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Mathematical Problems in Engineering
Volume 2013, Article ID 678943, 8 pages
http://dx.doi.org/10.1155/2013/678943
Research Article

Study on GPS/INS System Using Novel Filtering Methods for Vessel Attitude Determination

Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education and School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China

Received 22 February 2013; Accepted 26 March 2013

Academic Editor: Henryk Śniegocki

Copyright © 2013 Xiyuan Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Any vehicle such as vessel has three attitude parameters, which are mostly defined as pitch, roll, and heading from true north. In hydrographic surveying, determination of these parameters by using GPS or INS technologies is essential for the requirements of vehicle measurements. Recently, integration of GPS/INS by using data fusion algorithm became more and more popular. Therefore, the data fusion algorithm plays an important role in vehicle attitude determination. To improve attitude determination accuracy and efficiency, two improved data fusion algorithms are presented, which are extended Kalman particle filter (EKPF) and genetic particle filter (GPF). EKPF algorithm combines particle filter (PF) with the extended Kalman filter (EKF) to avoid sample impoverishment during the resampling process. GPF is based on genetic algorithm and PF; several genetic operators such as selection, crossover, and mutation are adopted to optimize the resampling process of PF, which can not only reduce the particle impoverishment but also improve the computation efficiency. The performances of the system based on the two proposed algorithms are analyzed and compared with traditional KF. Simulation results show that, comprehensively considering the determination accuracy and consumption cost, the performance of the proposed GPF is better than EKPF and traditional KF.