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- W2387380483 abstract "Based on the principle of Bayesian filtering theory,the basic idea and algorithm description of Extended Kalman Filter(EKF) and particle filter(PF) are introduced.Also,the estimation accuracies of EKF and PF are analyzed and the applicative conditions of the two different methods are given.EKF is a linearization technique which uses linear transformation of first order Taylor series expansion to approximate the nonlinear model.Particle filter represents the required posterior probability density by discrete random measures which are composed of weighted particles.The ex-perimental results demonstrate that the PF approach outperforms the EKF algorithm under strong nonlinear and non-Gaussian environment,while PF algorithm and EKF algorithm have the same tracking performance under weak nonlinear and non-Gaussian environment,but the PF is suffers from high computation complexity." @default.
- W2387380483 created "2016-06-24" @default.
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- W2387380483 date "2012-01-01" @default.
- W2387380483 modified "2023-09-24" @default.
- W2387380483 title "EKF and PF algorithm and their applications to target tracking" @default.
- W2387380483 hasPublicationYear "2012" @default.
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