Balancing Coverage and Response Time in Area Target Scheduling for Satellite Networks

He, Lijun; Liang, Ben; Li, Jiandong; Sheng, Min

doi:10.1109/tvt.2020.2989388

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…Each target set contains fifteen regions, including the previous seven regions and eight newly generated targets. In line with [15], these eight targets are randomly generated as a square region with a width of 4 12. We can observe a steady increase in the objective function value as the iteration number grows with respect to all four algorithms.…”

Section: B Results and Discussionmentioning

confidence: 99%

“…Single EOS Exact [6], [7], Heuristic [8], [9] − Multiple EOSs MOEA [10], [11], Heuristic [12], [13], [14], [15] MOEA [16], [17], Heuristic [18], [19], [20], [21], [22], [23] Large A considerable amount of literature has been published on the point targets observation scheduling problem over the past decades. Existing algorithms to solve the Earth observation scheduling problem can be divided into three types: the exact algorithm [24], [25], [26], heuristic [27], [28], [29], [30], and multi-objective evolutionary algorithm (MOEA) [31].…”

Section: Single Region Multiple Regionsmentioning

confidence: 99%

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Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

Gu¹,

Chen²,

Chen³

et al. 2022

Preprint

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The last decades have witnessed a rapid increase of Earth observation satellites (EOSs), leading to the increasing complexity of EOSs scheduling. On account of the widespread applications of large region observation, this paper aims to address the EOSs observation scheduling problem for large region targets. A rapid coverage calculation method employing a projection reference plane and a polygon clipping technique is first developed. We then formulate a nonlinear integer programming model for the scheduling problem, where the objective function is calculated based on the developed coverage calculation method. A greedy initialization-based resampling particle swarm optimization (GI-RPSO) algorithm is proposed to solve the model. The adopted greedy initialization strategy and particle resampling method contribute to generating efficient and effective solutions during the evolution process. In the end, extensive experiments are conducted to illustrate the effectiveness and reliability of the proposed method. Compared to the traditional particle swarm optimization and the widely used greedy algorithm, the proposed GI-RPSO can improve the scheduling result by 5.42% and 15.86%, respectively.

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Section: B Results and Discussionmentioning

confidence: 99%

Section: Single Region Multiple Regionsmentioning

confidence: 99%

Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

Gu¹,

Chen²,

Chen³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Each target set contains 15 regions, including the previous seven regions and eight newly generated targets. In line with [15], these eight targets are randomly generated as a square region with a width of 4 • , located within the latitude range [60 • S, 60 • N] and the longitude range [180 • W, 180 • E]. For each target set, 10 runs are conducted and the average results in different iterations are recorded.…”

Section: B Results and Discussionmentioning

confidence: 99%

“…Perea et al [14] modeled the scheduling problem as a set covering problem and presented an adaptive search algorithm to solve it. He et al [15] constructed an integer programming model for the area target scheduling problem, which aims at maximizing the coverage ratio while minimizing the response time by considering the weighted sum. A balanced heuristic has been presented to solve the model and the constant-factor performance guarantee can be provided.…”

Section: A Related Workmentioning

confidence: 99%

Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

Han

Chen

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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The last decades have witnessed a rapid increase of Earth observation satellites (EOSs), leading to the increasing complexity of EOSs scheduling. On account of the widespread applications of large region observation, this article aims to address the EOSs observation scheduling problem for large region targets. A rapid coverage calculation method employing a projection reference plane and a polygon clipping technique is first developed. We then formulate a nonlinear integer programming model for the scheduling problem, where the objective function is calculated based on the developed coverage calculation method. A greedy initialization-based resampling particle swarm optimization (GI-RPSO) algorithm is proposed to solve the model. The adopted greedy initialization strategy and particle resampling method contribute to generating efficient and effective solutions during the Manuscript

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Illuminator scheduling method for passive radar sea surface target detection based on RCS characteristic

Peng

Fang

2022

Remote Sensing Letters

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Balancing Coverage and Response Time in Area Target Scheduling for Satellite Networks

Cited by 3 publications

References 9 publications

Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

Illuminator scheduling method for passive radar sea surface target detection based on RCS characteristic

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