Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/43607
Title: Locally-linearized Particle Filter
Authors: GAN LUHUI
Keywords: particle filter, Monte Carlo method, Kalman filter, dynamic system, non-linear filter
Issue Date: 29-May-2013
Source: GAN LUHUI (2013-05-29). Locally-linearized Particle Filter. ScholarBank@NUS Repository.
Abstract: Nonlinear dynamic systems with Gaussian noise are widely used in applied fields. Extended Kalman filter (EKF) and particle filter (PF) are two traditional methods in estimating these systems. In the thesis, two algorithms called locally-linearized particle filters are proposed. Both of them combine the idea of EKF and PF. In the first algorithm, process noises are split into two parts. One part is realized in random samples, leaving out a remaining system on which EKF is applied. In the second algorithm, same procedure is conducted, but results from EKF are not the final estimates. Instead, they are used to construct a sampling scheme that generates samples from the target distribution. Results from simulation studies show that both methods gain improvements over EKF and PF.
URI: http://scholarbank.nus.edu.sg/handle/10635/43607
Appears in Collections:Master's Theses (Open)

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