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For nonlinear state space systems with additive noises, sometimes the number of process noise signals could be less than the dimension of the state space. In order to improve the accuracy and stability of nonlinear state estimation, this paper provides for the first time the derivation of the full information estimator (FIE) for such nonlinear systems. We verify our derivation of the FIE by firstly...
In order to predict future observations of a noise-driven system, we have to find a model that exactly or at least approximately describes the behavior of the system so that the current system state can be recovered from past observations. However, sometimes it is very difficult to model a system accurately, such as real ocean waves. It is therefore particularly interesting to analyze ocean wave properties...
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