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Iterated square root unscented Kalman filter and its application in deep sea vehicle navigation
Wang XL(王秀莲); Liu KZ(刘开周); Lin YP(林燕平); Liu B(刘本); Zhao Y(赵洋); Cui SG(崔胜国); Feng XS(封锡盛)
Department水下机器人研究室
Conference Name27th Chinese Control and Decision Conference, CCDC 2015
Conference DateMay 23-25, 2015
Conference PlaceQingdao, China
Source PublicationProceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages4880-4885
Indexed ByEI ; CPCI(ISTP)
EI Accession number20154401482821
WOS IDWOS:000375232906054
Contribution Rank2
ISBN978-1-4799-7016-2
KeywordDeep Sea Navigation System Human Occupied Vehicle (Hov) Unscented Kalman Filter (Ukf) Square Root Unscented Kalman Filter (Srukf) Iterated Square Root Unscented Kalman Filter (Isrukf)
AbstractIt is of vital importance to develop a high accuracy and fast convergence algorithm in deep sea navigation system, since location is essential for the sake of scientific survey and safety in very hazard environment. Unscented Kalman Filter (UKF) is the type of filter, which is designed in order to overrun this problem. However, in case of state estimation of the Human Occupied Vehicle (HOV) via the sensor data obtained from a Long Baseline (LBL) acoustic positioning system, a Doppler Velocity Log (DVL), a depth sensor and a motion sensor, where the nonlinearity degree of dynamic model is high and the operating environment is complex, UKF may give inaccurate results. In this study an iterated square root Unscented Kalman Filter (ISRUKF) is presented. An iterated measurement update procedure is included to increase the approximation accuracy of nonlinear state estimates, and a square root version of UKF is conducive to guarantee numerical stability of the algorithm. Compared with the UKF and square root Unscented Kalman Filter (SRUKF), used in deep-sea vehicle navigation system, the ISRUKF algorithm has potential advantages in convergence speed and location accuracy. Extensive experiment researches have been conducted by using the data obtained from previous sea trial to demonstrate its superiority.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/17189
Collection水下机器人研究室
Corresponding AuthorWang XL(王秀莲)
Affiliation1.School of Information Science and Engineering, Shenyang Ligong University, Shenyang, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, CAS, Shenyang, China
Recommended Citation
GB/T 7714
Wang XL,Liu KZ,Lin YP,et al. Iterated square root unscented Kalman filter and its application in deep sea vehicle navigation[C]. Piscataway, NJ, USA:IEEE,2015:4880-4885.
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