Quasi-stochastic integration filter for nonlinear estimation

Abstract

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. © 2014 Yong-Gang Zhang et al.