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Distributed State Fusion Estimation of Multi-Source Localization Nonlinear Systems.

Peng Liu1,2, Shuyu Zhou1,2,3, Peng Zhang1,2,3

  • 1Academy for Advanced Interdisciplinary Research, North University of China, Taiyuan 030051, China.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel distributed algorithm for reliable multi-source localization in nonlinear systems with bounded noise. The dual set-membership filtering method enhances state estimation accuracy and system reliability.

Keywords:
distributed fusion strategydual set-membership filteringmulti-source localizationunknown and bounded noise

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Area of Science:

  • Control Engineering
  • Signal Processing
  • Estimation Theory

Background:

  • State estimation in nonlinear systems with unknown, bounded noise presents significant challenges.
  • Ensuring reliability in multi-source localization systems is critical for accurate positioning.

Purpose of the Study:

  • To propose a distributed sequential ellipsoidal intersection fusion estimation algorithm for multi-source nonlinear localization systems.
  • To enhance the reliability and accuracy of state estimation under unknown and bounded noise conditions.

Main Methods:

  • Modeling unknown and bounded noise using bounded ellipsoidal regions.
  • Designing local estimators with dual set-membership filtering to handle noise interference.
  • Employing a first-order conditional gradient algorithm for efficient optimization of semi-infinite constrained problems.
  • Developing a distributed sequential ellipsoid intersection fusion algorithm for combining local estimates.

Main Results:

  • The proposed algorithm effectively filters noise interference using local estimators.
  • Fusion of local estimates yields more accurate localization results with reduced computational cost.
  • Demonstrated stability and reliability of the distributed fusion algorithm through simulations.

Conclusions:

  • The developed algorithm ensures the reliability of nonlinear localization systems with unknown and bounded noise.
  • Distributed fusion estimation significantly improves localization accuracy and computational efficiency.
  • The approach is validated for multi-source nonlinear systems, offering a robust solution for state estimation.