EUL: An Efficient and Universal Localization Method for Wireless Sensor Network

2009 ◽  
Author(s):  
Wei Xi ◽  
Jizhong Zhao ◽  
Xue Liu ◽  
Xiang-Yang Li ◽  
Yong Qi
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Localization Method ◽  
Universal Localization

scholarly journals A Hierarchical Voting Based Mixed Filter Localization Method for Wireless Sensor Network in Mixed LOS/NLOS Environments

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2348 ◽  
Author(s):  
Yan Wang ◽  
Jinquan Hang ◽  
Long Cheng ◽  
Chen Li ◽  
Xin Song
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Unscented Kalman Filter ◽  
Rapid Development ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Localization Method

In recent years, the rapid development of microelectronics, wireless communications, and electro-mechanical systems has occurred. The wireless sensor network (WSN) has been widely used in many applications. The localization of a mobile node is one of the key technologies for WSN. Among the factors that would affect the accuracy of mobile localization, non-line of sight (NLOS) propagation caused by a complicated environment plays a vital role. In this paper, we present a hierarchical voting based mixed filter (HVMF) localization method for a mobile node in a mixed line of sight (LOS) and NLOS environment. We firstly propose a condition detection and distance correction algorithm based on hierarchical voting. Then, a mixed square root unscented Kalman filter (SRUKF) and a particle filter (PF) are used to filter the larger measurement error. Finally, the filtered results are subjected to convex optimization and the maximum likelihood estimation to estimate the position of the mobile node. The proposed method does not require prior information about the statistical properties of the NLOS errors and operates in a 2D scenario. It can be applied to time of arrival (TOA), time difference of arrival (TDOA), received signal (RSS), and other measurement methods. The simulation results show that the HVMF algorithm can efficiently reduce the effect of NLOS errors and can achieve higher localization accuracy than the Kalman filter and PF. The proposed algorithm is robust to the NLOS errors.

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