English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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

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