期刊
IEEE TRANSACTIONS ON MOBILE COMPUTING
卷 21, 期 12, 页码 4381-4396出版社
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2021.3076088
关键词
Satellites; Resource management; Imaging; Task analysis; Earth; Space vehicles; Data collection; Agile earth observation satellites; time windows; observation scheduling; transmission scheduling
资金
- Natural Science Foundation of China [U19B2025, 61725103, 62001347]
- China Postdoctoral Science Foundation [2019TQ0241, 2020M673344]
- Natural Science Basic Research Program of Shaanxi Province [2021JQ126]
- National Natural Science Foundation of China [61901388]
This paper investigates joint observation and transmission scheduling in agile satellite networks (ASNs) to accommodate more imaging data. The problem is formulated as an integer linear programming (ILP) and solved efficiently using semidefinite relaxation (SDR).
Compared with traditional observation satellites, agile earth observation satellites are capable of prolonging observation time windows (OTWs) for targets, which significantly alleviates observation conflicts, thereby facilitating imaging data collection. However, it also leads to more uncertainties in determining the start time to image targets within these longer OTWs for an agile satellite network (ASN) to collect imaging data. Furthermore, these collected data are offloaded only within short transmission time windows between data collectors and data sinks, thus resulting in a transmission scheduling problem. Toward this end, this paper investigates joint observation and transmission scheduling in ASNs, aiming at accommodating more imaging data to be collected and offloaded successfully. Specifically, we formulate the studied problem as integer linear programming (ILP) to maximize the weighted sum of scheduled imaging tasks. Then, we explore the hidden structure of this ILP and transform it into a special framework, which can be solved efficiently through semidefinite relaxation (SDR). To reduce computation complexity, we further propose a fast yet efficient algorithm by combining the advantages of the devised SDR method and a genetic algorithm with special population initialization. Finally, simulation results demonstrate that the proposed algorithm can significantly increase the weighted sum of scheduled tasks.
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