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FAN Shuangfei, ZHAO Fangfang, LI Xiajing, TANG Zhongliang, HE Wei. Multiple Model Adaptive Estimation Algorithm for SINS/CNS Integrated Navigation System[J]. Journal of Deep Space Exploration, 2014, 1(4): 275-281. doi: 10.15982/j.issn.2095-7777.2014.04.005
Citation: FAN Shuangfei, ZHAO Fangfang, LI Xiajing, TANG Zhongliang, HE Wei. Multiple Model Adaptive Estimation Algorithm for SINS/CNS Integrated Navigation System[J]. Journal of Deep Space Exploration, 2014, 1(4): 275-281. doi: 10.15982/j.issn.2095-7777.2014.04.005

Multiple Model Adaptive Estimation Algorithm for SINS/CNS Integrated Navigation System

doi: 10.15982/j.issn.2095-7777.2014.04.005
  • Received Date: 2014-07-10
  • Rev Recd Date: 2014-07-30
  • In this paper, a new filtering method based on multiple model adaptive estimation(MMAE) algorithm is proposed, for the problem of poor adaptability of single model filters with unknown or uncertain parameters. In this proposed algorithm, we use improved Kalman filters rather than traditional Kalman filters, such as extended Kalman filter (EKF), unscented Kalman filter (UKF). And EKF and UKF are used as sub filters in MMAE algorithm to realize the state estimation of nonlinear system. Meanwhile, this method is applied to the SINS/CNS integrated navigation system under the motion of ballistic missile. As the simulation result shows, the improved filtering methods have better navigation accuracy, and can solve the problem of poor adaptability of single model filter, when compared with traditional EKF and UKF algorithms.
  • [1]
    Xiong Z, Liu J Y, Yu F, et al. Research of airborne INS/CNS integrated filtering algorithm based on celestial angle observation[J]. Journal of Astronautics, 2010,31(2):397-402.
    [2]
    Wang J J, Yu J Q, Wang L. Analysis of application of a new integrated navigation system of the tactical missiles[C]//Chinese Control Conference(CCC). [S.l.]: CCC, 2014:653-657.
    [3]
    吴伟仁,王大轶,宁晓琳.深空探测器自主导航原理与技术[M].北京:中国宇航出版社,2011:57-62.[Wu W R, Wang D Y, Ning X L. The principle and technology of deep space autonomous navigation[M]. Beijing: China Astronautic Publishing House, 2011:57-62.]
    [4]
    王小旭, 赵琳.自适应融合滤波算法及其在INS/GPS组合导航中的应用[J].宇航学报,2010,31(11):2504-2511.[Wang X X, Zhao L. Adaptive fusion filtering algorithm and its application for INS/GPS integrated navigation system[J]. Journal of Astronautics, 2010,31(11):2504-2511.]
    [5]
    陈培,杨颖,王云,等.扩展卡尔曼滤波估计载波参数的算法研究[J].电子科技大学学报,2009,38(4):509-512.[Chen P, Yang Y, Wang Y, et al. Estimation for carrier parameters based on the extended Kalman filter[J]. Journal of University of Electronic Science and Technology of China, 2009,38(4):509-512.]
    [6]
    吴楠,陈磊.高速声速滑翔再入飞行器弹道估计的自适应卡尔曼滤波[J].航空学报,2013,34(8):1960-1971.[Wu N, Chen L. Adaptive Kalman filtering for trajectory estimation of hypersonic glide reentry vehicles[J]. Acta Aeronautica et Astronautica Sinica, 2013,34(8):1960-1971.]
    [7]
    胡正东,郭才发,张士峰,等.Unscented卡尔曼滤波在飞航导弹地磁导航中的应用[J].宇航学报,2009,30(4):1443-1448.[Hu Z D, Guo C F, Zhang S F, et al. Application of unscented Kalman filter in geomagnetic navigation for aerodynamic missile[J]. Journal of Astronautics, 2009,30(4):1443-1448.]
    [8]
    Kong F C, Dai G L, Cai L.The composed correcting Kalman filtering method for integrated SINS/GPS navigation system[C]//Intelligent Computing and Intelligent Systems(ICIS), 2010 IEEE International Conference on. [S.l.]: IEEE, 2010,2:408-412.
    [9]
    李艳华,房建成.一种多模型自适应联邦滤波器及其在INS/CNS/GPS组合导航系统中的应用[J].航天控制,2003,21(2):33-38.[Li Y H, Fang J C. A multi-model adaptive federated filter and its application in INS/CNS/GPS integrated navigation system[J]. Aerospace Control, 2003,21(2):33-38.]
    [10]
    陈金广,李洁,高新波.双重迭代变分贝叶斯自适应卡尔曼滤波算法[J].电子科技大学学报,2012,41(3):360-363.[Chen J G, Li J, Gao X B. Dual recursive variational bayesian adaptive kalman filtering algorithm[J]. Journal of University of Electronic Science and Technology of China, 2012,41(3):360-363.]
    [11]
    熊凯,魏春玲.基于多模型自适应估计的航天器相对导航[J].系统科学与数学,2014,34(7):828-837.[Xiong K, Wei C L. Spacecraft relative navigation based on multiple model adaptive estimator[J]. Journal of Systems Science and Mathematical Sciences, 2014,34(7):828-837.]
    [12]
    Shima T, Oshman Y, Shinar J. Efficient multiple model adaptive estimation in ballistic missile interception scenarios[J]. Journal of Guidance, Control, and Dynamics, 2002,25(4):667-675.
    [13]
    Qu C S, Xu H L, Ying T. SINS/CNS integrated navigation solution using adaptive unscented Kalman filtering[J]. International Journal of Computer Applications in Technology, 2011,41(1):109-116.
    [14]
    Ning X L, Fang J C. Spacecraft autonomous navigation using unscented particle filter-based celestial/Doppler information fusion[J]. Measurement Science and Technology, 2008,19(9):095203.
    [15]
    宁晓琳,房建成.一种基于纯天文观测的火星车自主导航方法[J].空间科学学报,2006,26(2):142-147.[Ning X L, Fang J C. A new autonomous navigation method for martian rover based on celestial observation[J]. Chinese Journal of Space Science, 2006,26(2):142-147.]
    [16]
    Nebelecky C K, Crassidis J L, Singla P. A model error formulation of the multiple model adaptive estimation algorithm[C]//Information Fusion,2014 17th International Conference. [S.l.]: [s.n.], 2014:1-8.
    [17]
    邓红,刘光斌,陈昊明,等.发射惯性坐标系下误差角与数学平台失准角的推导与仿真[J].宇航学报,2011,32(4):781-786.[Deng H, Liu G B, Chen H M, et al. Deduction and simulation of angular error relationship in "SINS/CNS" integrated navigation system[J]. Journal of Astronautics, 2011,32(4):781-786.]
    [18]
    Hammersley J M. Monte Carlo methods for solving multivariable problems[J]. Proceedings of the New York Academy of Sciences, 1960,86(3):844-874.
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Multiple Model Adaptive Estimation Algorithm for SINS/CNS Integrated Navigation System

