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Mathematical Problems in Engineering
Volume 2014, Article ID 967127, 10 pages
Research Article

Quasi-Stochastic Integration Filter for Nonlinear Estimation

College of Automation, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin 150001, China

Received 21 October 2013; Revised 18 May 2014; Accepted 24 May 2014; Published 23 June 2014

Academic Editor: Dan Simon

Copyright © 2014 Yong-Gang Zhang et al. 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.


In practical applications, numerical instability problem, systematic error problem caused by nonlinear approximation, and nonlocal sampling problem for high-dimensional applications, exist in unscented Kalman filter (UKF). To solve these problems, a quasi-stochastic integration filter (QSIF) for nonlinear estimation is proposed in this paper. nonlocal sampling problem is solved based on the unbiased property of stochastic spherical integration rule, which can also reduce systematic error and improve filtering accuracy. In addition, numerical instability problem is solved by using fixed radial integration rule. Simulations of bearing-only tracking model and nonlinear filtering problem with different state dimensions show that the proposed QSIF has higher filtering accuracy and good numerical stability as compared with existing methods, and it can also solve nonlocal sampling problem effectively.