AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Article Link
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

Real-time hybrid method for maneuver detection and estimation of non-cooperative space targets

School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
Show Author Information

Graphical Abstract

Abstract

A novel hybrid scheme for the maneuver detection and estimation of a noncooperative space target was proposed in this study. The optical measurements, together with the range and range rate measurements from the ground-based radars, were used in the tracking scenarios. In many tracking scenarios, radar resources for non-cooperative targets are constrained, particularly for near-earth targets, where multiple objects can only be tracked by a single radar at a time. This limitation hinders the accurate estimation of noncooperative target maneuvers, and can at times result in target loss. Existing literature has addressed this issue to some extent through various maneuvering target-tracking methods. To address this problem, a hybrid maneuver detection and estimation method that combines the input detection and estimation extended kalman filter and the weighted nonlinear least squares method is presented. Simulation results demonstrate that the proposed method outperforms the previous method, offering more accurate and efficient estimations.

References

[1]

Baird, M. Maintaining space situational awareness and taking it to the next level. Air & Space Power Journal, 2013, 27(5): 50–72.

[2]

Abbot, R., Wallace, T. P. Decision support in space situational awareness. Lincoln Laboratory Journal, 2007, 16(2): 297.

[3]

Bobrinsky, N., Del Monte, L. The space situational awareness program of the European Space Agency. Cosmic Research, 2010, 48(5): 392–398.

[4]
Gates, R. M., Clapper, J. R. National security space strategy unclassified summary. US Department of Defense and Office of the Director of National Intelligence, Washington, DC, USA, 2011.
[5]

Tasif, T. H., Hippelheuser, J. E., Elgohary, T. A. Analytic continuation extended Kalman filter framework for perturbed orbit estimation using a network of space-based observers with angles-only measurements. Astrodynamics, 2022, 6(2): 161–187.

[6]
Kennewell, J. A., Vo, B. N. An overview of space situational awareness. In: Proceedings of the 16th International Conference on Information Fusion, 2013: 1029–1036
[7]

Luo, Y. Z., Di Lizia, P., Yang, Z. Message from the guest editors of the special issue on astrodynamics for space situational awareness. Astrodynamics, 2022, 6(2): 93–94.

[8]

Singer, R. Estimating optimal tracking filter performance for manned maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 1970, AES-6(4): 473–483.

[9]

Zhou, H. R., Kumar, K. S. P. A 'current' statistical model and adaptive algorithm for estimating maneuvering targets. Journal of Guidance, Control, and Dynamics, 1984, 7(5): 596–602.

[10]

Whang, I. H., Lee, J. G., Sung, T. K. Modified input estimation technique using pseudoresiduals. IEEE Transactions on Aerospace and Electronic Systems, 1994, 30(1): 220–228.

[11]

Lee, H. G., Tahk, M. J. Generalized input-estimation technique for tracking maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 1999, 35(4): 1388–1402.

[12]

Li, X. R., Jilkov, V. P. Survey of maneuvering target tracking. Part I. Dynamic models. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1333–1364.

[13]

Ko, H. C., Scheeres, D. J. Event representation-based orbit determination across unknown space events. Journal of Guidance, Control, and Dynamics, 2015, 38(12): 2351–2365.

[14]

Zhou, D. H., Xi, Y. G., Zhang, Z. J. Suboptimal fading extended Kalman filtering for nonlinear systems. Control and Decision, 1990, 5(5): 1–6. (in Chinese)

[15]

Jwo, D. J., Lai, S. Y. Navigation integration using the fuzzy strong tracking unscented Kalman filter. Journal of Navigation, 2009, 62(2): 303–322.

[16]

Wang, Y. D., Sun, S. M., Li, L. Adaptively robust unscented Kalman filter for tracking a maneuvering vehicle. Journal of Guidance, Control, and Dynamics, 2014, 37(5): 1696–1701.

[17]
Zhang, C., Zhao, M., Yu, X. L., Cui, M. L., Zhou, Y., Wang, X. G. Cubature Kalman filter based on strong tracking. In: Proceedings of the 3rd International Conference on Communications, Signal Processing, and Systems, 2015: 131–138.
[18]

Zhang, H. W., Xie, J. W., Ge, J. A., Lu, W. L., Zong, B. F. Adaptive strong tracking square-root cubature Kalman filter for maneuvering aircraft tracking. IEEE Access, 2018, 6: 10052–10061.

[19]

Jiang, Y. Z., Ma, P. B., Baoyin, H. X. Residual-normalized strong tracking filter for tracking a noncooperative maneuvering spacecraft. Journal of Guidance, Control, and Dynamics, 2019, 42(10): 2304–2309.

[20]

Kitanidis, P. K. Unbiased minimum-variance linear state estimation. Automatica, 1987, 23(6): 775–778.

[21]

Hsieh, C. S. Robust two-stage Kalman filters for systems with unknown inputs. IEEE Transactions on Automatic Control, 2000, 45(12): 2374–2378.

