Abstract
There has been significant recent interest of particle filters for nonlinear state estimation. Particle filters evaluate a posterior probability distribution of the state variable based on observations in Monte Carlo simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. We propose in this paper a novel particle filter, which combines the ideas of Gaussian sum filter based on the Gaussian mixture approximation of the posteriori distribution and Evolution strategies based particle filter using selection process in evolution strategies. Numerical simulation study indicates the potential to create high performance filters for nonlinear state estimation.
This work is partially supported by the Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (C)(2)14550447.
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Uosaki, K., Hatanaka, T. (2005). Nonlinear State Estimation by Evolution Strategies Based Gaussian Sum Particle Filter. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_91
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DOI: https://doi.org/10.1007/11552413_91
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