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Efficient Nonlinear Measurement Updating based on Gaussian Mixture Approximation of Conditional Densities
2007
American Control Conference (ACC)
Filtering or measurement updating for nonlinear stochastic dynamic systems requires approximate calculations, since an exact solution is impossible to obtain in general. We propose a Gaussian mixture approximation of the conditional density, which allows performing measurement updating in closed form. The conditional density is a probabilistic representation of the nonlinear system and depends on the random variable of the measurement given the system state. Unlike the likelihood, the
doi:10.1109/acc.2007.4282269
dblp:conf/acc/HuberBH07
fatcat:pnhrvxkwibgsjpnc3eojadzkbi