Typical iterated filters, such as the iterated extended Kalman filter (IEKF), KF (IUKF), and , have been developed to improve the linearization point (or density) ofthe likelihood linearization in the well-known extended KF (EKF) and unscented KF (UKF). A shortcoming of typical iterated filters is thatthey do not treat the linearization of the transition model of the system. To remedy this shortcoming, we introduce dynamically iterated filters (DIFs), a unified framework for iterated linearization-based nonlinear filters that deals with nonlinearities in both the transition modeland the likelihood, thereby constituting a generalization of the afore mentioned iterated filters. We further establish a relationship between the general DIF and the approximate iterated Rauch–Tung–Striebel smoother. This relationship allows for a Gauss–Newton interpretation, which in turn enables explicit step-size correction, leading to dampedversions of the DIFs. The developed algorithms, both damped and non-damped, are numerically demonstrated in three examples, showing superior mean squared error as well as improved parameter tuning robustness as compared to the analogous standard iterated filters.