Adaptive range gating based on variational Bayesian inference for space debris ranging with spaceborne single-photon...

Area of Science:

• Space exploration
• Optical sensing technologies
• Astrodynamics

Background:

• Space debris localization is critical for mission safety.
• Spaceborne single-photon LiDAR (SSPL) offers precise ranging capabilities.
• Extended Kalman Filtering (EKF) is used for range gating but struggles with noise.

Purpose of the Study:

• To improve the accuracy of space debris localization using SSPL.
• To address the limitations of traditional EKF in handling noise.
• To develop an adaptive range gating method for enhanced SSPL performance.

Main Methods:

• Proposed variational Bayesian adaptive extended Kalman filtering (ARG-VBAEKF).
• Utilized variational Bayesian (VB) posterior approximation for joint state and noise distribution estimation.
• Simulated performance under dynamic space debris conditions.

Main Results:

• ARG-VBAEKF accurately estimates both state and noise statistics.
• The proposed method effectively enhances range gating performance in SSPL.
• Improved accuracy in space debris ranging was demonstrated through simulations.

Conclusions:

• The ARG-VBAEKF method significantly improves SSPL-based space debris ranging accuracy.
• Adaptive noise estimation is key to overcoming EKF limitations in dynamic scenarios.
• This technique enhances the reliability of space debris detection and tracking.