Satellite Onboard Observation Task Planning Based on Attention Neural Network
Satellite onboard autonomous task planning is one of the key technologies for the operation of earth observation satellites,which has received great attention from researchers in recent years.Considering the limited computing resources,as well as the dynamic changes of observation tasks and resource...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial office of Computer Science
2022-07-01
|
| Series: | Jisuanji kexue |
| Subjects: | |
| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-7-242.pdf |
| _version_ | 1797845171302301696 |
|---|---|
| author | PENG Shuang, WU Jiang-jiang, CHEN Hao, DU Chun, LI Jun |
| author_facet | PENG Shuang, WU Jiang-jiang, CHEN Hao, DU Chun, LI Jun |
| author_sort | PENG Shuang, WU Jiang-jiang, CHEN Hao, DU Chun, LI Jun |
| collection | DOAJ |
| description | Satellite onboard autonomous task planning is one of the key technologies for the operation of earth observation satellites,which has received great attention from researchers in recent years.Considering the limited computing resources,as well as the dynamic changes of observation tasks and resource onboard,the heuristic search algorithms are mainly used to solve the satellite onboard task planning problem,and the optimization of solution needs to be improved.Firstly,a new sequential decision-ma-king framework for observation tasks is constructed in this paper.Based on this framework,an earth observation satellite can decide the observation task to be performed in real-time,without generating a plan in advance.Then,an observation task decision model based on attention mechanism,and the corresponding input feature representation method and model training method are designed.An observation task sequence algorithm based on attention neural network is proposed.Finally,based on a set of random data,the performance of the proposed algorithm,two deep learning algorithms and two heuristic online search algorithms are compared.Experimental results show that the response time of the proposed method is less than one-fifth of the existing deep learning algorithm,and the profit gap is much smaller than that of the heuristic search algorithms,which confirm the feasibility and effectiveness of our method. |
| first_indexed | 2024-04-09T17:35:34Z |
| format | Article |
| id | doaj.art-fcee315224bb467ab8e5686cf279f09c |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-04-09T17:35:34Z |
| publishDate | 2022-07-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-fcee315224bb467ab8e5686cf279f09c2023-04-18T02:32:12ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2022-07-0149724224710.11896/jsjkx.210500093Satellite Onboard Observation Task Planning Based on Attention Neural NetworkPENG Shuang, WU Jiang-jiang, CHEN Hao, DU Chun, LI Jun0College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,ChinaSatellite onboard autonomous task planning is one of the key technologies for the operation of earth observation satellites,which has received great attention from researchers in recent years.Considering the limited computing resources,as well as the dynamic changes of observation tasks and resource onboard,the heuristic search algorithms are mainly used to solve the satellite onboard task planning problem,and the optimization of solution needs to be improved.Firstly,a new sequential decision-ma-king framework for observation tasks is constructed in this paper.Based on this framework,an earth observation satellite can decide the observation task to be performed in real-time,without generating a plan in advance.Then,an observation task decision model based on attention mechanism,and the corresponding input feature representation method and model training method are designed.An observation task sequence algorithm based on attention neural network is proposed.Finally,based on a set of random data,the performance of the proposed algorithm,two deep learning algorithms and two heuristic online search algorithms are compared.Experimental results show that the response time of the proposed method is less than one-fifth of the existing deep learning algorithm,and the profit gap is much smaller than that of the heuristic search algorithms,which confirm the feasibility and effectiveness of our method.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-7-242.pdfearth observation satellite|satellite onboard autonomous task planning|sequential decision-making|recurrent neural network|attention mechanism |
| spellingShingle | PENG Shuang, WU Jiang-jiang, CHEN Hao, DU Chun, LI Jun Satellite Onboard Observation Task Planning Based on Attention Neural Network Jisuanji kexue earth observation satellite|satellite onboard autonomous task planning|sequential decision-making|recurrent neural network|attention mechanism |
| title | Satellite Onboard Observation Task Planning Based on Attention Neural Network |
| title_full | Satellite Onboard Observation Task Planning Based on Attention Neural Network |
| title_fullStr | Satellite Onboard Observation Task Planning Based on Attention Neural Network |
| title_full_unstemmed | Satellite Onboard Observation Task Planning Based on Attention Neural Network |
| title_short | Satellite Onboard Observation Task Planning Based on Attention Neural Network |
| title_sort | satellite onboard observation task planning based on attention neural network |
| topic | earth observation satellite|satellite onboard autonomous task planning|sequential decision-making|recurrent neural network|attention mechanism |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-7-242.pdf |
| work_keys_str_mv | AT pengshuangwujiangjiangchenhaoduchunlijun satelliteonboardobservationtaskplanningbasedonattentionneuralnetwork |