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In this paper, we consider state estimation for Stochastic Hybrid Systems (SHS). These are systems that possess both continuous-valued and discrete-valued dynamics. For SHS with nonlinear hybrid dynamics and/or non-Gaussian disturbances, state estimation can be implemented as an Interacting Multiple Model (IMM) particle filter. However, a disadvantage of particle filtering is the computational load...
The sample-based recursive prediction of discrete-time nonlinear stochastic dynamic systems requires a regular reapproximation of the Dirac mixture densities characterizing the state estimate with an exponentially increasing number of components. For that purpose, a systematic approximation method is proposed that is deterministic and guaranteed to minimize a new type distance measure, the so called...
In this paper we attempt to lay the foundation for a novel filtering technique for the fusion of two random vectors with imprecisely known stochastic dependency. This problem mainly occurs in decentralized estimation, e.g., of a distributed phenomenon, where the stochastic dependencies between the individual states are not stored. Thus, we derive parameterized joint densities with both Gaussian marginals...
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