A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems
This paper describes a new adaptive filtering approach for nonlinear systems with additive noise. Based on Square-Root Unscented Kalman Filter (SRUKF), the traditional Maybeck’s estimator is modified and extended to the nonlinear systems, the estimation of square root of the process noise covariance matrix Q or measurement noise covariance matrix R is obtained straightforwardly. Then the positive semi-definiteness of Q or R is guaranteed, some shortcomings of traditional Maybeck’s algorithm are overcome, so the stability and accuracy of the filter is improved greatly.
Ching Kuo Wang and Jing Guo
Y. Zhou et al., "A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems", Applied Mechanics and Materials, Vols. 300-301, pp. 623-626, 2013