Risk-aware sensor scheduling and tracking of large constellations

Ravago, Nicholas; Jones, Brandon A.

doi:10.1016/j.asr.2021.04.042

Cited by 7 publications

(1 citation statement)

References 24 publications

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“…The number of satellites may significantly exceed the current ability of SDA. Hence, in order to alleviate the burden from extreme increases of RSOs, we investigate the multisensor collaborative observation scheduling (MCOS) problem arising from the lack of sensor resources [4,5].…”

Section: Introductionmentioning

confidence: 99%

Mission Scheduling of Multi-Sensor Collaborative Observation for Space Surveillance Network

Long,

Cai,

Yang

et al. 2023

J. of Syst. Eng. Electron.

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With increased dependence on space assets, scheduling and tasking of the space surveillance network (SSN) are vitally important. The multi-sensor collaborative observation scheduling (MCOS) problem is a multi-constraint and high-conflict complex combinatorial optimization problem that is nondeterministic polynomial (NP)-hard. This research establishes a sub-time window constraint satisfaction problem (STWCSP) model with the objective of maximizing observation profit. Considering the significant effect of genetic algorithms (GA) on solving the problem of resource allocation, an evolution heuristic (EH) algorithm containing three strategies that focus on the MCOS problem is proposed. For each case, a task scheduling sequence is first obtained via an improved GA with penalty (GAPE) algorithm, and then a mission planning algorithm (heuristic rule) is used to determine the specific observation time. Compared to the model without sub-time windows and some other algorithms, a series of experiments illustrate the STWCSP model has better performance in terms of total profit. Experiments about strategy and parameter sensitivity validate its excellent performance in terms of EH algorithms.

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Section: Introductionmentioning

confidence: 99%