The indoor positioning technology based on fingerprint attracts the attentions from many researchers. RFID（RadioFrequency Identification） technology is more attractive, due to its high accuracy and adaptability to different environment. However, because of the heavy consumption of the computing power, it limits the applications in practice. A new hybrid Kmeans and Weighted K-nearest Neighbor method is proposed and applied in real-world indoor positioning. The new method divided the mapping area into several classes based on a clustering method. A matching into class is done first and then location is determined. The result shows that the proposed method reduces the accumulated errors and thus reduces the computational power whist maintains reasonable accuracy.
|Translated title of the contribution||Indoor positioning method using RFID and block clustering|
|Number of pages||6|
|Journal||Computer Engineering and Applications|
|Early online date||16 Feb 2015|
|Publication status||Published - 15 May 2016|
- Radio Frequency Identification (RFID)