Open AccessShip tracking based on the fusion of Kalman filter and particle filter
Author(s) -
Wanjin Xu,
Jiying Li,
Junjie Bai,
Yingying Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2113/1/012017
Subject(s) - particle filter , auxiliary particle filter , ensemble kalman filter , kalman filter , extended kalman filter , resampling , invariant extended kalman filter , tracking (education) , fast kalman filter , computer science , unscented transform , divergence (linguistics) , algorithm , control theory (sociology) , alpha beta filter , sensor fusion , global positioning system , artificial intelligence , moving horizon estimation , psychology , pedagogy , linguistics , philosophy , control (management) , telecommunications
Aiming at the problem of low filtering accuracy and even divergence caused by model mismatch when using extended Kalman filter in ship GPS navigation and positioning state estimation, a positioning ship state estimation algorithm based on the fusion of improved unscented Kalman filter and particle filter is proposed. Compared with the traditional particle filtering algorithm, the algorithm has two improvements: first, the algorithm uses untraced Kalman as the main framework, and uses the optimal estimation of particle updating state by particle algorithm; Secondly, in the resampling process, a resampling algorithm based on weight optimization is proposed to increase the diversity of particles. The simulation results show that not only the particle degradation degree of the particle filter is reduced, but also the particle tracking accuracy is improved.