Robust Interactive Multimodel INS/DVL Intergrated Navigation System With Adaptive Model Set

被引:14
作者
Qin, Xiaohui [1 ,2 ]
Zhang, Runbang [1 ]
Wang, Guangcai [1 ]
Long, Chengqi [1 ]
Hu, Manjiang [1 ,2 ]
机构
[1] Hunan Univ, Coll Mech & Vehicle Engn, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Peoples R China
[2] Hunan Univ, Wuxi Intelligent Control Res Inst WICRI, Wuxi 214072, Peoples R China
基金
中国博士后科学基金;
关键词
Navigation; Mathematical models; Adaptation models; Heavily-tailed distribution; Sea measurements; Sensors; Noise measurement; Adaptive filters; error state Kalman filter (ESKF); heavy-tailed non-Gaussian noise; inertial navigation system (INS)/Doppler velocity log (DVL) integrated navigation system; KALMAN FILTER; UKF;
D O I
10.1109/JSEN.2023.3252177
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A robust interactive multiple model (RIMM) algorithm with the adaptive model set is proposed to improve the performance of inertial navigation system (INS) and Doppler velocity log (DVL) integrated navigation system under a complex measurement environment with undesirable heavy-tailed non-Gaussian noise. Specifically, an improved Huber kernel function is applied to the robust error state Kalman filter (ESKF) for its benefit of better resisting larger measurement outliers. In addition, a flexible adaptive model set update strategy is proposed where the model set is determined by the current and historical measurement information stored in the sliding window. Also, for the model set update, the main model is no longer fixed, and it will be determined based on the probability weights corresponding to each model. This innovative RIMM algorithm is compared with ESKF, interactive multiple model (IMM), and hybrid IMM (HIMM) through simulations, autonomous underwater vehicle (AUV) lake trial, and semiphysical simulations. Experimental results show that our proposed algorithm has outstanding accuracy and robustness under a heavy-tailed non-Gaussian noise environment.
引用
收藏
页码:8568 / 8580
页数:13
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