[22]

Gillijns, S., De Moor, B. Unbiased minimum-variance input and state estimation for linear discrete-time systems. Automatica, 2007, 43(1): 111–116.

[23]

Li, X. R., Jilkov, V. P. Survey of maneuvering target tracking. Part v: Multiple-model methods. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1255–1321.

[24]

Magill, D. Optimal adaptive estimation of sampled stochastic processes. IEEE Transactions on Automatic Control, 1965, 10(4): 434–439.

[25]

Lainiotis, D. Optimal adaptive estimation: Structure and parameter adaption. IEEE Transactions on Automatic Control, 1971, 16(2): 160–170.

[26]

Lainiotis, D. G. Partitioning: A unifying framework for adaptive systems, I: Estimation. Proceedings of the IEEE, 1976, 64(8): 1126–1143.

[27]
Blom, H. P. An efficient filter for abruptly changing systems. In: Proceedings of the 23rd IEEE Conference on Decision and Control, 1984: 656–658.
[28]
Blom, H. P. Overlooked potential of systems with Markovian coefficients. In: Proceedings of the 25th IEEE Conference on Decision and Control, 1986: 1758–1764.
[29]
Sviestins, E. Multiradar tracking for theater missile defense. In: Proceedings of the SPIE 2561, Signal and Data Processing of Small Targets, 1995: 384–394.
[30]
Goff, G. M., Black, J., Beck, J. A., Hess, J. A. A dynamic sensor tasking strategy for tracking maneuvering spacecraft using multiple models. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference, 2016: AIAA 2016-1859.
[31]

Goff, G. M., Black, J. T., Beck, J. A. Tracking maneuvering spacecraft with filter-through approaches using interacting multiple models. Acta Astronautica, 2015, 114: 152–163.

[32]
Li, X. R., Bar-Shakm, Y. Mode-set adaptation in multiple-model estimators for hybrid systems. In: Proceedings of the American Control Conference, 1992: 1794–1799.
[33]

Li, X. R. Multiple-model estimation with variable structure. Ⅱ. Model-set adaptation. IEEE Transactions on Automatic Control, 2000, 45(11): 2047–2060.

[34]
Lee, S. J., Hwang, I. Interacting multiple model estimation for spacecraft maneuver detection and characterization. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference, 2015: AIAA 2015-1333.
[35]

Jiang, Y. Z., Yang, H. W., Baoyin, H. X., Ma, P. B. Extended Kalman filter with input detection and estimation for tracking manoeuvring satellites. Journal of Navigation, 2019, 72(3): 628–648.

[36]

Wu, D., Guo, X., Jiang, F. H., Baoyin, H. X. Atlas of optimal low-thrust rephasing solutions in circular orbit. Journal of Guidance, Control, and Dynamics, 2023, 46(5): 856–870.

[37]

Wen, C. X., Qiao, D. Calculating collision probability for long-term satellite encounters through the reachable domain method. Astrodynamics, 2022, 6(2): 141–159.

[38]

Li, J. S., Yang, Z., Luo, Y. Z. A review of space-object collision probability computation methods. Astrodynamics, 2022, 6(2): 95–120.

[39]

Wu, D., Wang, W., Jiang, F. H., Li, J. F. Minimum-time low-thrust many-revolution geocentric trajectories with analytical costates initialization. Aerospace Science and Technology, 2021, 119: 107146

[40]

Lev, D., Myers, R. M., Lemmer, K. M., Kolbeck, J., Koizumi, H., Polzin, K. The technological and commercial expansion of electric propulsion. Acta Astronautica, 2019, 159: 213–227.

[41]
Bar-Shalom, Y., Li, X. R., Kirubarajan, T. Estimation with Applications to Tracking and Navigation. John Wiley & Sons, Inc., 2001.
[42]
Hartman, P. Ordinary Differential Equations. Society for Industrial and Applied Mathematics, 2002.
[43]
Li, X. R., Jilkov, V. P. Survey of maneuvering target tracking: Dynamic models. In: Proceedings of the SPIE 4048, Signal and Data Processing of Small Targets, 2000: 212–235.
[44]
Casella, G., Berger, R. Statistical Inference. Boca Raton: Chapman and Hall/CRC, 2021.
[45]

Gillijns, S., De Moor, B. Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough. Automatica, 2007, 43(5): 934–937.

[46]
Schutz, B., Tapley, B., Born, G. H. Statistical Orbit Determination. Elsevier, 2004.
Astrodynamics
Pages 437-453
Cite this article:
Zhang P, Wu D, Baoyin H. Real-time hybrid method for maneuver detection and estimation of non-cooperative space targets. Astrodynamics, 2024, 8(3): 437-453. https://doi.org/10.1007/s42064-024-0203-y

205

Views

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics

Received: 29 October 2023
Accepted: 16 January 2024
Published: 06 June 2024
© Tsinghua University Press 2024
Return