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A Novel Variational Bayesian Method Based on Student's t Noise for Underwater Localization.

Haoqian Huang1, Yutong Zhang1, Chenhui Dong1

  • 1College of Artificial Intelligence and Automation, Hohai University, Changzhou 213200, China.

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Summary
This summary is machine-generated.

This study introduces a novel sliding window variational Kalman filter using Student's t-distribution (SWVKF-ST) to enhance underwater navigation accuracy. The SWVKF-ST method effectively handles measurement outliers and improves state estimation in challenging acoustic environments.

Keywords:
multi-sensor fusionmultiple fading factorssliding windowstudent’s t-distributionvariational Bayesian

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Area of Science:

  • Robotics and Control Systems
  • Signal Processing
  • Underwater Acoustics

Background:

  • Underwater navigation systems are susceptible to multipath effects from acoustic sensors, causing measurement outliers.
  • These outliers degrade the accuracy of integrated navigation state estimation.
  • Existing Kalman filter methods struggle with heavy-tailed noise distributions common in underwater acoustics.

Purpose of the Study:

  • To propose a robust state estimation method for underwater integrated navigation.
  • To improve accuracy and robustness against measurement outliers caused by multipath effects.
  • To mitigate the impact of unknown noise characteristics on navigation accuracy.

Main Methods:

  • Developed a sliding window variational Kalman filter based on Student's t-distribution (SWVKF-ST).
  • Utilized Student's t-distribution to model heavy-tailed noise and inverse Wishart distribution for noise covariance prior.
  • Employed a variational Bayesian framework for joint estimation of state variables and measurements within a sliding window.
  • Incorporated multiple fading factors to prevent degradation of estimation accuracy.

Main Results:

  • The SWVKF-ST demonstrated superior filtering precision and robustness to outliers compared to conventional filters in simulations and experiments.
  • The method effectively reduced uncertainty in the measurement noise covariance matrix.
  • Validated excellent adaptability of the SWVKF-ST in complex underwater environments.

Conclusions:

  • The SWVKF-ST is a highly effective method for improving state estimation accuracy in underwater integrated navigation.
  • The proposed filter offers enhanced robustness against outliers and unknown noise characteristics.
  • This approach significantly advances the reliability of acoustic sensing for underwater navigation.