Robust High-Precision Time Synchronization for Distributed Sensor Systems in Challenging Environments

Guardado en:

Detalles Bibliográficos
Publicado en:	Remote Sensing vol. 17, no. 22 (2025), p. 3715-3744
Autor principal:	Wang, Zhouji
Otros Autores:	Lyu Daqian, Zhou, Peiyuan, Ge Yulong, Hu, Yao, Zhu Rangang, Wang, Wei, Yang Xiaoniu
Publicado:	MDPI AG
Materias:	Wireless communications Accuracy Intelligent agents Protocol Algorithms Clocks & watches Synchronization Distributed sensor systems Time synchronization Machine learning Localization Reinforcement Heterogeneity Frequency synchronization Propagation Remote sensing Bayesian analysis Canyons Sensors Design Crystal oscillators Error reduction Impulse response Deep learning Real time Street canyons Filtration Global navigation satellite system
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Descripción
Resumen:	<sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>A novel DSTS architecture was proposed, integrating Bayesian filtering with DDPG reinforcement learning to solve synchronization in Challenging environments. <list-item> The DSTS architecture achieved a final frequency synchronization precision of <inline-formula>4×10−10</inline-formula> and a phase precision of <inline-formula>5×10−10</inline-formula> s. </list-item> What are the implications of the main findings? <list list-type="bullet"> <list-item> </list-item>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. <list-item> The use of a DDPG agent as an “attention-like” mechanism is a viable strategy for managing network heterogeneity. </list-item> 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 <inline-formula>4×10−10</inline-formula> and <inline-formula>5×10−10</inline-formula> s, respectively.
ISSN:	2072-4292
DOI:	10.3390/rs17223715
Fuente:	Advanced Technologies & Aerospace Database