An Adaptive Unscented Kalman Filter Approach for State Estimation of Nonlinear Continuous-Discrete System

The paper presents algorithms for solving the nonlinear filtering problem using an unscented Kalman filter and an adaptive unscented Kalman filter. Detailed of algorithm adaptive unscented Kalman filter is provided. Step-by-step schemes of filtering algorithms on the basis of which the corresponding software is developed are given. Efficiency of nonlinear filtering algorithms application is investigated on the example of nonlinear continuous-discrete model. Simulations conducted on the model structure of dynamic system indicate that the adaptive unscented Kalman filter is superior to the conventional standard unscented Kalman filter in terms of estimation accuracy and stability.