A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems
Yong Hua Zhou, Yu Feng Zhang, Ju Zhong Zhang · Applied Mechanics and Materials · 2013
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.