Scaled Minimum Unscented Multiple Hypotheses Mixing Filter

This work 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) features a pruning step that reduces the filter's computational effort and prevents its estimates from being degraded by very unlikely hypotheses; and 4) has a mixing step with merging depth greater than one. Second, we present a result revealing the conservativeness of one of the scaled unscented transform forms.