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

• URL: http://arxiv.org/abs/2603.27361v1
• Date: Sat, 28 Mar 2026 18:13:03 GMT
• Title: Online Inertia Tensor Identification for Non-Cooperative Spacecraft via Augmented UKF
• Abstract summary: 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.<n>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.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• CARO: Contact-Agnostic Residual Observation for Zero-Shot Robust Quadruped Locomotion [6.612698611142421]
  CARO is a contact-agnostic residual observation framework for policy adaptation.<n>It embeds a fixed-base Euler--Lagrange model into a reinforcement learning control loop and constructs a torque-level residual observation.<n>A disturbance observer extracts a structured signal representing dynamics mismatch, while the policy learns to exploit this feedback for online adaptation.
  arXiv  Detail & Related papers  (2026-08-25T08:23:24Z)
• Attitude Estimation Using Inertial and Barometric Measurements [0.0]
  This paper introduces a barometer-aided attitude estimation architecture that exploits barometric altitude measurements to provide complementary information on the vehicle's vertical motion.<n>Results demonstrate that barometer-aided estimation provides a lightweight, reliable, and effective complementary sensing modality for attitude estimation in minimal-sensing configurations.
  arXiv  Detail & Related papers  (2026-07-14T20:36:13Z)
• PedestrianDiffusion: Multimodal Generative Denoising and Dense State Estimation for Inertial Navigation [1.8620637029128544]
  PedestrianDiffusion is a generative framework that reformulates dense 6D state estimation as a continuous conditional denoising process.<n>It achieves state-of-the-art performance, exhibiting unprecedented robustness to impulse perturbations and coupled 6D kinematic drift.<n>This work provides a rigorous algorithmic blueprint for next-generation Neural Inertial Measurement Units (N-IMUs)
  arXiv  Detail & Related papers  (2026-07-03T14:04:30Z)
• Weighted Flow Matching and Physics-Informed Nonlinear Filtering for Parameter Estimation in Digital Twins [2.5967371714399787]
  Digital twins (DTs) rely on continuous synchronization between physical systems and their virtual counterparts.<n>We develop a new mathematical framework that integrates Weighted Flow Matching (WFM) generative modeling with physics-informed nonlinear filtering.
  arXiv  Detail & Related papers  (2026-05-16T20:31:29Z)
• Unveiling the Mechanism of Continuous Representation Full-Waveform Inversion: A Wave Based Neural Tangent Kernel Framework [52.632217600064344]
  Full-waveform inversion (FWI) estimates physical parameters in the wave equation from limited measurements.<n>FWI methods are limited by their notorious sensitivity to the accuracy of the initial models.<n>Recent progress in continuous representation FWI (CR-FWI) demonstrates that representing parameter models with a coordinate-based neural network, such as implicit neural representation (INR), can mitigate the dependence on initial models.
  arXiv  Detail & Related papers  (2026-03-23T03:22:33Z)
• Intelligent Control of Differential Drive Robots Subject to Unmodeled Dynamics with EKF-based State Estimation [28.37226750522601]
  This work introduces a unified control and state estimation framework that combines a Lyapunov-based nonlinear controller and Adaptive Networks (ANN)<n>The proposed controller leverages the universal approximation property of neural networks to model unknown nonlinearities in real time.<n>To ensure robust state estimation, the EKF fuses inertial measurement unit (IMU) and odometry from monocular, 2D-LiDAR and wheel encoders.
  arXiv  Detail & Related papers  (2026-03-16T07:44:28Z)
• Consistent and Efficient MSCKF-based LiDAR-Inertial Odometry with Inferred Cluster-to-Plane Constraints for UAVs [46.21996481591401]
  This paper proposes a consistent and efficient LiDAR-Inertial Odometry framework tailored for UAVs.<n>By applying null-space projection to sliding-window coplanar constraints, we eliminate the direct dependency on feature parameters in the state vector.<n>It exhibits improved robustness in degenerate scenarios, achieves the lowest memory usage via its map-free nature, and runs in real-time on resource-constrained embedded platforms.
