A novel filtering model for DVL aided inertial navigation system based on observability degree analysis
作者： Huapeng Yu , Yanli Geng , Hai Zhu , Nuan Jiang , Jianhua Chen
DOI: 10.1109/CGNCC.2016.7828765
关键词:
摘要: Because of its strong autonomy and high reliability, doppler velocity log (DVL) aided inertial navigation system (INS) has been widely used for autonomous underwater vehicle. Focusing on the aspect long-term INS/DVL integrated navigation, a novel reduced-order Kalman filtering algorithm with primary influencing variables errors is proposed, then observability degree information states are obtained from eigenvalues normalized posterior error covariance matrix to check effectiveness designed model. Experiments done collected field test data demonstrate advantages positioning precision proposed Experimental results shows that, model effective, achieved be better than 3‰ whole voyage within 5 hours under about 4 knots speed.
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