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Distributed asynchronous measurement system fusion estimation based on inverse covariance intersection algorithm.

Taishan Guo1, Mingquan Wang2, Shuyu Zhou3

  • 1School of Instrumentation and Electronic, North University of China, Taiyuan, 030051, China.

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Summary
This summary is machine-generated.

This study introduces a novel distributed sequential inverse covariance intersection (DSICI) algorithm for state estimation in asynchronous systems with missing data. The DSICI algorithm improves accuracy by 33% while maintaining data consistency.

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Data Fusion

Background:

  • State estimation in multi-source systems is challenging due to asynchronicity and missing measurements.
  • Existing fusion algorithms struggle with unknown information correlations between sensors.

Purpose of the Study:

  • To develop a robust distributed fusion algorithm for state estimation in asynchronous systems with missing measurements.
  • To enhance accuracy and consistency in data fusion when sensor information is partially unknown.

Main Methods:

  • A distributed sequential inverse covariance intersection (DSICI) algorithm based on conditional Kalman filtering.
  • Modeling missing measurements using Bernoulli distributed random variables.
  • Synchronization of asynchronous measurements using state iteration.
  • Ensuring local estimator reliability through domain sensor information interaction.

Main Results:

  • The proposed DSICI algorithm achieves higher accuracy and consistency in state estimation.
  • Demonstrated a 33% improvement in accuracy compared to existing methods.
  • Achieved an iteration time of less than 3 milliseconds, indicating computational efficiency.

Conclusions:

  • The DSICI algorithm effectively addresses state estimation challenges in multi-source asynchronous systems with missing data.
  • The method provides a reliable and accurate solution for data fusion with unknown sensor information.
  • The algorithm shows significant performance improvements and practical applicability.