A Non-Gaussian Bayesian Filter Using Power and Generalized Logarithmic Moments
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In our previous paper, we proposed a non-Gaussian Bayesian filter using power moments of the system state. A density surrogate parameterized as an analytic function is proposed to approximate the true system state, of which the distribution is only assumed Lebesgue integrable. To our knowledge, it is the first Bayesian filter where there is no prior constraints on the true density of the state and the state estimate has a continuous form of function. In this very preliminary version of paper, we propose a new type of statistics, which is called the generalized logarithmic moments. They are used to parameterize the state distribution together with the power moments. The map from the parameters of the proposed density surrogate to the power moments is proved to be a diffeomorphism, which allows to use gradient methods to treat the optimization problem determining the parameters. The simulation results reveal the advantage of using both moments for estimating mixtures of complicated types of functions.
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