Using Shape Constraints to Improve in RFID Positioning Accuracy

碩士 === 國立陽明大學 === 醫學工程研究所 === 97 === In recent years, the advanced development of communication technology and wireless network applications has been utilized in various areas. The obvious one is a concept of Home Media Center which brings enormous business opportunities, especially indoor positioni...

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Bibliographic Details
Main Authors: Wei-Hong Chen, 陳韋宏
Other Authors: Woei-Chyn Chu
Format: Others
Language:zh-TW
Published: 2009
Online Access:http://ndltd.ncl.edu.tw/handle/856nd5
Description
Summary:碩士 === 國立陽明大學 === 醫學工程研究所 === 97 === In recent years, the advanced development of communication technology and wireless network applications has been utilized in various areas. The obvious one is a concept of Home Media Center which brings enormous business opportunities, especially indoor positioning system. Although Global Positioning System (GPS) is a mature and popular technology, it can only be used outdoors. There are various ways effective to solve the indoor positioning problems, for example by the means of infrared ray, ultrasound, image identify, and wireless, etc. Became nowadays, the applications of radio waves have become the mainstream on indoor positioning researches. LANDMARC proposed to use a set of reference RFID tags to help positioning specific tracking tags. By comparing the signal strength between the tracking tag and four reference tags, an average positioning error of 1.09 meters can be achieved. The method in this study is to apply the Shape Constrain algorithm to select the most suitable triangle formed by three reference tags, not necessary the nearest neighbors. Our results showed that average positioning accuracy, compared to LANDMARC, increased by "43.65%" when three RF readers and the inter-reference tag, whose interval is 1 meter, were used. The accuracy of our system was 0.65 meter in a controlled interference environment. In a less controlled interference environment, the accuracy was 1.52 meter. In conclusion, Shape Constraint algorithm elevated the positioning accuracy because it reduced the error probability in selecting unreasonable four-nearest neighbor reference tags for positioning calculation.