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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17223715 |
