Finding an Optimal Geometric Configuration for TDOA Location Systems With Reinforcement Learning
In TDOA passive location tasks, the geometric configuration can greatly affect the positioning precision due to the complicated characteristics of electromagnetic environment. How to find an appropriate path to a good geometry to locate the transmitter accurately is vital in practical location tasks...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9415644/ |
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| author | Shengxiang Li Guangyi Liu Siyuan Ding Haisi Li Ou Li |
| author_facet | Shengxiang Li Guangyi Liu Siyuan Ding Haisi Li Ou Li |
| author_sort | Shengxiang Li |
| collection | DOAJ |
| description | In TDOA passive location tasks, the geometric configuration can greatly affect the positioning precision due to the complicated characteristics of electromagnetic environment. How to find an appropriate path to a good geometry to locate the transmitter accurately is vital in practical location tasks. This paper proposes a novel geometry optimization method based on deep reinforcement learning. In the proposed method, stations are regarded as mobile agents that can receive wireless signals decide where to go. All agents are controlled by an actor-critic learner, which is trained on the experiences collected from executing the TDOA location task repeatedly. To evaluate the trained agents, a TDOA location simulator environment with complex electromagnetic characteristics is developed. The empirical results show that, the learner mastered useful strategies and navigated to optimal geometric configurations efficiently. A visual depiction of highlights of the learner’s behavior in TDOA passive location tasks can be viewed in the video provided in the supplementary material. |
| format | Article |
| id | doaj-art-955e9780d65a44fc91e4bd83a290a770 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-955e9780d65a44fc91e4bd83a290a7702025-08-20T02:52:19ZengIEEEIEEE Access2169-35362021-01-019633886339710.1109/ACCESS.2021.30754759415644Finding an Optimal Geometric Configuration for TDOA Location Systems With Reinforcement LearningShengxiang Li0Guangyi Liu1Siyuan Ding2Haisi Li3Ou Li4PLA Strategy Support Force Information Engineering University, Zhengzhou, ChinaPLA Strategy Support Force Information Engineering University, Zhengzhou, ChinaKey Laboratory of Experimental Physics and Computational Mathematics, Beijing, ChinaPLA Strategy Support Force Information Engineering University, Zhengzhou, ChinaPLA Strategy Support Force Information Engineering University, Zhengzhou, ChinaIn TDOA passive location tasks, the geometric configuration can greatly affect the positioning precision due to the complicated characteristics of electromagnetic environment. How to find an appropriate path to a good geometry to locate the transmitter accurately is vital in practical location tasks. This paper proposes a novel geometry optimization method based on deep reinforcement learning. In the proposed method, stations are regarded as mobile agents that can receive wireless signals decide where to go. All agents are controlled by an actor-critic learner, which is trained on the experiences collected from executing the TDOA location task repeatedly. To evaluate the trained agents, a TDOA location simulator environment with complex electromagnetic characteristics is developed. The empirical results show that, the learner mastered useful strategies and navigated to optimal geometric configurations efficiently. A visual depiction of highlights of the learner’s behavior in TDOA passive location tasks can be viewed in the video provided in the supplementary material.https://ieeexplore.ieee.org/document/9415644/Geometry optimaztionpassive locationTDOAreinforcement learningactor-critic |
| spellingShingle | Shengxiang Li Guangyi Liu Siyuan Ding Haisi Li Ou Li Finding an Optimal Geometric Configuration for TDOA Location Systems With Reinforcement Learning IEEE Access Geometry optimaztion passive location TDOA reinforcement learning actor-critic |
| title | Finding an Optimal Geometric Configuration for TDOA Location Systems With Reinforcement Learning |
| title_full | Finding an Optimal Geometric Configuration for TDOA Location Systems With Reinforcement Learning |
| title_fullStr | Finding an Optimal Geometric Configuration for TDOA Location Systems With Reinforcement Learning |
| title_full_unstemmed | Finding an Optimal Geometric Configuration for TDOA Location Systems With Reinforcement Learning |
| title_short | Finding an Optimal Geometric Configuration for TDOA Location Systems With Reinforcement Learning |
| title_sort | finding an optimal geometric configuration for tdoa location systems with reinforcement learning |
| topic | Geometry optimaztion passive location TDOA reinforcement learning actor-critic |
| url | https://ieeexplore.ieee.org/document/9415644/ |
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