ABSTRACT
The extended Kalman filter (EKF) has been one of the most widely used methods for tracking and estimation based on its apparent simplicity, optimality, tractability, and robustness. However, after more than 30 years of experience with it, the tracking and control community has concluded that the EKF is difficult to implement, difficult to time, and only reliable for systems that are almost linear on the time scale of the update intervals. This chapter reviews the unscented transformation (UT), a mechanism for propagating mean and covariance information through nonlinear transformations, and describes its implications for data fusion. This method is more accurate, is easier to implement, and uses the same order of calculations as the EKF. Furthermore, the UT permits the use of Kalman-type filters in applications where, traditionally, their use was not possible. For example, the UT can be used to rigorously integrate artificial intelligence-based systems with Kalman-based systems.
