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Study On Wireless Sensor Network Node Indoor Positioning Technology

Posted on:2017-04-03Degree:MasterType:Thesis
Country:ChinaCandidate:T F GaoFull Text:PDF
GTID:2308330488462081Subject:Information and Communication Engineering
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With the rapid development of wireless communication technology, research and application of wireless sensor networks has attracted more and more attention. Wherein, positioning technology of network node as the core of wireless sensor network technology, has became a major research popular.This paper focuses on the research of RSSI-based indoor positioning technology. Since the RSSI-based ranging affected by various factors, it will cause great measurement errors. By analyzing the characteristics of this ranging method, we propose a feasible solution to reduce the error.First, we analyzed three main reasons about the RSSI-based ranging: the hardware aspect, ranging model and the environment. The reason of hardware is a error in the circuit board layout and the omni-directional antenna. This paper mainly test four directions RSSI to reduce interference in this regard. In ranging model aspect, We used linear regression analysis to estimate the optimal model parameters. In environment aspect, we apply a Gaussian filter to filter out the big interference sample, and then averaged to reduce the error.After completing ranging, we compared the characteristics of each location algorithm, and selected geometric positioning algorithm. After that we proposed a correction method based on this algorithm. After the experiment in two different sites, it shows a good correction effect, can effectively improve the accuracy. Furthermore, in order to achieve better human-computer interaction, convenient control system working state and observe the effect of positioning, we use QT software to design a PC interface.Finally, based on the positioning of the stationary object, we started research on a moving target tracking. According to the characteristics of a moving target motion, we chose Kalman filter to process the positioning result. First, we built models based on Kalman filter and then realized a Kalman filter based on this model. Finally, we test the program of ranging, positioning, correction and filter in a site, and get a good result.
Keywords/Search Tags:Indoor Localization, RSSI, Geometric Positioning Algorithm, Kalman Filter
PDF Full Text Request
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