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Localization is a crucial part in robot navigation. The significance of this issue is to the extent that further achievements in terms of robot control is highly dependent on that. There are various methods addressing this subject, among which the extended Kalman filter is proved to be one of the most successful ones. In classic extended Kalman filter, covariance matrixes of process noise Q and measurement...
In this study, a new input estimation method is proposed for targets with non-linear dynamics and unknown inputs (manoeuvres), where measurement model is corrupted with both additive and multiplicative noises. First, the authors proposed an innovative model by adding unknown inputs as a new state to the original state vector and constructed an augmented state vector. Therefore, manoeuvring model turns...
In this paper a new unknown input estimation method is proposed for a class of nonlinear stochastic systems in the presence of time dependent unknown inputs, when the system states and process noises are unknown but bounded. In this study, a new augmented state vector is constructed by augmenting unknown inputs as a new state to the original state vector. Then a recursive algorithm based on unknown...
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