Space Situational Awareness in Very Low Earth Orbit for Re-Entry Object Monitoring

As the number of objects in orbit increases every year, the number of objects re-entering Earth’s atmosphere grows as well. Re-entry path prediction is tricky, as atmospheric modeling lacks accuracy and requires constant monitoring of the object during re-entry. Ground-based sensors face limitations due to the field of view and weather. This paper explores the novel idea of using star trackers in very low Earth orbit to image Resident Space Objects (RSOs) that are on re-entry path and provides a comparison of different star trackers to determine the most effective parameters. A simulation with 1000 Resident Space Objects on re-entry path was performed and detectability analysis was run using AURICAM, SAGITTA, PCO, IDS, and FAI sensors placed in orbit between 200 and 600 km in altitude. The results show that all star trackers at any altitude were capable of detecting at least three RSOs on re-entry path and making at least 47 detections during the simulation period. In particular, instruments with larger aperture diameters such as SAGITTA and FAI and quantum efficiency performed better, making up to 134 detections and detecting up to 10 unique RSOs. They also detected a higher average signal-to-noise ratio. Detectability of RSOs is higher when the sensor is placed closer to the objects, with the most effective performance recorded at 300–400 km altitude. Future work should include practical testing of this technique.

Authors

Institutions

Publication Details

Journal
Aerospace
Published
2026-06-30
DOI
https://doi.org/10.3390/aerospace13070607
Primary Topic
Space Satellite Systems and Control
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Space Situational Awareness in Very Low Earth Orbit for Re-Entry Object Monitoring

Regina S. K. Lee, Ángel Porras-Hermoso, Vithurshan Suthakar, Marianna Veltri et al.
Aerospace
Space Satellite Systems and Control
article

Space Situational Awareness in Very Low Earth Orbit for Re-Entry Object Monitoring

Regina S. K. Lee, Ángel Porras-Hermoso, Vithurshan Suthakar, Marianna Veltri, Ruth Huang
article en

Abstract

As the number of objects in orbit increases every year, the number of objects re-entering Earth’s atmosphere grows as well. Re-entry path prediction is tricky, as atmospheric modeling lacks accuracy and requires constant monitoring of the object during re-entry. Ground-based sensors face limitations due to the field of view and weather. This paper explores the novel idea of using star trackers in very low Earth orbit to image Resident Space Objects (RSOs) that are on re-entry path and provides a comparison of different star trackers to determine the most effective parameters. A simulation with 1000 Resident Space Objects on re-entry path was performed and detectability analysis was run using AURICAM, SAGITTA, PCO, IDS, and FAI sensors placed in orbit between 200 and 600 km in altitude. The results show that all star trackers at any altitude were capable of detecting at least three RSOs on re-entry path and making at least 47 detections during the simulation period. In particular, instruments with larger aperture diameters such as SAGITTA and FAI and quantum efficiency performed better, making up to 134 detections and detecting up to 10 unique RSOs. They also detected a higher average signal-to-noise ratio. Detectability of RSOs is higher when the sensor is placed closer to the objects, with the most effective performance recorded at 300–400 km altitude. Future work should include practical testing of this technique.

AerospaceVol. 13(7)
York University (CA), Universidad Politécnica de Madrid (ES)
Canadian Space Agency, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 4%
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.