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基于加權(quán)自適應(yīng)平方根容積卡爾曼濾波的GPS/INS組合導(dǎo)航方法

岳哲 廉保旺 唐成凱

岳哲, 廉保旺, 唐成凱. 基于加權(quán)自適應(yīng)平方根容積卡爾曼濾波的GPS/INS組合導(dǎo)航方法[J]. 電子與信息學(xué)報(bào), 2018, 40(3): 565-572. doi: 10.11999/JEIT170597
引用本文: 岳哲, 廉保旺, 唐成凱. 基于加權(quán)自適應(yīng)平方根容積卡爾曼濾波的GPS/INS組合導(dǎo)航方法[J]. 電子與信息學(xué)報(bào), 2018, 40(3): 565-572. doi: 10.11999/JEIT170597
YUE Zhe, LIAN Baowang, TANG Chengkai. A GPS/INS Integrated Navigation Method Based on Weighting Adaptive Square-root Cubature Kalman Filter[J]. Journal of Electronics & Information Technology, 2018, 40(3): 565-572. doi: 10.11999/JEIT170597
Citation: YUE Zhe, LIAN Baowang, TANG Chengkai. A GPS/INS Integrated Navigation Method Based on Weighting Adaptive Square-root Cubature Kalman Filter[J]. Journal of Electronics & Information Technology, 2018, 40(3): 565-572. doi: 10.11999/JEIT170597

基于加權(quán)自適應(yīng)平方根容積卡爾曼濾波的GPS/INS組合導(dǎo)航方法

doi: 10.11999/JEIT170597
基金項(xiàng)目: 

國家自然科學(xué)基金(61301094, 61473308, 61501430)

A GPS/INS Integrated Navigation Method Based on Weighting Adaptive Square-root Cubature Kalman Filter

Funds: 

The National Natural Science Foundation of China (61301094, 61473308, 61501430)

