Open AccessPerformance comparison of CKF and UKF in passive state noise of UAV
Author(s) -
Huang qingshun,
Bing Deng
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 - 10.1088/1742-6596/1682/1/012068
Subject(s) - tracking (education) , kinematics , matlab , noise (video) , control theory (sociology) , stability (learning theory) , state (computer science) , computer vision , computer science , artificial intelligence , kalman filter , algorithm , image (mathematics) , machine learning , psychology , pedagogy , physics , control (management) , classical mechanics , operating system
Interacting multiple model (IMM) is widely used in target tracking, and is recognized as the most effective method for maneuvering target tracking. In order to remove the motion state noise in UAV passive location, this paper uses the idea of target tracking algorithm for reference. By setting the observation station data points in advance, introducing detection factors into CV and CT models, and then constructing measurement equations through geometric kinematics and fusing IMM algorithm, the filtering performance of IMM-CKF-CV-CT and IMM-UKF-CV-CT under typical s-maneuver in two-dimensional plane is studied. The simulation results of MATLAB show that IMM-CKF-CV-CT has higher accuracy and stronger stability than IMM-UKF.