A general clock synchronization method based on Kalman Filter model in Wireless Sensor Networks

2012 ◽  
Author(s):  
Xiali Li ◽  
Min Xi ◽  
Yongcun Cao ◽  
Jie Yuan
Keyword(s):  
Wireless Sensor Networks ◽  
Kalman Filter ◽  
Sensor Networks ◽  
Clock Synchronization ◽  
Wireless Sensor ◽  
Filter Model ◽  
Synchronization Method

scholarly journals Pair Nodes Clock Synchronization Algorithm Based on Kalman Filter for Underwater Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4426
Author(s):  
Xiaomeng Ni ◽  
Ting Lu ◽  
Sijia Ye ◽  
Yunsi Zheng ◽  
Pengfei Chen ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Kalman Filter ◽  
Sensor Networks ◽  
Time Synchronization ◽  
Clock Synchronization ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Clock Skew ◽  
Time Stamps ◽  
Clock Offset

Time synchronization is the basis of many applications. Aiming at the limitations of the existing clock synchronization algorithms in underwater wireless sensor networks, we propose a pairwise synchronization algorithm called K-Sync, which is based on the Kalman filter. The algorithm does not need the assistance of the position sensor or the speed sensor, and the high time synchronization accuracy can be realized only by utilizing the time-stamps information in the process of message exchange. The K-Sync uses the general constraints of the motion characteristics of the sensor nodes to establish the recursive equations of the clock skew, clock offset, relative mobility velocity, and relative distance. At the same time, the time-stamps are viewed as the observation variables and the system observation equation is obtained. The K-Sync estimates the normalized clock skew and offset of the node via the Kalman filter to achieve high-precision clock synchronization between the two nodes. The simulation shows that the K-Sync has obvious advantages in the key indicators such as the estimated accuracy of clock skew and clock offset, convergence speed, etc. In addition, the K-Sync is more robust to a variety of underwater motion scenes.

scholarly journals A Fusion Localization Method based on a Robust Extended Kalman Filter and Track-Quality for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3638 ◽  
Author(s):  
Yan Wang ◽  
Huihui Jie ◽  
Long Cheng
Keyword(s):  
Wireless Sensor Networks ◽  
Kalman Filter ◽  
Sensor Networks ◽  
Extended Kalman Filter ◽  
Wireless Sensor ◽  
Multiple Model ◽  
Fusion Algorithm ◽  
Time Step ◽  
Localization Method ◽  
Track Quality

As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF.

Sign in / Sign up

Export Citation Format

Share Document