Open AccessStudy of Comparing Several Nonlinear Filtering Algorithms in Carrier-based Aircraft Positioning
Author(s) -
Yajie Du,
Jianjun Zhao,
Gang Yao
Publication year - 2020
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 - 10.1088/1742-6596/1549/5/052041
Subject(s) - particle filter , algorithm , kalman filter , nonlinear system , extended kalman filter , nonlinear filter , computer science , radar , filter (signal processing) , ensemble kalman filter , control theory (sociology) , invariant extended kalman filter , filter design , computer vision , artificial intelligence , telecommunications , physics , control (management) , quantum mechanics
In order to improve the positioning accuracy of the landing guidance radar to the carrier aircraft, the nonlinear filtering algorithm is used to estimate the positioning of the carrier aircraft. The main algorithms of nonlinear filtering, such as extended Kalman filter, unscented Kalman filter and particle filter, are analyzed and compared. A typical nonlinear model is simulated to verify the performance of these algorithms. The simulation results show that the estimation accuracy of the particle filter algorithm is better than the other two filter algorithms.