  arXiv  Detail & Related papers  (2026-03-13T11:09:50Z)
• JAWS: Enhancing Long-term Rollout of Neural Operators via Spatially-Adaptive Jacobian Regularization [0.0]
  Jacobian-Adaptive Weighting for Stability (JAWS) is a probabilistic regularization strategy designed to mitigate these limitations.<n>By framing operator learning as Maximum A Posteriori (MAP) estimation with spatially heteroscedastic uncertainty, JAWS dynamically modulates the regularization strength based on local physical complexity.<n> Experiments demonstrate that this spatially-adaptive prior serves as an effective spectral pre-conditioner, which reduces the base operator's burden of handling high-frequency instabilities.
  arXiv  Detail & Related papers  (2026-03-04T06:15:09Z)
• Conditional Flow Matching for Continuous Anomaly Detection in Autonomous Driving on a Manifold-Aware Spectral Space [0.0]
  We present Deep-Flow, an unsupervised framework for safety-critical anomaly detection.<n>Unlike standard generative approaches that operate in unstable, high-dimensional coordinate spaces, Deep-Flow constrains the generative process to a low-rank spectral manifold.<n>This ensures kinematic smoothness by design and enables the computation of numerically stable, deterministic log-likelihood estimation.
  arXiv  Detail & Related papers  (2026-02-19T18:10:16Z)
• System Identification under Constraints and Disturbance: A Bayesian Estimation Approach [5.58001571731715]
  We introduce a Bayesian system identification (SysID) framework for jointly estimating robot's state trajectories and physical parameters with high accuracy.<n>It embeds physically consistent inverse dynamics, contact and loop-closure constraints, and fully featured joint friction models as hard, stage-wise equality constraints.
  arXiv  Detail & Related papers  (2026-02-18T10:45:30Z)
• KoopGen: Koopman Generator Networks for Representing and Predicting Dynamical Systems with Continuous Spectra [65.11254608352982]
  We introduce a generator-based neural Koopman framework that models dynamics through a structured, state-dependent representation of Koopman generators.<n>By exploiting the intrinsic Cartesian decomposition into skew-adjoint and self-adjoint components, KoopGen separates conservative transport from irreversible dissipation.
  arXiv  Detail & Related papers  (2026-02-15T06:32:23Z)
• SKANet: A Cognitive Dual-Stream Framework with Adaptive Modality Fusion for Robust Compound GNSS Interference Classification [47.20483076887704]
  Global Navigation Satellite Systems (GNSS) face growing threats from sophisticated jamming interference.<n>We propose a cognitive deep learning framework built upon a dual-stream architecture that integrates Time-Frequency Images (TFIs) and Power Spectral Density (PSD)<n>We show that SKANet achieves an overall accuracy of 96.99%, exhibiting superior robustness for compound jamming classification.
  arXiv  Detail & Related papers  (2026-01-19T07:42:45Z)
• Dual-quaternion learning control for autonomous vehicle trajectory tracking with safety guarantees [39.146761527401424]
  We propose a learning-based trajectory tracking controller for autonomous robotic platforms whose motion can be described kinematically.<n>The controller is formulated in the dual quaternion framework and operates at the velocity level, assuming direct command of angular and linear velocities.
  arXiv  Detail & Related papers  (2026-01-06T15:30:02Z)
• AERMANI-Diffusion: Regime-Conditioned Diffusion for Dynamics Learning in Aerial Manipulators [2.376629619475859]
  Aerial manipulators undergo rapid, configuration-dependent changes in inertial coupling forces and aerodynamic forces.<n>We propose a regime-conditioned diffusion framework that models the full distribution of residual forces using a conditional diffusion process and a lightweight temporal encoder.
  arXiv  Detail & Related papers  (2025-12-11T16:10:32Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems [49.819436680336786]
  We propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems.<n>Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive implicit process prior that captures complex, non-stationary transition dynamics.<n>Our ETGPSSM outperforms existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2025-03-24T03:19:45Z)
• Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal [46.348283638884425]
  This paper introduces a reverse Latent Evolution Model (rLEM) designed for temporal phase of forward beam dynamics. In this two-step self-supervised deep learning framework, we utilize a Conditional Autoencoder (CVAE) to project 6D space projections of a charged particle beam into a lower-dimensional latent distribution. We then autoregressively learn the inverse temporal dynamics in the latent space using a Long Short-Term Memory (LSTM) network.
  arXiv  Detail & Related papers  (2024-08-14T23:09:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.