Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

Area of Science:

• * Signal Processing
• * Estimation Theory
• * Aerospace Communications

Background:

• * Unscented Kalman Filter (UKF) is effective for non-linear state estimation.
• * UKF performance degrades with non-Gaussian and impulsive noises.
• * Robustness against noise is critical for space communication networks.

Purpose of the Study:

• * Introduce a new algorithm, Maximum Correntropy Unscented Kalman Filter (MCUKF).
• * Enhance the robustness of state estimation in space communication networks.
• * Improve UKF performance under non-Gaussian and impulsive noise conditions.

Main Methods:

• * Applied Maximum Correntropy Criterion (MCC) to enhance UKF.
• * Utilized Unscented Transformation (UT) for predicted state and covariance.
• * Employed nonlinear regression with MCC for measurement reformulation.
• * Adopted UT to the measurement equation for final state estimation.

Main Results:

• * MCUKF demonstrated superior performance compared to standard UKF.
• * Enhanced robustness against heavy-tailed impulsive noises was observed.
• * The algorithm effectively handles non-Gaussian noise in relative state estimation.

Conclusions:

• * MCUKF offers a robust solution for state estimation in challenging noise environments.
• * The proposed method significantly improves the reliability of space communication networks.
• * MCC integration provides a powerful mechanism for noise mitigation in filtering.