The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Approach for earth observation satellite real-time and playback data transmission scheduling
Open accessAbstract
The scheduling of earth observation satellites (EOSs) data transmission is a complex combinatorial optimization problem. Current researches mainly deal with this problem on the assumption that the data transmission mode is fixed, either playback or real-time transmission. Considering the characteristic of the problem, a multi-satellite real-time and playback data transmission scheduling model is established and a novel algorithm based on quantum discrete particle swarm optimization (QDPSO) is proposed. Furthermore, we design the longest compatible transmission chain mutation operator to enhance the performance of the algorithm. Finally, some experiments are implemented to validate correctness and practicability of the proposed algorithm.
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SAA4GS, QPSO4DT, and HGANS are nature-inspired metaheuristic algorithms that perform well on their own problems.
“SAA4GS, QPSO4DT, and HGANS are all based on nature-inspired metaheuristic algorithms and perform well in relation to their own problems.”
From A Satellite Observation Data Transmission Scheduling Algorithm Oriented to Data Topics (2020) · cites this paper as [2]
Section 3 describes the paper’s satellite-scheduling method as based on particle swarm optimization and a genetic mutation operator; Section 4 reports good results for that method. Our copy does not identify it as QPSO4DT or discuss SAA4GS or HGANS, so it does not support the claim about all three named algorithms. (LLM, conf=0.82)
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Cites
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Flagged first, then by citations
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Cited by
Sorted by citations
Autonomous Task Planning Method for Multi-Satellite System Based on a Hybrid Genetic Algorithm
Long, J., Wu, S., Han, X., Wang, Y., & Liu, L. (2023) · Aerospace · doi:10.3390/aerospace10010070 · Cited by 39
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A Satellite Observation Data Transmission Scheduling Algorithm Oriented to Data Topics
Chen, H., Zhai, B., Wu, J., Du, C., & Li, J. (2020) · International Journal of Aerospace Engineering · doi:10.1155/2020/2180674 · Cited by 12
“[2, 24, 25] employed improved genetic algorithm and particle swarm optimization algorithm to handle the satellite data transmission scheduling problem with some specific requirements, for incremental observation tasks, member satellites of the same cluster, and real-time and playback data transmission modes.”
MethodInfluentialSelf-citation
Metadata
- Authors
- Chen, H., Li, L., Zhong, Z., & Li, J.
- Affiliations
- National University of Defense Technology (CN)
- Venue
- Journal of Systems Engineering and Electronics
- Published
- 2015-10
- Volume
- 26
- Issue
- 5
- Pages
- 982-992
- ISSN
- 1004-4132 (ISSN-L)
- OpenAlex
- W2336204612
- Cited by
- 18
- References
- 24
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 4 · 2022: 1 · 2021: 2 · 2020: 3 · 2019: 1 · 2017: 1 · 2016: 1
- FWCI
- 0.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Systems Engineering and Electronics (publishedVersion)
- Funders
- National Natural Science Foundation of China (61101184, 61174159)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Satellite Communication Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Scheduling and Optimization Algorithms
- Keywords
- Earth observation satellites, real-time data transmission, multi-satellite scheduling, combinatorial optimization
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