شماره ركورد كنفرانس :
2727
عنوان مقاله :
Kalman Filter-based Multisensor Data Fusion
عنوان به زبان ديگر :
Kalman Filter-based Multisensor Data Fusion
پديدآورندگان :
Entezari Rahim نويسنده Malek-e-Ashtar University of Technology (MUT) - Electrical and Electronic Engineering University Complex (EEEUC) , Sedaghat Reza نويسنده Malek-e-Ashtar University of Technology (MUT) - Electrical and Electronic Engineering University Complex (EEEUC) , Rashidi AliJabar نويسنده Malek-e-Ashtar University of Technology (MUT) - Electrical and Electronic Engineering University Complex (EEEUC)
كليدواژه :
Discrete Kalman filter , Data Fusion Architectures , Multisensor Data Fusion , State estimation
عنوان كنفرانس :
اولين كنفرانس بين المللي دستاوردهاي نوين پژوهشي در مهندسي برق و كامپيوتر
چكيده لاتين :
In this paper, we evaluate the performance of two multisensor data fusion techniques for state estimation by
applying Kalman filter. These methods are state vector fusion and measurement fusion. The comparisons of these methods are demonstrated for a target tracking problem and analysis is performed by means of the components of the error covariance
matrix. According to environmental conditions, we should select one of the fusion architectures in order to fuse data obtained
from respective sensors. The simulation results show that the measurement fusion methods generally have better state
estimation performance over the state vector fusion methods.
شماره مدرك كنفرانس :
4240260
