Ship navigation via GPS/IMU/LOG integration using adaptive fission particle filter
To achieve a continuous and accurate navigation solution that avoids interruption, GPS is integrated with dead reckoning techniques such as inertial navigation and speed log. A common approach to obtain a low-cost navigation solution is to utilize a multisensor system consisting of MEMS-based inertial measurement unit (IMU), and the whole system is integrated with GPS. However, these sensors provide significant inherent errors because of their complex error characteristics. Particle filter (PF) is considered as a nonlinear filtering technique to accommodate for arbitrary inertial sensor characteristics and motion dynamics. An adaptive fission PF (AFPF) method is proposed to overcome the shortcoming of sample impoverishment problem and improve particle quality. Moreover, further improvement of the MEMS-based IMU/GPS integration navigation system performance during GPS unavailability is achieved by integrating measurement updates from speed log data. The performance of the proposed GPS/IMU/LOG integration using AFPF is evaluated by sea trial, and results indicate that the AFPF solution outperform three different solutions.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
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Supplemental Notes:
- Abstract reprinted with permission of Elsevier.
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Authors:
- Zhang, Chuang
- Guo, Chen
- Zhang, Daheng
- Publication Date: 2018-5-15
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 435-445
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Serial:
- Ocean Engineering
- Volume: 156
- Issue Number: 0
- Publisher: Pergamon
- ISSN: 0029-8018
- EISSN: 1873-5258
- Serial URL: http://www.sciencedirect.com/science/journal/00298018
Subject/Index Terms
- TRT Terms: Global Positioning System; Inertial navigation systems; Measurement; Navigation; Sensors; Ships
- Subject Areas: Marine Transportation; Vehicles and Equipment;
Filing Info
- Accession Number: 01672417
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 19 2018 9:34AM