Since the landmark work of the Kalman filter in the 1960s, considerable effort has been devoted to a variety of novel filters for nonlinear estimation. Among them, the class of nonlinear Gaussian filters plays a key role. This paper briefly revisits the state-of-the-art developments, highlights a part of interesting and new findings and raises some of our concerns.