Comparison of Sequential Monte Carlo Filtering with Kalman Filtering for Nonlinear State Estimation

In this paper different filtering techniques for nonlinear state estimation are explored and compared. We distinguish between approaches that approximate the nonlinear function (extended Kalman filter) and other approaches approximating the distribution of measurements and state (unscented Kalman filter and sequential Monte Carlo filter). The paper is showing both, the algorithms and simulated examples where a vehicle moves along a nonlinear trajectory such as a circle arc or a clothoid. It is shown, that the estimation of the system state can be improved if the nonlinearities were taken into account.