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International Journal of Aerospace Engineering
Volume 2015, Article ID 381478, 9 pages
Research Article

A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems with Additive Noise

1School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China
2School of Electrical and Control Engineering, Xi’an University of Science & Technology, Xi’an 710054, China
3713th Institute of China Shipbuilding Industry Corporation, Zhengzhou 450002, China

Received 27 February 2015; Accepted 18 May 2015

Academic Editor: Paul Williams

Copyright © 2015 Yong Zhou 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.


The Kalman filter (KF), extended KF, and unscented KF all lack a self-adaptive capacity to deal with system noise. This paper describes a new adaptive filtering approach for nonlinear systems with additive noise. Based on the square-root unscented KF (SRUKF), traditional Maybeck’s estimator is modified and extended to nonlinear systems. The square root of the process noise covariance matrix Q or that of the measurement noise covariance matrix R is estimated straightforwardly. Because positive semidefiniteness of Q or R is guaranteed, several shortcomings of traditional Maybeck’s algorithm are overcome. Thus, the stability and accuracy of the filter are greatly improved. In addition, based on three different nonlinear systems, a new adaptive filtering technique is described in detail. Specifically, simulation results are presented, where the new filter was applied to a highly nonlinear model (i.e., the univariate nonstationary growth model (UNGM)). The UNGM is compared with the standard SRUKF to demonstrate its superior filtering performance. The adaptive SRUKF (ASRUKF) algorithm can complete direct recursion and calculate the square roots of the variance matrixes of the system state and noise, which ensures the symmetry and nonnegative definiteness of the matrixes and greatly improves the accuracy, stability, and self-adaptability of the filter.