SCIENCE CHINA Information Sciences, Volume 60, Issue 4: 042201(2017) https://doi.org/10.1007/s11432-015-0189-9

Iterative spherical simplex unscented particle filter for CNS/Redshift integrated navigation system

More info
  • ReceivedApr 19, 2016
  • AcceptedMay 7, 2016
  • PublishedMar 14, 2017


We propose an improved Unscented Particle Filter (UPF) algorithm for the Celestial Navigation System/Redshift (CNS/Redshift) integrated navigation system. The algorithm adopts the iterated spherical simplex unscented transformation rather than the traditional unscented transformation. The navigation performance of the proposed algorithm is assessed by several indexes. Simulation results show that the proposed UPF algorithm has advantages over the traditional UPF algorithm in terms of computation burden, navigation accuracy, and numerical stability.

Funded by

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

National Basic Research Program of China(973 Program)



This work was supported by National Basic Research Program of China (973 Program) (Grant No. 2014CB744206). The authors would like to thank the anonymous reviewers for their constructive comments in improving the quality and presentation of this paper.


[1] Ning X L, Fang J C. An autonomous celestial navigation method for \{LEO\} satellite based on unscented Kalman filter and information fusion. Aerosp Sci Technol, 2007, 11: 222-228 CrossRef Google Scholar

[2] Ning X L, Fang J C. Spacecraft autonomous navigation using unscented particle filter-based celestial/Doppler information fusion. Meas Sci Technol, 2008, 19: 095203-228 CrossRef Google Scholar

[3] Ning X L, Fang J C. A new autonomous celestial navigation method for the lunar rover. Robot Auton Syst, 2009, 57: 48-54 CrossRef Google Scholar

[4] Zhuang Y, Gu M W, Wang W, et al. Multi-robot cooperative localization based on autonomous motion state estimation and laser data interaction. Sci China Inf Sci, 2010, 53: 2240-2250 CrossRef Google Scholar

[5] St-Pierre M, Gingras D. Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system. In: {Proceedings of IEEE Intelligent Vehicles Symposium}, Parma, 2004. 831--835. Google Scholar

[6] Huang Y, Chen Z J, Wei C. Least trace extended set-membership filter. Sci China Inf Sci, 2010, 53: 258-270 CrossRef Google Scholar

[7] Julier S J, Uhlmann J K. Unscented filtering and nonlinear estimation. Proc IEEE, 2004, 92: 401-422 CrossRef Google Scholar

[8] Wang J L, Feng X Y, Zhao L Q, et al. Unscented transformation based robust Kalman filter and its applications in fermentation process. Chin J Chem Eng, 2010, 18: 412-418 CrossRef Google Scholar

[9] Morelande M R, Moran B. An unscented transformation for conditionally linear models. In: {Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, Honolulu, 2007. 3: 1417--1420. Google Scholar

[10] Arulampalam M S, Maskell S, Gordon N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process, 2002, 50: 174-188 CrossRef Google Scholar

[11] Wang L, Wan J W, Liu Y H, et al. Cooperative localization method for multi-robot based on pf-ekf. Sci China Ser-F: Inf Sci, 2008, 51: 1125-1137 Google Scholar

[12] van der Merwe R, Doucet A, de Freitas N, et al. The unscented particle filter. In: {Proceedings of Conference on Neural Information Processing Systems}, Denver, 2000. 584--590. Google Scholar

[13] Julier S J. The spherical simplex unscented transformation. In: {Proceedings of American Control Conference}, Denver, 2003. 3: 2430--2434. Google Scholar

[14] He W, Ge S S. Robust adaptive boundary control of a vibrating string under unknown time-varying disturbance. IEEE Trans Control Syst Technol, 2012, 20: 48-58 Google Scholar

[15] He W, He X, Ge S S. Vibration control of flexible marine riser systems with input saturation. IEEE/ASME Trans Mechatron, 2016, 21: 254-265 Google Scholar

[16] Zhan R H, Wan J W. Iterated unscented Kalman filter for passive target tracking. IEEE Trans Aerosp Electron Syst, 2007, 43: 1155-1163 CrossRef Google Scholar

[17] He W, Ge S S, Huang D Q. Modeling and vibration control for a nonlinear moving string with output constraint. IEEE/ASME Trans Mechatron, 2015, 20: 1886-1897 CrossRef Google Scholar

[18] Zhang S, He W, Huang D Q. Active vibration control for a flexible string system with input backlash. IET Control Theory Appl, 2016, 10: 800-805 CrossRef Google Scholar

[19] He W, Ge S S, Li Y N, et al. Neural network control of a rehabilitation robot by state and output feedback. J Intell Robot Syst, 2015, 80: 15-31 CrossRef Google Scholar

[20] Song T T, Ning X L, Yu W B. A new autonomous celestial navigation method for Mars probe. In: {Proceedings of 6th International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic technology, and Artificial Intelligence}, Beijing, 2006. 63574N. Google Scholar

[21] Li P, Cui H T, Cui P Y. Upf based autonomous navigation scheme for deep space probe. J Syst Eng Electron, 2008, 19: 529-536 CrossRef Google Scholar

[22] Valarmathi J, Emmanuel D S, Christopher S. Velocity tracking based on interpolated adaptive Doppler filter. In: {Proceedings of 15th International Conference on Information Fusion (FUSION)}, Singapore, 2012. 1511--1518. Google Scholar

[23] Krysik P, Wielgo M, Misiurewicz J, et al. Doppler-only tracking in GSM-based passive radar. In: {Proceedings of 17th International Conference on Information Fusion (FUSION)}, Salamanca, 2014. 1--7. Google Scholar

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有