.
[9] Ge Q B,Li W B,Wen C L. SCKF-STF-CN:a universal nonlinear filter for maneuver target tracking[J]. Journal of Zhejiang University SCIENCE C. 2011,12(8):678-686.
[10] Brunke S,Campbell M. Square root sigma point filtering for real-time,nonlinear estimation[J]. Journal of Guidance,Control,And Dynamics,2004,27(2):314-317.
[11] Ning X. Analysis of filtering methods for satellite autonomous orbit determination using celestial and geomagnetic measurement[J]. Mathematical Problems in Engineering,2012,2012:1-6.
[12] Tang X,Wei J,Chen K. Square-root adaptive cubature Kalman filter with application to spacecraft attitude estimation. in Information Fusion(FUSION)[C]//201215th International Conference on.[S.l.]:IEEE,2012.
[13] Stastny N B,Geller D K. Autonomous optical navigation at Jupiter:a linear covariance analysis[J]. Journal of Spacecraft and Rockets,2008,45(2):290-298.
[14] Lyzhoft J. GPU-Based Optical Navigation and Terminal Guidance Simulation of a Hypervelocity Asteroid Intercept Vehicle[C]//AIAA Cuidance,Navigation and Control(GNC)Conference. Washington DC:AIAA,2013:19-22
[15] Ahn H S,Won C H. Fast alignment using rotation vector and adaptive Kalman filter[J]. IEEE Transactions on Aerospace and Electronic Systems,2006,42(1):70-83.
[16] Yu M J. INS/GPS integration system using adaptive filter for estimating measurement noise variance[J]. Aerospace and Electronic Systems,IEEE Transactions on,2012,48(2):1786-1792.
[17] Hu C. Adaptive Kalman filtering for vehicle navigation[J]. Positioning,2009,1(04):0.
[18] Hide C,Moore T,Smith M. Adaptive Kalman filtering for low-cost INS/GPS[J]. The Journal of Navigation,2003,56(01):143-152.
[19] Ding W. Improving adaptive Kalman estimation in GPS/INS integration[J]. Journal of Navigation,2007,60(03):517-529.
[20] Yang Y,Gao W. An optimal adaptive Kalman filter[J]. Journal of Geodesy,2006,80(4):177-183.
[21] Kallemeyn P. The Mars pathfinder navigation System[J]. AIAA Paper,1996:96-3656.