Online Inertia Tensor Identification for Non-Cooperative Spacecraft via Augmented UKF

2026-03-28

AI Summary

This work addresses the divergence of relative navigation in on-orbit servicing of non-cooperative spacecraft caused by unknown target inertia tensors. To overcome this challenge, an augmented unscented Kalman filter framework is proposed that, for the first time, incorporates all six independent elements of the inertia tensor into the state vector, enabling joint estimation of the target’s full six-degree-of-freedom pose and complete inertia tensor. The approach fuses monocular vision features from a CNN with LiDAR depth measurements, enforces rigid-body dynamics constraints, and employs adaptive process noise to ensure physical consistency and numerical stability. Monte Carlo simulations demonstrate that the method simultaneously achieves convergence of both kinematic states and inertial parameters, significantly improving long-term trajectory prediction accuracy and guidance robustness in deep-space non-cooperative scenarios.

Abstract

Autonomous proximity operations, such as active debris removal and on-orbit servicing, require high-fidelity relative navigation solutions that remain robust in the presence of parametric uncertainty. Standard estimation frameworks typically assume that the target spacecraft's mass properties are known a priori; however, for non-cooperative or tumbling targets, these parameters are often unknown or uncertain, leading to rapid divergence in model-based propagators. This paper presents an augmented Unscented Kalman Filter (UKF) framework designed to jointly estimate the relative 6-DOF pose and the full inertia tensor of a non-cooperative target spacecraft. The proposed architecture fuses visual measurements from monocular vision-based Convolutional Neural Networks (CNN) with depth information from LiDAR to constrain the coupled rigid-body dynamics. By augmenting the state vector to include the six independent elements of the inertia tensor, the filter dynamically recovers the target's normalized mass distribution in real-time without requiring ground-based pre-calibration. To ensure numerical stability and physical consistency during the estimation of constant parameters, the filter employs an adaptive process noise formulation that prevents covariance collapse while allowing for the gradual convergence of the inertial parameters. Numerical validation is performed via Monte Carlo simulations, demonstrating that the proposed Augmented UKF enables the simultaneous convergence of kinematic states and inertial parameters, thereby facilitating accurate long-term trajectory prediction and robust guidance in non-cooperative deep-space environments.

Problem

• inertia tensor identification
• non-cooperative spacecraft
• relative navigation
• parametric uncertainty
• 6-DOF pose estimation

Innovation

• Augmented UKF
• inertia tensor identification
• non-cooperative spacecraft
• 6-DOF pose estimation
• adaptive process noise