摘要
为了降低水下传感器时间同步周期长、提高同步效率,本文提出了一种基于因子图模型的水下位置、声速、时延测量值的参数融合方法。该方法在求解系统钟差边缘概率密度函数后,对该函数进行二进制化简,从而快速解算各传感器钟差,实现网络的动态时间统一。试验结果证明:在其动态时间同步准确度高于8×10^(-4) s的前提下,其同步周期仅为现有方法的1/2,并可以在一个周期内完成对整个网络的授时,计算量降低。
To address the problems of lengthy synchronization and low efficiency in underwater sensor networks,a parameter fusion method based on the factor graph model is proposed in this article for measuring underwater loca-tion,sound speed,and time delay.After calculating the marginal probability density function of system clock bias parameters,it is simplified through binarization;thus,the clock bias parameters of each sensor can be quickly calculated,enabling dynamic time synchronization across the network.Experimental results demonstrate that under the assumption of high synchronization accuracy of larger than 8×10^(-4)s,the synchronization period is only half the current methods,and the time setting of the entire network can be achieved within one cycle,reducing computa-tional load.
作者
孙大军
欧阳雨洁
韩云峰
王泽彧
刘璐
SUN Dajun;OUYANG Yujie;HAN Yunfeng;WANG Zeyu;LIU Lu(National Key Laboratory of Underwater Acoustic Technology,Harbin Engineering University,Harbin 150001,China;Key Labo-ratory of Marine Information Acquisition and Security(Harbin Engineering University),Ministry of Industry and Information Technolo-gy,Harbin 150001,China;College of Underwater Acoustic Engineering,Harbin Engineering University,Harbin 150001,China)
出处
《哈尔滨工程大学学报》
EI
CAS
CSCD
北大核心
2023年第11期1996-2004,共9页
Journal of Harbin Engineering University
基金
国家重点研发计划(2021YFC2801300)
黑龙江省自然科学基金项目(YQ2019D003).
关键词
概率图模型
因子图模型
水下时间同步方法
水下授时
水下传感器网络
和积算法
概率密度函数
全局函数
probabilistic graph model
factor graph model
underwater time synchronization method
underwater timekeeping
underwater sensor network
sum-product algorithm
probability density function
global function
