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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46975

Title: Nonlinear Non-Gaussian Filtering Algorithm Based on Cubature Kalman and Particle Filter
Authors: Chien-Hao Tseng
Dah-Jing Jwo
Sheng-Fuu Lin
Contributors: 國立臺灣海洋大學:通訊科學系
Keywords: Particle Filter (PF)
Nonlinear Non-Gaussian
Cubature Kalman Filter
Date: 2013
Issue Date: 2018-06-25T02:26:05Z
Publisher: Applied Mechanics and Materials
Abstract: Abstract: To resolve the nonlinear non-Gaussian tracking problem effectively, a novel filtering algorithm based on Cubature Kalman Filter (CKF) and Particle Filters (PF) is proposed, which is called Cubature Kalman Particle Filter (CPF). CKF is used to generate the importance density function for PF. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian cubature points. It need not compute the Jacobian matrix. Moreover, it makes efficient use of the latest observation information into system state transition density, thus greatly improving the filter performance. The simulation results show that CPF has higher estimation accuracy and less computational load comparing against the widely used Unscented Particle Filter (UPF).
Relation: 380-384, pp. 1323-1326
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46975
Appears in Collections:[通訊與導航工程學系] 演講及研討會

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