Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

• URL: http://arxiv.org/abs/2003.06169v1
• Date: Fri, 13 Mar 2020 09:38:40 GMT
• Title: Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions
• Authors: Xinwei Wang, Guohua Wu, Lining Xing, Witold Pedrycz
• Abstract summary: Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs) The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs)
• Score: 69.47531199609593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this paper are thus to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multi-objective function and autonomous model. A detailed literature review from 1997 up to 2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic and machine learning. Finally, we discuss a number of topics worth pursuing in the future.

Related papers

• European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry [2.0880207832785436]
  The European Space Agency Benchmark for Anomaly Detection in Satellite Telemetry (ESA-ADB) aims to establish a new standard in the domain. The newly introduced ESA Anomalies dataset contains annotated real-life telemetry from three different ESA missions. Results of typical anomaly detection algorithms assessed in our novel hierarchical evaluation pipeline show that new approaches are necessary to address operators' needs.
  arXiv  Detail & Related papers  (2024-06-25T13:23:37Z)
• Multi-strip observation scheduling problem for ac-tive-imaging agile earth observation satellites [0.0]
  We investigate the multi-strip observation scheduling problem for an active-image agile earth observation satellite (MOSP) A bi-objective optimization model is presented along with an adaptive bi-objective memetic algorithm which integrates the combined power of an adaptive large neighborhood search algorithm (ALNS) and a nondominated sorting genetic algorithm II (NSGA-II) Our model is more versatile than existing models and provide enhanced capabilities in applied problem solving.
  arXiv  Detail & Related papers  (2022-07-04T08:35:57Z)
• Three multi-objective memtic algorithms for observation scheduling problem of active-imaging AEOS [0.0]
  We call the novel problem as observation scheduling problem for AEOS with variable image duration (OSWVID) A cumulative image quality and a detailed energy consumption is proposed to build OSWVID as a bi-objective optimization model. Three multi-objective memetic algorithms, PD+NSGA-II, LANSGA-II and ALNS+NSGA-II, are then designed to solve OSWVID.
  arXiv  Detail & Related papers  (2022-07-04T08:18:54Z)
• Innovations in the field of on-board scheduling technologies [64.41511459132334]
  This paper proposes an onboard scheduler, that integrates inside an onboard software framework for mission autonomy. The scheduler is based on linear integer programming and relies on the use of a branch-and-cut solver. The technology has been tested on an Earth Observation scenario, comparing its performance against the state-of-the-art scheduling technology.
  arXiv  Detail & Related papers  (2022-05-04T12:00:49Z)
• Learning to Act with Affordance-Aware Multimodal Neural SLAM [11.695095006311176]
  We propose a Neural SLAM approach that utilizes several modalities for exploration, predicts an affordance-aware semantic map, and plans over it at the same time. We obtain more than $40%$ improvement over prior published work on the ALFRED benchmark and set a new state-of-the-art generalization performance at a success rate of $23.48%$ on the test unseen scenes.
  arXiv  Detail & Related papers  (2022-01-24T18:39:10Z)
• The State of Aerial Surveillance: A Survey [62.198765910573556]
  This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective. The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed.
  arXiv  Detail & Related papers  (2022-01-09T20:13:27Z)
• Artificial Intelligence for Satellite Communication: A Review [91.3755431537592]
  This work provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. The application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing.
  arXiv  Detail & Related papers  (2021-01-25T13:01:16Z)
• The EOS Decision and Length Extrapolation [103.7271774593922]
  Extrapolation to unseen sequence lengths is a challenge for neural generative models of language. We study an oracle setting to compare the length-extrapolative behavior of networks trained to predict EOS (+EOS) with networks not trained to (-EOS) We find that -EOS substantially outperforms +EOS, for example extrapolating well to lengths 10 times longer than those seen at training time in a bracket closing task.
  arXiv  Detail & Related papers  (2020-10-14T15:46:17Z)
• Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks [51.05735925326235]
  A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
  arXiv  Detail & Related papers  (2020-05-26T05:39:27Z)
• Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty [1.100580615194563]
  Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty. An improved simulated annealing based combining a fast insertion strategy is proposed for large-scale observation missions.
  arXiv  Detail & Related papers  (2020-03-14T16:37:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.