  • 摘要: 針對GPS/INS組合導(dǎo)航系統(tǒng)中,由于量測噪聲統(tǒng)計(jì)的不確定性導(dǎo)致平方根容積卡爾曼濾波器(SCKF)濾波精度下降甚至發(fā)散的問題,該文提出一種基于加權(quán)的自適應(yīng)SCKF(WASCKF)方法。該方法首先利用移動(dòng)開窗理論對SCKF新息的協(xié)方差陣進(jìn)行最大似然估計(jì),實(shí)現(xiàn)對測量噪聲統(tǒng)計(jì)特性的在線調(diào)整;然后,利用加權(quán)理論,依據(jù)窗口內(nèi)不同時(shí)刻信息的有用程度的不同而設(shè)置相應(yīng)的權(quán)值,增強(qiáng)對窗口內(nèi)有用信息的利用。最后,將WASCKF方法應(yīng)用于GPS/INS組合導(dǎo)航系統(tǒng)中進(jìn)行仿真驗(yàn)證,并與SCKF和ASCKF方法進(jìn)行比較,結(jié)果表明,在測量噪聲統(tǒng)計(jì)存在不確定情況下,該文所提出方法的速度誤差和位置誤差的均方根均小于SCKF和ASCKF方法,能夠有效地提高GPS/INS組合導(dǎo)航系統(tǒng)對量測噪聲統(tǒng)計(jì)不確定的自適應(yīng)能力與導(dǎo)航性能。
  • TSENG Chienhao, LIN Shengfuu, and JWO Dahjing. Fuzzy adaptive cubature Kalman filter for integrated navigation systems[J]. Sensors, 2016, 16(8): 1167-1189. doi: 10.3390/ s16081167.
    LI Zengke, WANG Jingxiang, WANG Jian, et al. PPP/INS tightly coupled navigation using adaptive federated filter[J]. GPS Solutions, 2017, 21(1): 1-12. doi: 10.1007/s10291-015- 0511-z.
    WANG Liansheng and XIA Yuanqing. Mars Entry Navigation with uncertain parameters based on desensitized extended Kalman filter[J]. IEEE Transactions on Industrial Informatics, 2015, 11(5): 998-1005. doi: 10.1109/TII.2015. 2463763.
    LI Donggong, JI Daxiong, LIU Jian, et al. A Multi-model EKF integrated navigation algorithm for deep water AUV[J]. International Journal of Advanced Robotic Systems, 2016, 13(1): 1-15. doi: 10.5772/62076.
    YAZDKHASTI S, SASIADEK J, and ULRICH S. Performance enhancement for GPS/INS fusion by using a fuzzy adaptive unscented Kalman filter[C]. 2016 21st International Conference on Methods and Models in Automation and Robotics, Poland, 2016: 1194-1199. doi: 10.1109/MMAR.2016.7575308.
    LI Kailong, CHANG Lubin, and HU Baiqing. A variational bayesian-based unscented Kalman filter with both adaptivity and robustness[J]. IEEE Sensors Journal, 2016, 16(18): 6966-6976. doi: 10.1109/JSEN.2016.2591260.
    ARASARATNAM I and HAYKIN S. Cubature Kalman filters[J]. IEEE Transactions on Automatic Control, 2009, 54(6): 510-518. doi: 10.1109/TAC.2009.2019800.
    XU Bo, ZHANG Peng, WEN Hongzhi, et al. Stochastic stability and performance analysis of Cubature Kalman Filter[J]. Neurocomputing, 2015, 186: 218-227. doi: 10.1016 /j.neucom.2015.12.087.
    CHANDRA K, GU D, and POSTLETHWAITE I. Square root cubature information filter[J]. IEEE Sensors Journal, 2013, 13(2): 750-758. doi: 10.1109/JSEN.2012.2226441.
    WANG Dingjie, L Hanfeng, and WU Jie. Augmented cubature Kalman filter for nonlinear RTK/MIMU integrated navigation with non-additive noise[J]. Measurement Journal of the International Measurement Confederation, 2017, 97: 111-125. doi: 10.1016/j.measurement.2016.10.056.
    CUI Bingbo, CHEN Xiyuan, and TANG Xinhua. Improved cubature Kalman filter for GNSS/INS based on transformation of posterior sigma-points error[J]. IEEE Transactions on Signal Processing, 2017, 65(11): 2975-2987. doi: 10.1109/TSP.2017.267968.
    JIANG Changhui, CHEN Shuai, BO Yuming, et al. Performance improvement of GPS/SINS ultra-tightly integrated navigation system utilizing a robust Cubature Kalman Filter[J]. Journal of Aeronautics, Astronautics and Aviation, 2017, 49(1): 49-55. doi: 10.6125/16-1201-916.
    SHI Yuepeng, CHE Lei, GE Quanbo, et al. Adaptive high-degree Cubature Kalman Filter with unknown noise statistics[J]. Journal of Information and Computational Science, 2014, 11(18): 6703-6712. doi: 10.12773/jics20105083.
    JIANG Chen, ZHANG Shubi, and ZHANG Qiuzhao. A novel robust interval Kalman filter algorithm for GPS/INS integrated navigation[J]. Journal of Sensors, 2016, 2016: ID 3727241. doi: 10.1155/2016/3727241.
    YUE Zhe, LIAN Baowang, and TANG Chengkai. The GPS/INS integrated navigation method based on adaptive SSR-SCKF cubature Kalman filter[C]. The 8th China Satellite Navigation Conference, Shanghai, 2017: 395-405. doi: 10.1007/978-981-10-4591-2_32.
    胡宇, 張世英, 羅雷. 基于自適應(yīng)容積卡爾曼濾波方法的渦扇發(fā)動(dòng)機(jī)氣路部件故障診斷[J]. 航空動(dòng)力學(xué)報(bào), 2016, 31(5): 1260-1267. doi: 10.13224/j.cnki.jasp.2016.05.030.
    HU Yu, ZHANG Shiying, and LUO Lei. Turbofan engine gas path components fault diagnosis based on adaptive cubature Kalman filter[J]. Journal of Aerospace Power, 2016, 31(5):
    YANG Yuanxi and ZHANG Shuangcheng. Adaptive fitting of systematic errors in navigation[J]. Journal of Geodesy, 2005, 79(1): 43-49. doi: 10.1007/s00190-005-0441-6.
    GAO Shesheng, HU Gaoge, and ZHONG Yongmin. Window and random weighting-based adaptive unscented Kalman filter[J]. International Journal of Adaptive Control and Signal Processing, 2015, 29: 201-223. doi: 10.1002/acs.2467.
    -1267. doi: 10.13224/j.cnki.jasp.2016.05.030.
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出版歷程
  • 收稿日期:  2017-06-22
  • 修回日期:  2017-11-23
  • 刊出日期:  2018-03-19

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