High-precision clock synchronization based on PSO algorithm under the small sample condition

In order to solve the problem of clock synchronization under the condition of small sample set, a state optimal estimation method of data fusion bases on natural selection PSO algorithm is proposed. First, use Kalman filtering algorithm and Bootstrap method to local filtering process the measurement...

Full description

Saved in:
Bibliographic Details
Published in2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) pp. 90 - 97
Main Authors Yilin Jiang, Fangyuan Zhang, Muran Guo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
Bootstrap method
clock synchronization
data fusion
Data integration
Estimation
Filtering
Frequency estimation
Kalman filtering
Particle swarm optimization algorithm
Synchronization
Time-frequency analysis
Wireless sensor networks
Online AccessGet full text
DOI10.1109/IMCEC.2016.7867179

Cover

More Information
Summary:In order to solve the problem of clock synchronization under the condition of small sample set, a state optimal estimation method of data fusion bases on natural selection PSO algorithm is proposed. First, use Kalman filtering algorithm and Bootstrap method to local filtering process the measurement data under the condition of Monte Carlo simulation experiment, and the reliability of the data is improved. Then, use natural selection PSO algorithm to fuse the data, the optimal fused estimation is achieved and the fusion accuracy is improved. Finally, the fusion result is considered as the new information of virtual master clock, which could exchange time messages with node clock of network from the algorithm. Thus, clock synchronization in the network is realized. The simulated results show that the accuracy is improved. High precision clock synchronization is realized and the stability time is decreased.
DOI:10.1109/IMCEC.2016.7867179