What is particle filtering?
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Particle filtering is a powerful sequential Monte Carlo method used to estimate hidden states of parameters of interest from noisy measurements obtained from dynamic systems. It involves randomly drawing samples, called particles, from all possible hidden states according to previous knowledge or prior information. Degeneracy occurs when only a few particles locate in high-probability regions, while the others locate in low-probability regions. Resampling is used to tackle degeneracy but can lead to particle impoverishment, which destroys particle diversity. To address this, a scheme is proposed to ensure that the particles remain adequately diverse during the prediction stage. When noisy measurements become available, the particles are herded to form a better approximation of the posterior probability density function of the hidden state, allowing for accurate estimation or inference .
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| Papers (5) | Insight |
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27 Sep 2021 | Particle filtering is a group of filtering methods based on recursive Bayesian filters used to estimate the state of a nonlinear system with non-Gaussian noise sources. |
| Particle filtering is an effective data-assimilation method for low-dimensional, nonlinear systems, which is easy to implement and allows for the inclusion of model error, parameters, and controls in the state vector. | |
| Particle filtering is a method that uses a set of random samples, called particles, to approximate distributions. It deploys the sequential Monte Carlo method and resampling to handle nonlinearity and non-Gaussianity. | |
10 Nov 2022 1 Citations | Particle filtering is a sequential Monte Carlo method used to estimate hidden states of parameters of interest from noisy measurements obtained from dynamic systems. |
10 Nov 2022 1 Citations | Particle filtering is a sequential Monte Carlo method used to estimate hidden states of parameters of interest from noisy measurements obtained from dynamic systems. |
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