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Distributed minimum error entropy with fiducial points Kalman filter for state tracking.

Haiquan Zhao1, Boyu Tian1

  • 1Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, China.

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|December 10, 2024
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Summary
This summary is machine-generated.

This study introduces robust Kalman filter algorithms for multi-sensor networks, improving state estimation in non-Gaussian noise. The new methods enhance accuracy and reduce communication load in distributed systems.

Keywords:
Consensus averageDistributed Kalman filterMinimum error entropy with fiducial pointsNon-Gaussian noiseSensor network

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Information Fusion

Background:

  • Distributed Kalman filters (DKF) are essential for multi-sensor networks but struggle with non-Gaussian noise.
  • Accurate state estimation is critical for applications like navigation and power system monitoring.

Purpose of the Study:

  • To develop robust algorithms for state estimation in multi-sensor networks, particularly in non-Gaussian noise environments.
  • To address the communication burden associated with centralized information fusion.

Main Methods:

  • A regression equation incorporating all sensor node information was constructed.
  • Minimum Error Entropy with Fiducial Points (MEEF) standard was applied for robust information fusion.
  • Centralized MEEF KF (CMEEF-KF) and distributed MEEF KF (DMEEF-KF) algorithms were developed.
  • A distributed MEEF extended Kalman filter was proposed for nonlinear problems.

Main Results:

  • The proposed CMEEF-KF algorithm demonstrates robustness against non-Gaussian noise and outliers.
  • DMEEF-KF effectively reduces communication overhead by enabling neighborhood-only information exchange.
  • The algorithms were validated through simulations in land vehicle navigation and power system state estimation.

Conclusions:

  • The developed MEEF-based Kalman filter algorithms significantly enhance state estimation accuracy and robustness in challenging sensor network environments.
  • DMEEF-KF offers a practical solution for large-scale sensor networks by optimizing communication efficiency.
  • The proposed methods are effective for both linear and nonlinear state estimation problems.