Particle Flow Gaussian Sum Particle Filter

Karthik Comandur; Yunpeng Li; Santosh Nannuru

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arXiv · 2211.05104

Particle Flow Gaussian Sum Particle Filter

Karthik Comandur·Yunpeng Li·Santosh Nannuru

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Abstract

Particle flow Gaussian particle flow (PFGPF) uses an invertible particle flow to generate a proposal density. It approximates the predictive and posterior distributions as Gaussian densities. In this paper, we use bank of PFGPF filters to construct a Particle flow Gaussian sum particle filter (PFGSPF), which approximates the predictive and posterior as Gaussian mixture model. This approximation is useful in complex estimation problems where a single Gaussian approximation is not sufficient. We compare the performance of this proposed filter with PFGPF and others in challenging numerical simulations.

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Karthik Comandur, Yunpeng Li, Santosh Nannuru. 2022-11-09. Particle Flow Gaussian Sum Particle Filter. https://arxiv.org/abs/2211.05104

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