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Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network.

Zhan Chen1, Wenxing Fu1, Ruitao Zhang1

  • 1Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China.

Entropy (Basel, Switzerland)
|March 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel distributed cubature information filtering (DCIF) method for improved state estimation in bearing-only sensor networks. The DCIF method enhances system observability, outperforming traditional algorithms in accuracy and stability.

Keywords:
DCIF algorithmbearing-only sensor networkcooperative consistency theorystate estimation

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Area of Science:

  • Engineering
  • Signal Processing
  • Control Systems

Background:

  • State estimation using bearing-only sensors is crucial but faces limited distributed filtering solutions.
  • Traditional nonlinear filtering algorithms often suffer from accuracy errors and divergence.

Purpose of the Study:

  • To propose a novel distributed cubature information filtering (DCIF) method for state estimation in bearing-only sensor networks.
  • To enhance system observability and improve the accuracy and stability of state estimation.

Main Methods:

  • Constructed a system model for bearing-only sensor networks and analyzed system observability.
  • Employed coordinated consistency theory for unified state estimation of maneuvering targets.
  • Developed the distributed cubature information filtering (DCIF) method.

Main Results:

  • The DCIF method enhances system observability, mitigating accuracy errors and divergence.
  • Theoretical proof of consistency convergence for the DCIF method was established.
  • Simulation experiments demonstrated the effectiveness and superiority of DCIF.

Conclusions:

  • The proposed DCIF method is effective for state estimation in bearing-only sensor networks.
  • DCIF offers superior performance compared to traditional nonlinear filtering algorithms.