Multi-Sensor Tasking for Ground-Based Space Situational Awareness via Job-Shop Scheduling Problem

Abstract Sensor-tasking to collect observation measurements of resident space objects constitutes a central problem for Space Situational Awareness (SSA). One specific type of sensor-tasking problem aims at rapidly producing an optimal or near-optimal schedule spanning over an observation horizon of a few hours up to a few nights. We derive tractable binary linear program formulations for solving the scheduling problem for both single and multiple telescopes. Observations are modeled by assuming the telescopes must track orbit passes for a user-defined exposure time. The proposed programs are efficiently solvable by branch-and-bound algorithms for instance sizes of practical applicability.

Authors

Institutions

Publication Details

Journal
The Journal of the Astronautical Sciences
Published
2025-11-20
DOI
https://doi.org/10.1007/s40295-025-00532-4
Primary Topic
Space Satellite Systems and Control
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-Sensor Tasking for Ground-Based Space Situational Awareness via Job-Shop Scheduling Problem

Koki Ho, Yuri Shimane
The Journal of the Astronautical Sciences
Space Satellite Systems and Control
article

Multi-Sensor Tasking for Ground-Based Space Situational Awareness via Job-Shop Scheduling Problem

Koki Ho, Yuri Shimane
article en

Abstract

Abstract Sensor-tasking to collect observation measurements of resident space objects constitutes a central problem for Space Situational Awareness (SSA). One specific type of sensor-tasking problem aims at rapidly producing an optimal or near-optimal schedule spanning over an observation horizon of a few hours up to a few nights. We derive tractable binary linear program formulations for solving the scheduling problem for both single and multiple telescopes. Observations are modeled by assuming the telescopes must track orbit passes for a user-defined exposure time. The proposed programs are efficiently solvable by branch-and-bound algorithms for instance sizes of practical applicability.

The Journal of the Astronautical SciencesVol. 72(6)
Georgia Institute of Technology (US)
Openalex Percentile: Top 54%
Space Satellite Systems and Control
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.