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Discrete Dynamics in Nature and Society
Volume 2016 (2016), Article ID 1082837, 10 pages
http://dx.doi.org/10.1155/2016/1082837
Research Article

An Improved Gaussian Mixture CKF Algorithm under Non-Gaussian Observation Noise

College of Automation, Harbin Engineering University, Harbin 150001, China

Received 14 March 2016; Revised 10 June 2016; Accepted 16 June 2016

Academic Editor: Juan R. Torregrosa

Copyright © 2016 Hongjian Wang and Cun Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In order to solve the problems that the weight of Gaussian components of Gaussian mixture filter remains constant during the time update stage, an improved Gaussian Mixture Cubature Kalman Filter (IGMCKF) algorithm is designed by combining a Gaussian mixture density model with a CKF for target tracking. The algorithm adopts Gaussian mixture density function to approximately estimate the observation noise. The observation models based on Mini RadaScan for target tracking on offing are introduced, and the observation noise is modelled as glint noise. The Gaussian components are predicted and updated using CKF. A cost function is designed by integral square difference to update the weight of Gaussian components on the time update stage. Based on comparison experiments of constant angular velocity model and maneuver model with different algorithms, the proposed algorithm has the advantages of fast tracking response and high estimation precision, and the computation time should satisfy real-time target tracking requirements.