文献类型：期刊文献

中文题名：Improved Square-Root UKF Algorithm for State Estimation of Nonlinear Systems

英文题名：Improved square-root UKF algorithm for state estimation of nonlinear systems

作者：Liu, Ji[1]; Gu, Xing-Sheng[1]

机构：[1] Research Institute of Automation, East China University of Science and Technology, Shanghai 200237, China

年份：2010

卷号：27

期号：1

起止页码：74

中文期刊名：Journal of Donghua University(English Edition)

外文期刊名：Journal of Donghua University (English Edition)

收录：EI(收录号：20121414918025);Scopus

基金：Shanghai Commission of Science and Technology,China(No.08JC1408200);Shanghai Leading Academic Discipline Project,China(No.B504)

语种：英文

中文关键词：square-root unscented Kalman filter; filter invalidation; Cholesky factor update; state estimation

外文关键词：Errors - Nonlinear systems - Bandpass filters - Covariance matrix - Kalman filters - Nonlinear analysis

摘要：The square-root unscented Kalman filter （SR- UKF） for state estimation probably encounters the problem that Cholesky factor update of the covariance matrices can＇t be implemented when the zero＇th weight of sigma points is negative or the mnnerical computation error becomes large during the faltering procedure. Consequently the filter becomes invalid. An improved SR-UKF algorithm （ISR- UKF） is presented for state estimation of arbitrary nonlinear systems with linear measurements. It adopts a modified form of predicted covariance matrices, and modifies the Cholesky factor calculation of the updated covariance matrix originating from the square-root covariance filtering method. Discussions have been given on how to avoid the filter invalidation and further error accumulation. The comparison between the ISR-UKF and the SR-UKF by simulation also shows both have the same accuracy for state estimation. Finally the performance of the improved filter is evaluated under the impact of model mismatch. The error behavior shows that the ISR-UKF can overcome the impact of model mismatch to a certain extent and has excellent trace capability.
The square-root unscented Kalman filter (SR-UKF) for state estimation probably encounters the problem that Cholesky factor update of the covariance matrices can't be implemented when the zero'th weight of sigma points is negative or the numerical computation error becomes large during the filtering procedure. Consequently the filter becomes invalid. An improved SR-UKF algorithm (ISR-UKF) is presented for state estimation of arbitrary nonlinear systems with linear measurements. It adopts a modified form of predicted covariance matrices, and modifies the Cholesky factor calculation of the updated covariance matrix originating from the square-root covariance filtering method. Discussions have been given on how to avoid the filter invalidation and further error accumulation. The comparison between the ISR-UKF and the SR-UKF by simulation also shows both have the same accuracy for state estimation. Finally the performance of the improved filter is evaluated under the impact of model mismatch. The error behavior shows that the ISR-UKF can overcome the impact of model mismatch to a certain extent and has excellent trace capability. Copyright ? 2010 Editorial Department of Journal of Donghua University.