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    DUAN Chenhao,WANG Jue,DENG Zhixin.Pseudolite dynamic tracking and positioning algorithm based on square root UKF[J].Journal of Hebei University of Science and Technology,2020,41(6):493-499
    基于平方根UKF的偽衛星動態跟蹤定位算法
    Pseudolite dynamic tracking and positioning algorithm based on square root UKF
    Received:August 09, 2020  Revised:October 16, 2020
    DOI:10.7535/hbkd.2020yx06003
    中文關鍵詞:  算法理論  動態定位跟蹤  偽衛星  平方根濾波  卡爾曼濾波算法  非線性
    英文關鍵詞:algorithm theory  dynamic positioning and tracking  pseudolite  square root filtering  Kalman filter algorithm  nonlinearity
    基金項目:國家“十三五”重點研發計劃基金資助項目(2016YFB0502100,2016YFB0502102)
    Author NameAffiliationE-mail
    DUAN Chenhao The 54th Research Institute of CETC,Shijiazhuang  
    WANG Jue The 54th Research Institute of CETC,Shijiazhuang 13833186911@139.com 
    DENG Zhixin The 54th Research Institute of CETC,Shijiazhuang;State Key Laboratory of Satellite Navigation System and Equipment Technology,Shijiazhuang  
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    中文摘要:
          為了解決傳統Kalman濾波在處理非線性系統時的局限性,以及擴展Kalman濾波(EKF)在處理強非線性系統時發散性和精度較差的問題,結合動態導航系統中的目標跟蹤定位問題,在不敏Kalman濾波(UKF)算法的基礎上,提出了一種基于平方根UKF的動態跟蹤定位算法,在遞推運算過程中采用協方差矩陣的平方根代替傳統算法計算過程中的協方差矩陣。MATLAB仿真結果表明,平方根UKF算法的精度比EKF提升了54.7%,比UKF提升了14.8%。所提出的算法解決了Kalman處理非線性系統的局限性以及傳統EKF和UKF算法精度不高的問題,為偽衛星系統的高精度定位研究提供了有力支撐。
    英文摘要:
          In order to solve the limitation of traditional Kalman filter in dealing with nonlinear system and the problem of divergence and poor accuracy of extended Kalman filter (EKF) in dealing with strong nonlinear system, a dynamic tracking and positioning algorithm based on square root UKF was proposed based on the unscented Kalman filter (UKF) algorithm in combination with the problem of target tracking and positioning in dynamic navigation system. In the process of recursive operation, the square root of covariance matrix was used to replace the covariance matrix in the calculation process of covariance algorithm. The MATLAB simulation results show that the accuracy of the square root UKF algorithm is 54.7% higher than that of the EKF algorithm, and 14.8% higher than that of the UKF algorithm. The proposed algorithm solves the limitation of Kalman processing nonlinear system and the problem of the low accuracy of traditional EKF and UKF algorithms, and provides a strong support for the high-precision positioning of pseudolite system.
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