State Estimation of Nonlinear Systems Using Novel Adaptive Unscented Kalman Filter

This paper investigates the application of multi-sensor data fusion (MSDF) teclmique to enhance the state estimation of a nonlinear plant. The proposed method is based on Kalman filters approach to improve the state estimation obtained by the novel adaptive unscented Kalman filter (AUKF). The common trend for the KF implementation assumes pre-specified fixed distribution matrices for both process and measurement noises. Here, however, the variance matrices for both process and measurement noise signals are assumed unknown a priori and thus incrementally estimated and updated using a sliding time window paradigm within which an estimation of the noise variance is calculated and adaptively updated each time the window is shifted forward. The proposed methodology is tested on a simulated continuous stirred tank reactor (CSTR) problem to estimate 4 states of this nonlinear plant. The simulation results demonstrate the superiority of the suggested method in state estimation compared with a previously reported approach.