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Set-valued mode recognition-based Bayesian estimation for nonlinear stochastic systems with unknown sensor mode.

Wanying Zhang1, Yan Liang1, Feng Yang1

  • 1School of Automation, Northwestern Polytechnical University, Xi'an, China; Key Laboratory of Information Fusion Technology, Ministry of Education, Xi'an, China.

ISA Transactions
|May 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian framework for joint state estimation and sensor mode recognition in nonlinear systems. The method enhances accuracy in fault detection and target tracking by effectively handling unknown sensor modes.

Keywords:
Bayesian estimationMaximum correntropy criterionMode recognitionNonlinear stochastic systems

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Stochastic Systems

Background:

  • Accurate state estimation in nonlinear stochastic systems is crucial for reliable operation.
  • Unknown sensor modes introduce significant challenges in traditional estimation frameworks.
  • Existing methods often struggle with dynamic and uncertain sensor behavior.

Purpose of the Study:

  • To develop a robust framework for joint state estimation and sensor mode recognition in nonlinear stochastic systems.
  • To address the problem of unknown and potentially multi-valued sensor modes.
  • To improve the effectiveness of fault detection and target tracking under uncertain conditions.

Main Methods:

  • A set-valued mode recognition-based Bayesian estimation framework is proposed.
  • The maximum correntropy criterion is employed for sensor mode recognition.
  • A mode-separability metric is introduced to assess recognition reliability.
  • Two implementation schemes (separable and inseparable modes) are derived.

Main Results:

  • The proposed method demonstrates superior state estimation accuracy compared to existing approaches.
  • Effective recognition of unknown sensor modes is achieved.
  • Improved performance in simulated fault detection and target tracking scenarios is observed.

Conclusions:

  • The developed Bayesian framework offers a significant advancement in handling unknown sensor modes for nonlinear systems.
  • The maximum correntropy criterion and mode-separability metric provide effective tools for robust estimation and recognition.
  • The approach enhances reliability in critical applications like fault detection and target tracking.