A Fuzzy Adaptive Tightly-Coupled Integration Method for Mobile Target Localization Using SINS/WSN
| Autor: | Zhang Jinyao, Hai Yang, Wei Li, Chengming Luo, Mengbao Fan, Si Zhuoyin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Engineering
Positioning system lcsh:Mechanical engineering and machinery Real-time computing 02 engineering and technology 01 natural sciences Article mobile target wireless sensor network (WSN) strapdown inertial navigation system (SINS) integrated positioning tightly-coupled integration fuzzy adaptive Kalman filter Control theory 0202 electrical engineering electronic engineering information engineering Computer Science::Networking and Internet Architecture lcsh:TJ1-1570 Electrical and Electronic Engineering Inertial navigation system business.industry Covariance matrix Mechanical Engineering Node (networking) 010401 analytical chemistry 020206 networking & telecommunications Covariance 0104 chemical sciences Control and Systems Engineering business Wireless sensor network Multipath propagation |
| Zdroj: | Micromachines Micromachines, Vol 7, Iss 11, p 197 (2016) Micromachines; Volume 7; Issue 11; Pages: 197 |
| ISSN: | 2072-666X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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