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Optimal Distributed Finite-Time Fusion Method for Multi-Sensor Networks under Dynamic Communication Weight.

Hang Yu1, Keren Dai1, Qingyu Li2

  • 1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

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Summary

This study introduces an optimal distributed finite-time fusion filtering method for sensor networks. The novel approach enhances state estimation accuracy by minimizing fusion errors using dynamic communication weights and fast finite-time convergence.

Keywords:
distributed Kalman filterdynamic communication weightfinite-time consensussensor networks

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Area of Science:

  • Distributed Systems
  • Sensor Networks
  • Estimation Theory

Background:

  • Distributed state estimation in sensor networks faces challenges with fusion errors due to incomplete node information.
  • Existing methods may struggle with convergence and accuracy in dynamic network environments.

Purpose of the Study:

  • To develop a novel optimal distributed finite-time fusion filtering method for sensor networks.
  • To address fusion errors and improve state estimation accuracy using dynamic communication weights.
  • To achieve fast finite-time convergence for global information aggregation.

Main Methods:

  • Constructed a local filtering algorithm architecture for fusion error convergence within limited iterations.
  • Determined the maximum number of iterations based on the communication topology graph diameter.
  • Employed matrix weight fusion for optimal estimation (minimum variance) of local filtering results.
  • Introduced Generalized Information Quality (GIQ) to derive relative communication weights, integrated into the fusion algorithm.

Main Results:

  • The proposed method achieves fusion error convergence within a finite number of iterations.
  • Optimal estimation with minimum variance is attained through matrix weight fusion.
  • Dynamic communication weights, based on GIQ and local bias, enhance fusion accuracy.
  • Numerical simulations and experimental tests validated the algorithm's effectiveness and feasibility.

Conclusions:

  • The developed optimal distributed finite-time fusion filtering method effectively minimizes errors in sensor networks.
  • Dynamic communication weights significantly improve the accuracy of distributed state estimation.
  • The algorithm demonstrates fast finite-time convergence and practical applicability.