Robust High-Precision Time Synchronization for Distributed Sensor Systems in Challenging Environments.
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| Title: | Robust High-Precision Time Synchronization for Distributed Sensor Systems in Challenging Environments. |
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| Authors: | Wang, Zhouji1 (AUTHOR), Lyu, Daqian1,2 (AUTHOR) daqian_lv@nudt.edu.cn, Zhou, Peiyuan3 (AUTHOR), Ge, Yulong4 (AUTHOR), Hu, Yao1,4 (AUTHOR), Zhu, Rangang1,2 (AUTHOR), Wang, Wei2,3 (AUTHOR), Yang, Xiaoniu2,4 (AUTHOR) |
| Source: | Remote Sensing. Nov2025, Vol. 17 Issue 22, p3715. 29p. |
| Subjects: | Distributed sensors, Reinforcement learning, Bayesian analysis, Time-of-flight measurements, Impulse response |
| Abstract: | Highlights: What are the main findings? A novel DSTS architecture was proposed, integrating Bayesian filtering with DDPG reinforcement learning to solve synchronization in Challenging environments. The DSTS architecture achieved a final frequency synchronization precision of 4 × 10 − 10 and a phase precision of 5 × 10 − 10 s. What are the implications of the main findings? The fusion of ToF and CIR data via Bayesian filtering provides an effective method to address non-linear communication errors and propagation path state uncertainty. The use of a DDPG agent as an "attention-like" mechanism is a viable strategy for managing network heterogeneity. Timing and time synchronization are critical capabilities of Global Navigation Satellite Systems (GNSSs), but their performance deteriorates significantly in challenging environments like urban canyons and tunnels. To address this issue, this paper proposes the Distributed Sensor Time Synchronization architecture (DSTS), a novel architecture integrating Bayesian filtering with deep reinforcement learning. DSTS utilizes Bayesian filtering to fuse Time-of-Flight (ToF) measurements with Channel Impulse Response features for real-time compensation of non-linear errors and accurate path state prediction. Concurrently, the Deep Deterministic Policy Gradient (DDPG) algorithm trains each node into an intelligent agent that dynamically learns optimal synchronization weights based on local information like neighbor clock stability and link quality. This allows the architecture to adaptively amplify reliable nodes while mitigating the negative effects of unstable peers and adverse channels, ensuring high accuracy and availability. Simulation experiments based on a real-world UWB dataset demonstrate the architecture's exceptional performance. The Bayesian filtering module effectively mitigates non-linear errors, reducing the standard deviation of ToF measurements in NLOS scenarios by up to 51.6% (over 41.2% consistently) while achieving high path state prediction accuracy (>85% static, >95% simulated dynamic). In simulated dynamic and heterogeneous networks, the DDPG algorithm achieves a synchronization accuracy better than traditional average-consensus algorithms, ultimately reaching a frequency and phase precision of 4 × 10 − 10 and 5 × 10 − 10 s, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 189674958 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Robust High-Precision Time Synchronization for Distributed Sensor Systems in Challenging Environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Zhouji%22">Wang, Zhouji</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lyu%2C+Daqian%22">Lyu, Daqian</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> daqian_lv@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Peiyuan%22">Zhou, Peiyuan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ge%2C+Yulong%22">Ge, Yulong</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Yao%22">Hu, Yao</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Rangang%22">Zhu, Rangang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Wei%22">Wang, Wei</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xiaoniu%22">Yang, Xiaoniu</searchLink><relatesTo>2,4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Nov2025, Vol. 17 Issue 22, p3715. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Distributed+sensors%22">Distributed sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Time-of-flight+measurements%22">Time-of-flight measurements</searchLink><br /><searchLink fieldCode="DE" term="%22Impulse+response%22">Impulse response</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A novel DSTS architecture was proposed, integrating Bayesian filtering with DDPG reinforcement learning to solve synchronization in Challenging environments. The DSTS architecture achieved a final frequency synchronization precision of 4 × 10 − 10 and a phase precision of 5 × 10 − 10 s. What are the implications of the main findings? The fusion of ToF and CIR data via Bayesian filtering provides an effective method to address non-linear communication errors and propagation path state uncertainty. The use of a DDPG agent as an "attention-like" mechanism is a viable strategy for managing network heterogeneity. Timing and time synchronization are critical capabilities of Global Navigation Satellite Systems (GNSSs), but their performance deteriorates significantly in challenging environments like urban canyons and tunnels. To address this issue, this paper proposes the Distributed Sensor Time Synchronization architecture (DSTS), a novel architecture integrating Bayesian filtering with deep reinforcement learning. DSTS utilizes Bayesian filtering to fuse Time-of-Flight (ToF) measurements with Channel Impulse Response features for real-time compensation of non-linear errors and accurate path state prediction. Concurrently, the Deep Deterministic Policy Gradient (DDPG) algorithm trains each node into an intelligent agent that dynamically learns optimal synchronization weights based on local information like neighbor clock stability and link quality. This allows the architecture to adaptively amplify reliable nodes while mitigating the negative effects of unstable peers and adverse channels, ensuring high accuracy and availability. Simulation experiments based on a real-world UWB dataset demonstrate the architecture's exceptional performance. The Bayesian filtering module effectively mitigates non-linear errors, reducing the standard deviation of ToF measurements in NLOS scenarios by up to 51.6% (over 41.2% consistently) while achieving high path state prediction accuracy (>85% static, >95% simulated dynamic). In simulated dynamic and heterogeneous networks, the DDPG algorithm achieves a synchronization accuracy better than traditional average-consensus algorithms, ultimately reaching a frequency and phase precision of 4 × 10 − 10 and 5 × 10 − 10 s, respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17223715 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 3715 Subjects: – SubjectFull: Distributed sensors Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Time-of-flight measurements Type: general – SubjectFull: Impulse response Type: general Titles: – TitleFull: Robust High-Precision Time Synchronization for Distributed Sensor Systems in Challenging Environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Zhouji – PersonEntity: Name: NameFull: Lyu, Daqian – PersonEntity: Name: NameFull: Zhou, Peiyuan – PersonEntity: Name: NameFull: Ge, Yulong – PersonEntity: Name: NameFull: Hu, Yao – PersonEntity: Name: NameFull: Zhu, Rangang – PersonEntity: Name: NameFull: Wang, Wei – PersonEntity: Name: NameFull: Yang, Xiaoniu IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 22 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
