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Related Concept Videos

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Updated: Aug 19, 2025

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Generalized minimum error entropy Kalman filter for non-Gaussian noise.

Jiacheng He1, Gang Wang2, Huijun Yu1

  • 1School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.

ISA Transactions
|November 28, 2022
PubMed
Summary

A new Kalman-type filter uses generalized minimum error entropy (GMEEKF) to adapt to various noise types. This filter offers improved performance and flexibility compared to existing methods.

Keywords:
Error entropyGeneralized Gaussian kernel functionGeneralized minimum error entropyKalman filter

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Area of Science:

  • Information theoretic learning
  • Signal processing
  • Kalman filtering

Background:

  • Error entropy is a key learning criterion in ITL, but its Gaussian kernel limits noise handling.
  • Existing algorithms struggle with diverse noise types due to fixed kernel shapes.

Purpose of the Study:

  • To develop a novel Kalman-type filter algorithm using the generalized minimum error entropy (GMEEKF) criterion.
  • To enhance adaptability to various noise types by utilizing a generalized Gaussian kernel function.

Main Methods:

  • Derivation of the GMEEKF algorithm.
  • Analysis of mean error, mean square error, and computational complexity.
  • Performance evaluation through simulations and experiments.

Main Results:

  • The GMEEKF algorithm demonstrates superior performance compared to existing Kalman-type filters.
  • The generalized Gaussian kernel allows for flexible shape adjustment, improving noise handling.
  • Analysis confirms the algorithm's effectiveness in terms of error behavior and complexity.

Conclusions:

  • The GMEEKF algorithm offers a flexible and effective approach for Kalman-type filtering.
  • This novel method expands the applicability of error entropy-based algorithms to a wider range of noise conditions.