The Square-Root Spherical Simplex Unscented Kalman Filter for State and Parameter Estimation

摘要：

This article presents a variant of sigma-point Kalman filters family called square-root spherical simplex unscented Kalman filter for online Bayesian recursive estimation of the state and parameter of nonlinear systems with non-Gaussian statistics.The algorithm consists of a better-behaved spherical simplex unscented transformation to build the sigma point set.The square-root forms have equal or marginally better estimation accuracy when compared to the standard forms,but at the added benefit of reduced computational cost for certain nonlinear non-Gaussian systems and a consistently increased numerical stability as all resulting covariance matrices are guaranteed to stay semi-positive definite.Simulation results indicate that the consistent performance benefits of the proposed filter make it an attractive alternative to the state and parameter estimation in general state-space models.

作者: Xiaojun Tang Xiaobei Zhao Xubin Zhang

作者单位: School of Aeronautics,Northwestern Polytechnical University,Xian,China

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）