Hostname: page-component-77f85d65b8-g4pgd Total loading time: 0 Render date: 2026-04-23T00:13:33.462Z Has data issue: false hasContentIssue false
  • English
  • Français

Extended Kalman Filter with Input Detection and Estimation for Tracking Manoeuvring Satellites

Published online by Cambridge University Press:  11 January 2019

Yuzi Jiang ,
Hongwei Yang ,
Hexi Baoyin  and
Pengbin Ma
Yuzi Jiang
Affiliation:
(School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China)
Hongwei Yang
Affiliation:
(College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China)
Hexi Baoyin*
Affiliation:
(School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China)
Pengbin Ma
Affiliation:
(State Key Laboratory of Astronautic Dynamics, Xi'an, 710043, China)
*
(E-mail: baoyin@tsinghua.edu.cn)
Get access Rights & Permissions [Opens in a new window]

Abstract

The technique of tracking a non-cooperative manoeuvring satellite is important for Space Situation Awareness (SSA). However, the classical extended Kalman filter cannot work successfully in this situation. Motivated by this problem, a novel Extended Kalman Filter with Input Detection and Estimation (EKF/IDE) method is proposed in this paper for tracking a non-cooperative satellite with impulsive manoeuvres. The impulsive manoeuvre is modelled as an unknown acceleration without any prior information. An unbiased minimum-variance input and state estimation method is introduced to estimate the manoeuvre acceleration. An approach based on the Mahalanobis distance of the manoeuvre estimate error is proposed for manoeuvre detection. With the impulsive manoeuvre being detected and estimated accurately, an adaptive extended Kalman filter is proposed to estimate the state of the target. An approach of covariance inflation is proposed to deal with the manoeuvre during the unobserved period. Simulations and Monte Carlo experiments are implemented to demonstrate the feasibility and validity of the proposed method. The results of simulations show that the proposed method can accurately detect and estimate unknown impulsive manoeuvres of a non-cooperative satellite. Through the compensation of the estimated manoeuvre, the estimation of position and velocity after the manoeuvre maintains the accuracy of the pre-manoeuvre period, demonstrating the robustness of the proposed method against unknown manoeuvres.

Keywords

Extended Kalman FilterTracking manoeuvring targetManoeuvre detection

Information

Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 3 , May 2019 , pp. 628 - 648
Copyright
Copyright © The Royal Institute of Navigation 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Cao, L., Chen, X. Q. and Misra, A. K. (2015). A novel unscented predictive filter for relative position and attitude estimation of satellite formation. Acta Astronautica, 112, 140–157.Google Scholar
Cao, L. and Li, H. N. (2016). Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors. Acta Astronautica, 127, 505–513.Google Scholar
Cao, L., Chen, Y. Q., Zhang, Z. D., Li, H. N. and Misra, A. K. (2017). Predictive smooth variable structure filter for attitude synchronization estimation during satellite formation flying. IEEE Transactions on Aerospace and Electronic Systems, 53(3), 1375–1383.Google Scholar
Daeipour, E. and Bar-Shalom, Y. (1998). IMM tracking of maneuvering targets in the presence of glint. IEEE Transactions on Aerospace and Electronic systems, 34(3), 996–1003.Google Scholar
Fitzgerald, R. (1971). Divergence of the Kalman filter. IEEE Transactions on Automatic Control, 16(6), 736–747.Google Scholar
Gillijns, S. and De Moor, B. (2007a). Unbiased minimum-variance input and state estimation for linear discrete-time systems. Automatica, 43(1), 111–116.Google Scholar
Gillijns, S. and De Moor, B. (2007b). Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough. Automatica, 43(5), 934–937.Google Scholar
Jwo, D. J. and Lai, S. Y. (2009). Navigation integration using the fuzzy strong tracking unscented Kalman filter. The Journal of Navigation, 62(2), 303–322.Google Scholar
Kaempchen, N., Weiss, K., Schaefer, M. and Dietmayer, K. C. (2004, June). IMM object tracking for high dynamic driving maneuvers. In Intelligent Vehicles Symposium, 2004 IEEE (825–830). IEEE.Google Scholar
Kelecy, T. and Jah, M. (2010). Detection and orbit determination of a satellite executing low thrust maneuvers. Acta Astronautica, 66(5), 798–809.Google Scholar
Khaloozadeh, H. and Karsaz, A. (2009). Modified input estimation technique for tracking manoeuvring targets. IET Radar, Sonar & Navigation, 3(1), 30–41.Google Scholar
Ko, H. C. and Scheeres, D. J. (2016). Tracking Maneuvering Satellite Using Thrust-Fourier-Coefficient Event Representation. Journal of Guidance, Control, and Dynamics, 39(11), 2554–2562.Google Scholar
Lee, B. J., Park, J. B., Lee, H. J. and Joo, Y. H. (2005). Fuzzy-logic-based IMM algorithm for tracking a manoeuvring target. IEE Proceedings-Radar, Sonar and Navigation, 152(1), 16–22.Google Scholar
Lee, H. and Tahk, M. J. (1999). Generalized input-estimation technique for tracking maneuvering targets. IEEE transactions on Aerospace and Electronic Systems, 35(4), 1388–1402.Google Scholar
Lee, S. and Hwang, I. (2015). Interacting multiple model estimation for spacecraft maneuver detection and characterization. In AIAA Guidance, Navigation, and Control Conference (2015–1333).Google Scholar
Lin, H. J. and Atherton, D. P. (1993). Investigation of IMM tracking algorithm for the maneuvering target tracking. In Aerospace Control Systems, 1993. Proceedings. The First IEEE Regional Conference (113–117). IEEE.Google Scholar
Mazor, E., Averbuch, A., Bar-Shalom, Y. and Dayan, J. (1998). Interacting multiple model methods in target tracking: a survey. IEEE Transactions on Aerospace and Electronic Systems, 34(1), 103–123.Google Scholar
Munir, A. and Atherton, D. P. (1995). Adaptive interacting multiple model algorithm for tracking a manoeuvring target. IEE Proceedings-Radar, Sonar and Navigation, 142(1), 11–17.Google Scholar
Patera, R. P. (2008). Space event detection method. Journal of Spacecraft and Rockets, 45(3), 554–559.Google Scholar
Tan, X. L., Wang, J., Jin, S. G. and Meng, X. L. (2015). GA-SVR and pseudo-position-aided GPS/INS integration during GPS outage. The Journal of Navigation, 68(4), 678–696.Google Scholar
Wang, Y. D., Sun, S. M. and Li, L. (2014). Adaptively robust unscented Kalman filter for tracking a maneuvering vehicle. Journal of Guidance, Control, and Dynamics, 37(5), 1696–1701.Google Scholar
Wei, E. H., Jin, S. G., Zhang, Q., Liu, J. N., Li, X. C. and Yan, W. (2013). Autonomous navigation of Mars probe using X-ray pulsars: modeling and results. Advances in Space Research, 51(5), 849–857.Google Scholar
Whang, I. H., Lee, J. G. and Sung, T. K. (1994). A modified input estimation technique using pseudoresiduals. IEEE transactions on aerospace and electronic systems, 30(2), 591–598.Google Scholar
Zhai, G., Bi, X. Z., Zhao, H. Y. and Liang, B. (2018). Non-cooperative maneuvering spacecraft tracking via a variable structure estimator. Aerospace Science and Technology, 79, 352–363.Google Scholar
Zhou, D. H. and Frank, P. M. (1996). Strong tracking filtering of nonlinear time-varying stochastic systems with coloured noise: Application to parameter estimation and empirical robustness analysis. International Journal of Control, 65(2), 295–307.Google Scholar