An adaptive Gaussian mixture filter whose split operation halves variance along a target direction and whose KL-bound combine step reduces particle count, outperforming baselines on Van der Pol and Lorenz benchmarks.
A Bayesian Filtering Algorithm for Gaussian Mixture Models
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abstract
A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture terms and this is handled here by utilising a Gaussian mixture reduction step after both the time and measurement updates. In addition, a square-root implementation of the unified algorithm is presented and this algorithm is profiled on several simulated systems. This includes the state estimation for two non-linear systems that are strictly outside the class considered in this paper.
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An adaptive split-combine Gaussian mixture filter for nonlinear and multimodal state estimation
An adaptive Gaussian mixture filter whose split operation halves variance along a target direction and whose KL-bound combine step reduces particle count, outperforming baselines on Van der Pol and Lorenz benchmarks.