GNSS-based Lunar Orbit and Clock Estimation With Stochastic Cloning UD Filter

• URL: http://arxiv.org/abs/2601.16393v3
• Date: Wed, 18 Feb 2026 22:41:22 GMT
• Title: GNSS-based Lunar Orbit and Clock Estimation With Stochastic Cloning UD Filter
• Abstract summary: This paper presents a terrestrial-based orbit and clock estimation framework for lunar satellites.<n>We develop a Lunar-cloning UD-factorized filter and delayed-state smoother that provide enhanced numerical stability when processing precise time-differenced carrier phase (TDCP) measurements.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a terrestrial GNSS-based orbit and clock estimation framework for lunar navigation satellites. To enable high-precision estimation under the low-observability conditions encountered at lunar distances, we develop a stochastic-cloning UD-factorized filter and delayed-state smoother that provide enhanced numerical stability when processing precise time-differenced carrier phase (TDCP) measurements. A comprehensive dynamics and measurement model is formulated, explicitly accounting for relativistic coupling between orbital and clock states, lunar time-scale transformations, and signal propagation delays including ionospheric, plasmaspheric, and Shapiro effects. The proposed approach is evaluated using high-fidelity Monte-Carlo simulations incorporating realistic multi-constellation GNSS geometry, broadcast ephemeris errors, lunar satellite dynamics, and ionospheric and plasmaspheric delay computed from empirical electron density models. Simulation results demonstrate that combining ionosphere-free pseudorange and TDCP measurements achieves meter-level orbit accuracy and sub-millimeter-per-second velocity accuracy, satisfying the stringent signal-in-space error requirements of future Lunar Augmented Navigation Services (LANS).

