Satellite-Terrestrial Coordinated Multi-Satellite Beam Hopping Scheduling Based on Multi-Agent Deep Reinforcement Learning

Lin, Zhiyuan; Ni, Zuyao; Kuang, Linling; Jiang, Chunxiao; Huang, Zhen

doi:10.1109/twc.2024.3368689

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…Wang et al [23] proposed a multi-objective differential evolutionary algorithm based on spatial partitioning-user preference policy to solve the MSDMPUP problem by simultaneously considering the three objectives of optimizing the overall task benefit, antenna load balancing, and task completion timelines. Lin et al [24] propose a multi-satellite deep learning architecture for fusing user preferences, in which each satellite can train an efficient model based on deep learning algorithms, which significantly improves the timeliness of satellite mission decision-making. Song et al [25] propose a deep reinforcement learning-based solution for the large-scale MSDMPUP problem, which utilizes deep neural networks for each action to estimate to prioritize the task scheduling.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

Yang,

Hu,

Huang

et al. 2024

Electronics

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The existing multi-satellite dynamic mission planning system hardly satisfies the requirements of fast response time and high mission benefit in highly dynamic situations. In the meantime, a reasonable decision-maker preference mechanism is an additional challenge for multi-satellite imaging dynamic mission planning based on user preferences (MSDMPUP). Therefore, this study proposes the hybrid preference interaction mechanism and knowledge transfer strategy for the multi-objective evolutionary algorithm (HPIM–KTSMOEA). Firstly, an MSDMPUP model based on a task rolling window is constructed to achieve timely updating of the target task importance degree through the simultaneous application of periodic triggering and event triggering methods. Secondly, the hybrid preference interaction mechanism is constructed to plan according to the satellite controller’s preference-based commands in different phases of the optimal search of the mission planning scheme to effectively respond to the dynamic changes in the environment. Finally, a knowledge transfer strategy for the multi-objective evolutionary algorithm is proposed to accelerate population convergence in new environments based on knowledge transfer according to environmental variability. Simulation experiments verify the effectiveness and stability of the method in processing MSDMPUP. This study found that the HPIM–KTSMOEA algorithm has high task benefit, short response time, and high task completion when processing MSDMPUP.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

Yang,

Hu,

Huang

et al. 2024

Electronics

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Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond

Paheding,

Saleem,

Siddiqui

et al. 2024

Neural Comput & Applic

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In recent years, deep learning has significantly reshaped numerous fields and applications, fundamentally altering how we tackle a variety of challenges. Areas such as natural language processing (NLP), computer vision, healthcare, network security, wide-area surveillance, and precision agriculture have leveraged the merits of the deep learning era. Particularly, deep learning has significantly improved the analysis of remote sensing images, with a continuous increase in the number of researchers and contributions to the field. The high impact of deep learning development is complemented by rapid advancements and the availability of data from a variety of sensors, including high-resolution RGB, thermal, LiDAR, and multi-/hyperspectral cameras, as well as emerging sensing platforms such as satellites and aerial vehicles that can be captured by multi-temporal, multi-sensor, and sensing devices with a wider view. This study aims to present an extensive survey that encapsulates widely used deep learning strategies for tackling image classification challenges in remote sensing. It encompasses an exploration of remote sensing imaging platforms, sensor varieties, practical applications, and prospective developments in the field.

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Satellites Beam Hopping Scheduling for Interference Avoidance

Deng,

Ying,

Feng

et al. 2024

IEEE J. Select. Areas Commun.

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Satellite-Terrestrial Coordinated Multi-Satellite Beam Hopping Scheduling Based on Multi-Agent Deep Reinforcement Learning

Cited by 5 publications

References 28 publications

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond

Satellites Beam Hopping Scheduling for Interference Avoidance

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