Comment on ´Huber-based unscented filtering and its application to vision-based relative navigation´

In a recent paper published in IET Radar, Sonar, and Navigation, Wang et al. [Vol. 4, (1), p. 134-141, 2010] develop a robust sigma-point Kalman filter using Huber´s generalised maximum likelihood estimation technique. The key step in this development is to recast the sigma-point Kalman filter update step into the form of a linear regression problem. In Wang et al., the particular type of sigma-point Kalman filter investigated is the unscented Kalman filter (UKF) [ See IEEE Trans. Autom. Control, Vol. 45, (3), p. 477-4822, 2000]. Another example of a sigma-point Kalman filter is the divided difference filter (DDF) [See Automatica, Vol. 36, (11), p. 1627-1638, 2000].