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

TL;DR

This paper introduces an augmented UKF that jointly estimates the relative pose and inertia tensor of a non-cooperative spacecraft using visual and LiDAR data, enabling robust navigation without prior mass property knowledge.

Contribution

The novel augmented UKF framework estimates both pose and inertia tensor in real-time, integrating CNN and LiDAR data without pre-calibration, improving non-cooperative spacecraft navigation.

Findings

The augmented UKF successfully converges on inertial parameters in Monte Carlo simulations.

The approach improves long-term trajectory prediction accuracy for non-cooperative targets.

Numerical results demonstrate robustness against parametric uncertainties.

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.