Henrique Marra Menegaz

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In this paper, we propose a systematization of the (discrete-time) Unscented Kalman Filter (UKF) theory. We gather all available UKF variants in the literature, present corrections to theoretical inconsistencies, and provide a tool for the construction of new UKF’s in a consistent way. This systematization is done, mainly, by revisiting the concepts of(More)
In this work we propose a new set of sigma points for the Unscented Transform that uses the minimum number of points. We than compare this new set with the symmetric set, the reduced set, and the spherical set. Simulations comparing this sets are done to verify the properties of this set and to verify their transforms. Lastly, we simulate each of these sets(More)
Switching Systems P.H.R.Q.A. Santana, H.M. Menegaz, G.A. Borges, and J.Y. Ishihara Abstract This work addresses the problem of stochastic state estimation for hybrid Markovian switching systems. The proposed Multiple Hypotheses Mixing Filter (MHMF) combines the Generalized Pseudo Bayes’ (GPB) multiple hypotheses tracking with the Interacting Multiple(More)
The spring-like behavior is an inherent condition for human walking and running. Since leg stiffness k(leg) is a parameter that cannot be directly measured, many techniques has been proposed in order to estimate it, most of them using force data. This paper intends to address this problem using an Extended Kalman Filter (EKF) based on the Spring-Loaded(More)
This works brings two new contributions. First, it introduces the Scaled Minimum Unscented Multiple Hypotheses Mixing Filter, a novel filter for hybrid dynamical systems that 1) uses a new minimum set of sigma points along with the scaled unscented transform in a hybrid framework; 2) can estimate the Markovian Transition Probability Matrix in real-time; 3)(More)
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