Comparison of standard and modified recursive state estimation techniques for nonlinear systems

This paper deals with recursive state estimation for nonlinear systems. A new set of sigma-points for the unscented Kalman filter is proposed as well as a way to substitute a nonlinear output with a linear one. The importance of the function of the state which must be estimated is also illustrated and the need for taking it into account when designing the state estimator. All the proposed methods are compared with standard extended Kalman filter, unscented Kalman filter and particle filter with sampling importance resampling using simulations. The results show that the modifications proposed in some cases lead to considerable reduction in estimation error.