doi: 10.15982/j.issn.2095-7777.2014.04.005

Abstract: In this paper, a new filtering method based on multiple model adaptive estimation(MMAE) algorithm is proposed, for the problem of poor adaptability of single model filters with unknown or uncertain parameters. In this proposed algorithm, we use improved Kalman filters rather than traditional Kalman filters, such as extended Kalman filter (EKF), unscented Kalman filter (UKF). And EKF and UKF are used as sub filters in MMAE algorithm to realize the state estimation of nonlinear system. Meanwhile, this method is applied to the SINS/CNS integrated navigation system under the motion of ballistic missile. As the simulation result shows, the improved filtering methods have better navigation accuracy, and can solve the problem of poor adaptability of single model filter, when compared with traditional EKF and UKF algorithms.

FAN Shuangfei, ZHAO Fangfang, LI Xiajing, TANG Zhongliang, HE Wei. Multiple Model Adaptive Estimation Algorithm for SINS/CNS Integrated Navigation System[J]. Journal of Deep Space Exploration, 2014, 1(4): 275-281. doi: 10.15982/j.issn.2095-7777.2014.04.005
Citation: FAN Shuangfei, ZHAO Fangfang, LI Xiajing, TANG Zhongliang, HE Wei. Multiple Model Adaptive Estimation Algorithm for SINS/CNS Integrated Navigation System[J]. Journal of Deep Space Exploration, 2014, 1(4): 275-281. doi: 10.15982/j.issn.2095-7777.2014.04.005
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