Space Situational Awareness

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11 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

Scalable LEO Conjunction Screening using Adaptive Synthetic-Covariance Thresholds
Oct 8, 2026Vedant Srinivas, Grace Ra Kim, Duncan Eddy +1Space Situational Awareness
Stanford University 496 Lomita Mall Stanford, CA 94305
VISTA: An Attention-Based Multi-Agent Reinforcement Learning Architecture for Space Situational Awareness Sensor Tasking
Sep 20, 2026Miguel Leiva-Vélez, Adalberto Claudio Quiros, Nicolas Gaston Rozado +2Active Sensing Space Situational Awareness
ETSIAE-School of Aeronautics, Universidad Politécnica de Madrid, Spain · Indra Sistemas S.A., Spain · Department of Computer Systems Engineering, Universidad Politécnica de Madrid, Spain
Improving Faint Object Detection for Space Situational Awareness with Variational Autoencoders
Sep 10, 2026Angela Cratere, Luca Ghilardi, Vishnu Reddy +3Variational Autoencoders Small Object Detection
Department of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands · Systems and Industrial Engineering, University of Arizona, Tucson, USA · Space4 Center, University of Arizona, Tucson, USA +2
Sub-Pixel Affine Registration of Space Debris Images via the Radon Point Spread Function
Sep 7, 2026Shenshen Luan, Miaomiao Tian, Shuai Jiang +3Space Situational Awareness
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China · China Academy of Space Technology, Beijing, 100091, China
Detecting Satellites in Radio-Frequency Data via Semi-Supervised Learning
Jun 18, 2026Cade W. Trotter, Maksim E. Eren, Justin C. Holmes +4Remote Sensing Space Situational Awareness
Modeling and Observations of Earth Systems, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. · Computational Intelligence & Modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. · Applied Mathematics and Plasma Physics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. +2
FPGA-Accelerated Neuromorphic Vision System for Real-Time Orbital Object Detection
Jun 17, 2026Diego Hernández, Sebastián Valdivia, Vicente Westerhout +2Event-Based Object Detection FPGA Acceleration
Pontificia Universidad Cat´olica de Valpara´ıso, Valpara´ıso, Chile
AstroMind: A High-Fidelity Benchmark for Spacecraft Behavior Reasoning Based on Large Language Models
May 23, 2026Hao Liu, Siyuan Yang, Qinglei Hu +1LLM Evaluation Space Situational Awareness
Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China · KTH Royal Institute of Technology, Stockholm, Sweden · School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China +1
Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale
May 10, 2026Yong FuWeakly Supervised Learning Physics-Informed ML
Substratum Labs, Inc.
Unified Orbit-Attitude Estimation and Sensor Tasking Framework for Autonomous Cislunar Space Domain Awareness Using Multiplicative Unscented Kalman Filter
Mar 21, 2026Smriti Nandan Paul, Siwei FanSpace Situational Awareness Spacecraft Pose Estimation
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, 65409, MO, United States · Embry-Riddle Aeronautical University, 3700 Willow Creek Rd, Prescott, 86301, AZ, United States
Cognitively Layered Data Synthesis for Domain Adaptation of LLMs to Space Situational Awareness
Mar 10, 2026Ding Linghu, Cheng Wang, Da Fan +6Space Situational Awareness Domain Adaptation for LLMs
Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing, China · China Academy of Space Technology, Beijing, China · State Key Laboratory of Space Information System and Integrated Application, Beijing, China
Consensus Based Task Allocation for Angles-Only Local Catalog Maintenance of Satellite Systems
Feb 18, 2026Harrison Perone, Christopher W. HaysSpace Situational Awareness Multi-Robot Task Allocation
Undergraduate Mechanical Engineering Student, University of Connecticut, Storrs, CT 06269 · Research Aerospace Engineer, Air Force Research Laboratory Space Vehicles Directorate, Kirtland AFB, NM. 87117

Oct 8, 2026J/K move · Enter open · S save

Scalable LEO Conjunction Screening using Adaptive Synthetic-Covariance Thresholds

Vedant Srinivas, Grace Ra Kim, Duncan Eddy, Mykel J. Kochenderfer

Stanford University 496 Lomita Mall Stanford, CA 94305

Space Situational Awareness

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As orbital populations grow, conjunction screening must search more object pairs for potential collision risks. Existing pipelines use early-stage filters, such as an apsis filter, to eliminate pairs whose orbital altitude ranges cannot produce a close approach. However, these filters apply the same fixed threshold to every object, making the screening unnecessarily conservative for well-constrained objects while potentially insufficient for objects with greater orbit-prediction uncertainty. In this work, we present an uncertainty-aware conjunction screening method that replaces a single fixed apsis threshold with object-specific thresholds derived from propagated radial uncertainty. The resulting thresholds vary across both objects and prediction time, becoming narrower when propagated uncertainty is small and wider when uncertainty grows. We pair this adaptive screening rule with a binary-search altitude sweep that makes resulting heterogeneous intervals efficient to evaluate at catalog scale with log-linear search complexity. We evaluate the method on 33,791 objects from the Space-Track General Perturbations (GP) catalog using the preceding eight public ephemeris snapshots and a seven-day prediction horizon. The adaptive sieve retains 162.8 million candidate pairs, reducing the candidate set by 28.78% relative to a fixed 50 km apsis threshold and by 71.48% relative to brute-force all-pairs screening. It retains all close approaches identified in two independently propagated reference sets. At the largest tested catalog size, the binary-search sweep is 27 times faster than exhaustive interval evaluation while returning the same candidate set. These results demonstrate that uncertainty-aware screening can substantially reduce the candidate burden passed to downstream conjunction analysis, providing a scalable path toward managing increasingly large and heterogeneous orbital populations.