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《Acta Armamentarii》 2011-02
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Research on Integrated Navigation for Autonomous Underwater Vehicle Based on an Improved Unscented Kalman Filter

LIU Ming-yong,HU Jun-wei,LI Wen-bai(School of Marine Engineering,Northwestern Polytechnical University,Xi'an 710072,Shaanxi,China)  
To meet the stability,precision and real-time performance of navigation system for autonomous underwater vehicle(AUV),a novel SINS/DVL integrated navigation method based on improved unscented Kalman filter(UKF) algorithm is presented.By analyzing the characteristics and error model of the low-precision integrated navigation system,a simplified UKF integrated navigation algorithm based on spherical simplex sampling transformation is designed for the additive and complex noise model of the system.Simulation results show that this algorithm can effectively reduce the computational complexity and improve the efficiency of the navigation system without loss of filtering accuracy compared with the traditional UKF algorithm using scaled symmetric sampling.
【Fund】: 国家自然科学基金(50979093);; 新世纪优秀人才计划资助(NCET-06-0877)
【CateGory Index】: TN967.2
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【Citations】
Chinese Journal Full-text Database 2 Hits
1 Li Jun Shen Anwen Song Baowei Xu Demin Assoc. Prof.; College of Marine Engineering, Northwestern Polytechnical University, Xi′an 710072, China.;The methods and shipping trial of the navigation of autonomous underwater vehicles based on Doppler dead reckon[J];Journal of Huazhong University of Science and Technology;2004-01
2 LIAN Jun-xiang, WU Wen-qi, LI Tao, HU Xiao-ping (College of Electromechanical Engineering and Automation, National University of Defense Technology, Changsha 410073, China);Application of Unscented Kalman Filter in AUV Integrated Navigation[J];Journal of Chinese Inertial Technology;2005-01
【Secondary Citations】
Chinese Journal Full-text Database 1 Hits
1 LI Xiao-xuan1, DAI Yong-kang2, WENG Hai-na2, ZHUANG Liang-jie2 (1. Civil Aviation University of China, Tianjin 300300, China; 2. Tianjin Navigation Instrument Research Institute, Tianjin 300131, China);Multiple Model Adaptive Filtering Technique for INS/ESGM Integrated Navigation System[J];Journal of Chinese Inertial Technology;2002-06
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