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Enhanced GNSS Navigation Using a Centered Error Entropy Extended Kalman Filter in Non-Gaussian Noise Environments.

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  • 1Department of Electrical Engineering, National Taiwan Ocean University, Keelung 202301, Taiwan.

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Summary
This summary is machine-generated.

This study introduces the centered error entropy extended Kalman filter (CEE-EKF) to improve Global Navigation Satellite Systems (GNSS) accuracy. The CEE-EKF enhances Global Positioning System (GPS) navigation reliability in challenging environments with non-Gaussian noise and outliers.

Keywords:
GNSScentered error entropyextended Kalman filtermaximum correntropy criterionminimum error entropymultipath effectsnon-Gaussian noise

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Area of Science:

  • Geomatics Engineering
  • Signal Processing
  • Navigation Systems

Background:

  • Global Navigation Satellite Systems (GNSS), including Global Positioning System (GPS), face signal interference from multipath effects, challenging accurate state estimation.
  • Traditional extended Kalman filters (EKF) perform poorly in non-Gaussian noise environments due to their reliance on the minimum mean square error (MMSE) criterion.
  • Existing alternative criteria like minimum error entropy (MEE) and maximum correntropy criterion (MCC) have limitations in handling bias and achieving optimal convergence.

Purpose of the Study:

  • To develop an enhanced extended Kalman filter (EKF) that overcomes the limitations of existing criteria for improved GNSS state estimation.
  • To introduce the centered error entropy (CEE) extended Kalman filter (CEE-EKF) by integrating the strengths of MEE and MCC.
  • To validate the performance of the CEE-EKF in complex, nonlinear GPS environments with non-Gaussian noise and outliers.

Main Methods:

  • Development of the centered error entropy (CEE) criterion by combining minimum error entropy (MEE) and maximum correntropy criterion (MCC) principles.
  • Integration of the CEE criterion into the extended Kalman filter framework, resulting in the CEE-EKF.
  • Experimental validation using simulated complex nonlinear GPS data with non-Gaussian noise and outlier scenarios.

Main Results:

  • The CEE-EKF demonstrated significantly superior performance in noise suppression compared to individual MEE and MCC-based filters.
  • The proposed CEE-EKF exhibited enhanced robustness and accuracy, particularly in the presence of outliers in the GPS observables.
  • Experimental results confirmed the effectiveness of the CEE-EKF in complex nonlinear GPS environments with non-Gaussian noise.

Conclusions:

  • The CEE-EKF offers an effective solution for enhancing the reliability of GPS navigation in challenging real-world conditions.
  • The algorithm provides improved state estimation accuracy and robustness against non-Gaussian noise and outliers.
  • The CEE-EKF methodology is readily extendable to other Global Navigation Satellite Systems (GNSS) applications requiring robust state estimation.