mlsanityData to 14 Oct 2026

Learning Satellite Pattern-of-Life Identification: A Diffusion-based Approach

arXiv 2412.10814 ↗First posted 14 Dec 2024cs.LG cs.CEcited by 0

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Abstract (arXiv)

As Earth's orbital satellite population grows exponentially, effective space situational awareness becomes critical for collision prevention and sustainable operations. Current approaches to monitor satellite behaviors rely on expert knowledge and rule-based systems that scale poorly. Among essential monitoring tasks, satellite pattern-of-life (PoL) identification, analyzing behaviors like station-keeping maneuvers and drift operations, remains underdeveloped due to aerospace system complexity, operational variability, and inconsistent ephemerides sources. We propose a novel generative approach for satellite PoL identification that significantly eliminates the dependence on expert knowledge. The proposed approach leverages orbital elements and positional data to enable automatic pattern discovery directly from observations. Our implementation uses a diffusion model framework for end-to-end identification without manual refinement or domain expertise. The architecture combines a multivariate time-series encoder to capture hidden representations of satellite positional data with a conditional denoising process to generate accurate PoL classifications. Through experiments across diverse real-world satellite operational scenarios, our approach demonstrates superior identification quality and robustness across varying data quality characteristics. A case study using actual satellite data confirms the approach's transformative potential for operational behavior pattern identification, enhanced tracking, and space situational awareness.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 52 references that are in the map. First posted on arXiv 14 Dec 2024.

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24 of 52 references.

References · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2020Denoising Diffusion Implicit Modelscited by 9,781
  4. 2022Classifier-Free Diffusion Guidancecited by 5,847
  5. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  6. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
  7. 2021TS2Vec: Towards Universal Representation of Time Seriescited by 471
  8. 2022DiffuSeq: Sequence to Sequence Text Generation with Diffusion Modelscited by 450
  9. 2019MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentationcited by 373
  10. 2021ASFormer: Transformer for Action Segmentationcited by 168
  11. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 99
  12. 2019U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Stagingcited by 88
12 more
  1. 2022Temporal Action Segmentation: An Analysis of Modern Techniquescited by 85
  2. 2023DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Modelscited by 81
  3. 2022Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoisecited by 79
  4. 2023Diffusion Action Segmentationcited by 74
  5. 2023DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Modelcited by 65
  6. 2020Time Series Change Point Detection with Self-Supervised Contrastive Predictive Codingcited by 58
  7. 2021Unsupervised Action Segmentation by Joint Representation Learning and Online Clusteringcited by 49
  8. 2024ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Modelcited by 49
  9. 2024Temporally Consistent Unbalanced Optimal Transport for Unsupervised Action Segmentationcited by 38
  10. 2023Large Pre-trained time series models for cross-domain Time series analysis taskscited by 23
  11. 2022ClaSP -- Parameter-free Time Series Segmentationcited by 21
  12. 2023Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transportcited by 10

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