The underlying reason the the EKF is the defacto standard is that the difficulty in all nonlinear Kalman type filters is the process of linearization. Julier and Uhlmann's filter is a good one, however they have said too much and many of the things they publish have been disputed.  Firstly the UKF is a special example of something called a linear regression kalman filter, though they rebutt that so is a Particle Filter which predates the Kalman Filter.  The EKF relies on Taylor Series to perform linearization and the UKF relies on linear regression, the main difference between the UKF and the LRKF is that the LRKF selects samples about the mean randomly and the UKF uses the so called Unscented Transform which picks points based on the linearly independant vectors of the matrix square root.  This is nice and helps improve the performance of the sampling algorithm.  It doesn't really dethrone the ekf though.  (Thats just my opinion.)