Jinyao Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jinyao Zhang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Chengming Luo
Ocean College, Jiangsu University of Science and Technology, Zhenjiang, People’s Republic of China
1 shared publication - Hai Yang
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
1 shared publication - Mengbao Fan
China University of Mining and Technology
1 shared publication - Wei Li
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
1 shared publication - Zhuoyin Si
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
1 shared publication
Latest Publications
- Open Access
- 2 November 2016
- Published by MDPI AG in Micromachines
- Vol. 7 (11) , 197
A Fuzzy Adaptive Tightly-Coupled Integration Method for Mobile Target Localization Using SINS/WSN
In recent years, mobile target localization for enclosed environments has been a growing interest. In this paper, we have proposed a fuzzy adaptive tightly-coupled integration (FATCI) method for positioning and tracking applications using strapdown inertial navigation system (SINS) and wireless sensor network (WSN). The wireless signal outage and severe multipath propagation of WSN often influence the accuracy of measured distance and lead to difficulties with the WSN positioning. Note also that the SINS are known for their drifted error over time. Using as a base the well-known loosely-coupled integration method, we have built a tightly-coupled integrated positioning system for SINS/WSN based on the measured distances between anchor nodes and mobile node. The measured distance value of WSN is corrected with a least squares regression (LSR) algorithm, with the aim of decreasing the systematic error for measured distance. Additionally, the statistical covariance of measured distance value is used to adjust the observation covariance matrix of a Kalman filter using a fuzzy inference system (FIS), based on the statistical characteristics. Then the tightly-coupled integration model can adaptively adjust the confidence level for measurement according to the different measured accuracies of distance measurements. Hence the FATCI system is achieved using SINS/WSN. This innovative approach is verified in real scenarios. Experimental results show that the proposed positioning system has better accuracy and stability compared with the loosely-coupled and traditional tightly-coupled integration model for WSN short-term failure or normal conditions.
- 2 November 2016
- Published by MDPI AG in Micromachines
- Vol. 7 (11) , 197
Open AccessA Fuzzy Adaptive Tightly-Coupled Integration Method for Mobile Target Localization Using SINS/WSN
In recent years, mobile target localization for enclosed environments has been a growing interest. In this paper, we have proposed a fuzzy adaptive tightly-coupled integration (FATCI) method for positioning and tracking applications using strapdown inertial navigation system (SINS) and wireless sensor network (WSN). The wireless signal outage and severe multipath propagation of WSN often influence the accuracy of measured distance and lead to difficulties with the WSN positioning. Note also that the SINS are known for their drifted error over time. Using as a base the well-known loosely-coupled integration method, we have built a tightly-coupled integrated positioning system for SINS/WSN based on the measured distances between anchor nodes and mobile node. The measured distance value of WSN is corrected with a least squares regression (LSR) algorithm, with the aim of decreasing the systematic error for measured distance. Additionally, the statistical covariance of measured distance value is used to adjust the observation covariance matrix of a Kalman filter using a fuzzy inference system (FIS), based on the statistical characteristics. Then the tightly-coupled integration model can adaptively adjust the confidence level for measurement according to the different measured accuracies of distance measurements. Hence the FATCI system is achieved using SINS/WSN. This innovative approach is verified in real scenarios. Experimental results show that the proposed positioning system has better accuracy and stability compared with the loosely-coupled and traditional tightly-coupled integration model for WSN short-term failure or normal conditions.