State augmentation-based iterated divided difference filtering

Due to the drawbacks in the two typical iterated divided difference filters with additive measurement noise, a state augmentation-based iterated divided difference filter (SIDDF) is proposed in this paper. In each iterated step of the measurement updates of the SIDDF, the state is firstly augmented with the measurement noise and then propagated using the same measurement update steps as the traditional divided difference filter, which made the state statistically independent of the measurement noise in the iterated steps. This filter approach is then applied to a benchmark problem of estimating the trajectory of an entry body from discrete-time noisy range data measured by a radar system. Simulation results show that the proposed filter algorithm can produce better estimation results than those of the previous filter algorithms.