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Event-triggered sequential fusion estimation with correlated noises.

Liping Yan1, Lu Jiang2, Yuanqing Xia3

  • 1Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China; University of Windsor, Windsor N9B3P4, Canada.

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|August 11, 2019
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Summary
This summary is machine-generated.

This study introduces event-triggered sequential fusion estimation for multi-sensor systems. It reduces energy waste while accurately estimating states with correlated noises using a novel algorithm.

Keywords:
Correlated noisesEvent-triggered mechanismSequential fusion estimation

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Information Theory

Background:

  • Multi-sensor systems are crucial for enhanced data acquisition.
  • Correlated noises in sensors and systems pose significant estimation challenges.
  • Traditional estimation methods can be energy-intensive due to continuous data transmission.

Purpose of the Study:

  • To develop an energy-efficient event-triggered sequential fusion estimation algorithm.
  • To address correlated measurement and system noises in multi-sensor systems.
  • To minimize estimation error covariance while reducing communication load.

Main Methods:

  • An event-triggered communication mechanism is implemented.
  • A sequential fusion estimation algorithm is designed based on linear minimum covariance.
  • Standard values for correlation parameters are defined to ensure algorithm convergence.

Main Results:

  • The proposed algorithm effectively reduces energy consumption through event-triggered communication.
  • Accurate state estimation is achieved despite correlated noises.
  • An upper bound for the estimation error covariance is derived, demonstrating algorithm performance.

Conclusions:

  • The event-triggered sequential fusion estimation algorithm offers an efficient solution for multi-sensor systems with correlated noises.
  • The method balances estimation accuracy with significant energy savings.
  • The defined correlation parameters are key to ensuring the algorithm's stability and convergence.