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A novel uncertainty evaluation method based on the particle filter and beta distribution for data with unknown
Zhenying Cheng1,2, Xu Chen1,2, Liying Liu1,2
1School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, China.
The Review of Scientific Instruments
|September 22, 2023
Summary
This study introduces a new method using particle filters (PF) and beta distribution for measurement uncertainty evaluation, especially for data with unknown or non-Gaussian distributions. The approach accurately estimates uncertainty for various data types, showing good robustness and consistency with other methods.
Area of Science:
- Metrology
- Statistical Analysis
- Data Science
Background:
- Accurate uncertainty evaluation is crucial in metrology.
- Existing methods struggle with data from unknown or non-Gaussian distributions.
- A robust method is needed for diverse measurement scenarios.
Purpose of the Study:
- To propose a novel method for evaluating measurement uncertainty with unknown data distributions.
- To address limitations of traditional methods for non-Gaussian and asymmetric data.
- To provide a versatile tool for optimal estimation and uncertainty quantification.
Main Methods:
- Utilized a state-space model with beta distribution to represent measurement results.
- Employed the particle filter (PF) method for parameter estimation, suitable for non-Gaussian data.
- Calculated best estimates and uncertainties using derived beta distribution parameters.
Main Results:
- The proposed method accurately evaluates measurement uncertainties, particularly for non-Gaussian and asymmetric data.
- Demonstrated good robustness across multiple evaluation scenarios.
- Experimental results for laser interferometer drift errors showed consistency with the Monte Carlo method.
Conclusions:
- The novel method effectively handles measurement uncertainty for data with unknown distributions.
- It is adaptable to various distribution types characterized by beta distribution.
- The approach offers a reliable solution for optimal estimation and uncertainty evaluation in diverse measurement contexts.
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