An Extended Kalman Filter-Based Data Fusion Method for Wireless Sensor Networks

2011 ◽  
pp. 407-415
Author(s):  
Xiaona Hu ◽  
Lihong Ren ◽  
Yongsheng Ding
Keyword(s):  
Wireless Sensor Networks ◽  
Kalman Filter ◽  
Sensor Networks ◽  
Data Fusion ◽  
Extended Kalman Filter ◽  
Wireless Sensor ◽  
Fusion Method

Study of Data Fusion Mechanism Based on the WSN

2013 ◽  
Vol 321-324 ◽  
pp. 600-603
Author(s):  
Wei Liu ◽  
Qin Sheng Du ◽  
Le Le Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Wireless Sensor ◽  
Network Technology ◽  
Fusion Method ◽  
Simulation Test ◽  
Embedded Computing ◽  
New Type

Wireless sensor networks integrated four technologies including sensor, embedded computing, network technology and wireless communication. It is a new type of non-infrastructure wireless network. In this paper, a data fusion method has been brought forward based on wireless sensor networks, and through an algorithm simulation test, It is proved that the algorithm is effective to reduce the energy consumption of the network, and extend the lifetime of the network.

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.

scholarly journals A Data Fusion Method in Wireless Sensor Networks

Sensors ◽  
2015 ◽  
Vol 15 (2) ◽  
pp. 2964-2979 ◽  
Author(s):  
Davood Izadi ◽  
Jemal Abawajy ◽  
Sara Ghanavati ◽  
Tutut Herawan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Wireless Sensor ◽  
Fusion Method
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