Related papers

• Learning Spatio-Temporal Foundation Models from Pure Synthetic Data [85.23980441738811]
  NeoST is a scalable synthetic-training corpus to real-world bias, a latent-space reasoning architecture that generates and iteratively refines multiple future trajectories, and latent-space objectives that emphasize structural dynamics and enable inference-time correction under distribution shifts.<n>Extensive experiments across diverse real-world benchmarks show that NeoST consistently outperforms existing STFMs in diverse real-worldtemporal systems.
  arXiv  Detail & Related papers  (2026-06-27T02:56:46Z)
• FAST-LIVGO: A Degeneracy-Robust LiDAR-Inertial-Visual-GNSS Fusion Odometry [14.641801914375163]
  State estimation and mapping in long-term, large-scale, and highly dynamic environments remains a key challenge in robotics.<n>Existing LiDAR-In-ertial-Visual Odometry (LIVO) systems achieve strong local accuracy but suffer from accumulated drift over long distances.<n>We propose a LiDAR-aided fusion framework based on an Error-State Iterated Kalman Filter to address these limitations.<n>Our system reduces accumulated drift and map ghosting, outperforming state-of-the-art methods in accuracy and robustness.
  arXiv  Detail & Related papers  (2026-06-17T15:33:11Z)
• Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection [29.577755639815027]
  We present a framework for detecting transient gamma-ray phenomena in a controlled environment by combining end-to-end simulations of the Fermi-LAT sky with self-supervised deep learning.<n>We generate a ten-year synthetic Universe with gtobssim and process the simulated events into daily all-sky maps of exposure counts.<n>We employ a ConvLSTM network that operates directly on sequences map sequences, preserving spatial locality while learning temporal dependencies.
  arXiv  Detail & Related papers  (2026-05-21T07:45:54Z)
• Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction [0.0]
  We introduce Velocityformer, an equivariant graph transformer architecture designed to match the specific symmetry of the observational data.<n>matching the model's inductive bias to the data's broken symmetry consistently improves performance across all model sizes and training volumes.<n> Velocityformer is highly data-efficient, training to high accuracy on as few as 4 low-fidelity simulations, and generalises zero-shot across input geometry, cosmological parameters, and galaxy sample.
  arXiv  Detail & Related papers  (2026-05-20T17:59:05Z)
• Multi-Fidelity Emulation of Atmospheric Correction Coefficients with Physics-Guided Kolmogorov-Arnold Networks [0.17188280334580194]
  Atmospheric correction is a critical preprocessing step in optical remote sensing.<n>This study presents a physics-aware multi-fidelity surrogate framework for emulating atmospheric correction coefficients.
  arXiv  Detail & Related papers  (2026-05-04T22:43:25Z)
• HarmoniDiff-RS: Training-Free Diffusion Harmonization for Satellite Image Composition [48.39960286051028]
  HarmoniDiff-RS is a training-free diffusion-based framework for harmonizing composite satellite images.<n>We show that our method effectively performs satellite image composition, showing strong potential for scalable remote-sensing synthesis and simulation tasks.
  arXiv  Detail & Related papers  (2026-04-21T12:18:15Z)
• Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems [0.0]
  This paper introduces the Physics-Informed State Space Model (PISSM) to bridge the gap between efficiency and physical accuracy for edge-deployed microcontrollers.<n>A Linear State Space Model replaces heavy attention mechanisms, efficiently modeling temporal dependencies for parallel processing.<n> PISSM achieves superior accuracy with fewer than 40,000 parameters, establishing an ultra-lightweight benchmark for real-time off-grid control.
  arXiv  Detail & Related papers  (2026-04-13T17:59:49Z)
• Low-Dimensional Adaptation of Rectified Flow: A New Perspective through the Lens of Diffusion and Stochastic Localization [59.04314685837778]
  Rectified flow (RF) has gained considerable popularity due to its generation efficiency and state-of-the-art performance.<n>In this paper, we investigate the degree to which RF automatically adapts to the intrinsic low dimensionality of the support of the target distribution to accelerate sampling.<n>We show that, using a carefully designed choice of the time-discretization scheme and with sufficiently accurate drift estimates, the RF sampler enjoys an complexity of order $O(k/varepsilon)$.
  arXiv  Detail & Related papers  (2026-01-21T22:09:27Z)
• OptFormer: Optical Flow-Guided Attention and Phase Space Reconstruction for SST Forecasting [4.206799880454911]
  We propose OptFormer, a novel optical encoder-decoder model that integrates phase-space reconstruction with a motion-aware attention mechanism guided by flow.<n>Experiments on NOAA SST datasets across multiple spatial scales demonstrate that OptFormer achieves superior performance under a 1:1 training-to-prediction setting.
  arXiv  Detail & Related papers  (2025-12-29T22:27:15Z)
• Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion Transformers [0.28112829609955153]
  Total Electron Content (TEC) is a key ionospheric parameter.<n>TEC is derived from observations, but its reliable forecasting is limited by the sparse nature of global measurements.<n>We present a machine learning framework for ionospheric TEC forecasting that leverages Temporal Fusion Transformers (TFT) to predict sparse ionosphere data.
  arXiv  Detail & Related papers  (2025-08-30T23:08:19Z)
• A Data-Driven Approach to Estimate LEO Orbit Capacity Models [0.0]
  The population of resident space objects, divided into Active, Derelict, and Debris, in can be accurately modeled to predict future satellite and debris propagation.<n>This proposed approach makes use of a data set coming from a computational expensive high-fidelity model, the MOCAT-MC, to provide a light, low-fidelity counterpart that provides accurate forecasting in a shorter time frame.
  arXiv  Detail & Related papers  (2025-07-25T15:16:54Z)
• Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution [48.34051432429767]
  We propose a conditional diffusion model pre-trained on ERA5 reanalysis data with satellite observations (GridSat) as conditions.<n>During the training process, we propose to fuse the information from GridSat satellite observations into ERA5 maps via the attention mechanism.<n>In the sampling, we employed optimizable convolutional kernels to simulate the upscale process.
  arXiv  Detail & Related papers  (2025-02-09T02:05:33Z)
• Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators [4.852378895360775]
  We evaluate the efficiency of three surrogate models in speeding up experimental research by simulating land surface processes. Our findings indicate that while all models on average demonstrate high accuracy over the forecast period, the LSTM network excels in continental long-range predictions when carefully tuned.
  arXiv  Detail & Related papers  (2024-07-23T13:26:05Z)
• Satellite Federated Edge Learning: Architecture Design and Convergence Analysis [47.057886812985984]
  This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low data rate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation.
  arXiv  Detail & Related papers  (2024-04-02T11:59:58Z)
• A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators [46.348283638884425]
  We propose a two-step unsupervised deep learning framework named as Latent Autoregressive Recurrent Model (CLARM) for learning dynamics of charged particles in accelerators. The CLARM can generate projections at various accelerator sampling modules by capturing and decoding the latent space representation. The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.
  arXiv  Detail & Related papers  (2024-03-19T22:05:17Z)
• Modeling the space-time correlation of pulsed twin beams [68.8204255655161]
  Entangled twin-beams generated by parametric down-conversion are among the favorite sources for imaging-oriented applications. We propose a semi-analytic model which aims to bridge the gap between time-consuming numerical simulations and the unrealistic plane-wave pump theory.
  arXiv  Detail & Related papers  (2023-01-18T11:29:49Z)
• Time-to-Green predictions for fully-actuated signal control systems with supervised learning [56.66331540599836]
  This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
  arXiv  Detail & Related papers  (2022-08-24T07:50:43Z)

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.