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Generalized M-Estimation-Based Framework for Robust Guidance Information Extraction.

Jiawei Ren1, Xiaoyu Zhang1, Shoupeng Li2

  • 1College of Artificial Intelligence, Nankai University, Tianjin 300350, China.

Entropy (Basel, Switzerland)
|December 24, 2025
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Summary
This summary is machine-generated.

This study introduces a robust framework to improve state estimation in guidance systems facing non-Gaussian noise. The new method enhances accuracy and reliability, even with unstable noise characteristics.

Keywords:
generalized M-estimationguidance information estimationnon-Gaussian noise maximum correlation entropy

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Robotics

Background:

  • State estimation in guidance systems is challenged by non-Gaussian noise.
  • Existing methods struggle with unstable noise, leading to accuracy loss and filter divergence.
  • Optimal kernel width selection is difficult with statistically undefined noise.

Purpose of the Study:

  • To develop a robust framework for state estimation under non-Gaussian noise.
  • To enhance the accuracy and reliability of guidance information extraction.
  • To address limitations in kernel width selection and filter divergence.

Main Methods:

  • Linearizing nonlinear models using statistical linear regression.
  • Integrating generalized M-estimation with the Information-theoretic Maximum Correntropy Criterion Filter (IMCCF).
  • Employing Singular Value Decomposition (SVD) for numerical stability and the DCS kernel function for severe non-Gaussian noise.

Main Results:

  • The proposed framework demonstrates precision in Gaussian noise.
  • High accuracy is maintained under significant non-Gaussian noise, proving robustness.
  • Improvements in numerical stability and adaptive noise suppression enhance system reliability.

Conclusions:

  • The developed algorithm effectively handles non-Gaussian noise in guidance systems.
  • It offers enhanced robustness, accuracy, and reliability across diverse interference scenarios.
  • This work benefits guidance system designers and filtering researchers focused on robust estimation.