A federated filtering personal navigation algorithm based on MEMS-INS/GPS integrated - 文章详情页

作者：

Pan, Kai (Pan, Kai.) | Ren, Mingrong (Ren, Mingrong.) | Wang, Pu (Wang, Pu.) | Liu, Yanhong (Liu, Yanhong.)

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EI Scopus

摘要：

Designed　a　zero　velocity　detection　algorithm　based　on　adaptive　threshold　in　order　to　accurately　detect　the　zero　velocity　moment,　and　through　the　Kalman　zero　velocity　correction　algorithm　timely　correct　the　error　of　inertial　navigation　system(INS).　Because　at　zero　velocity　correction　time　position　and　heading　information　is　observed,　by　unscented　Kalman　filter　(UKF)　INS　and　GPS　are　integrated　navigation　and　compared　with　extended　Kalman　filter　(EKF).　Through　the　experiment　under　the　condition　of　strong　nonlinear　UKF　has　better　filtering　performance　than　EKF,　and　also　better　than　the　EKF　on　rapidity.　Using　Kalman　zero　velocity　correction　and　UKF　building　federal　filtering　model　can　effectively　inhibit　the　INS　position　drift　problem.　At　the　same　time,　in　straight　line　phase,　the　use　of　GPS　location　information　correct　course　angle.　When　GPS　signal　loss　within　a　certain　time＇　the　integrated　navigation　system　can　get　the　right　course　angle.　Thus,　the　positioning　accuracy　of　the　integrated　navigation　system　is　obviously　improved.　©　2016　IEEE.

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作者机构：

[ 1 ] [Pan, Kai]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 2 ] [Pan, Kai]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 3 ] [Pan, Kai]Beijing Laboratory for Urban Mass Transit, Beijing; 100124, China
[ 4 ] [Pan, Kai]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 5 ] [Ren, Mingrong]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 6 ] [Ren, Mingrong]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 7 ] [Ren, Mingrong]Beijing Laboratory for Urban Mass Transit, Beijing; 100124, China
[ 8 ] [Ren, Mingrong]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 9 ] [Wang, Pu]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 10 ] [Wang, Pu]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 11 ] [Wang, Pu]Beijing Laboratory for Urban Mass Transit, Beijing; 100124, China
[ 12 ] [Wang, Pu]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 13 ] [Liu, Yanhong]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 14 ] [Liu, Yanhong]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 15 ] [Liu, Yanhong]Beijing Laboratory for Urban Mass Transit, Beijing; 100124, China
[ 16 ] [Liu, Yanhong]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China

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