Title :
An indoor positioning technology based on GA-BP Neural Network
Author :
Miao Kehua ; Chen YaoDong ; Miao Xiao
Author_Institution :
Deptment Of Autom., Xiamen Univ., Xiamen, China
Abstract :
With the development of wireless positioning technologies, indoor positioning technologies are getting more and more noticeable. RFID positioning technology reaches the goal of recognizing and positioning by using radio frequency to transmit data through non-contact two-way communication. This technology has the advantages of long transmission effective range, low-cost, non-contact, non-line-of-sight and so on. Using the technology, a GA-BP Neural Network based indoor positioning algorithm is proposed in this thesis, series of tests to it in practical setting are also carried out. The result shows that the algorithm can solve the problems caused by the complication and variation of indoor environment at a certain degree.
Keywords :
backpropagation; genetic algorithms; indoor radio; neural nets; radiofrequency identification; GA-BP neural network; RFID positioning technology; indoor positioning technology; noncontact two way communication; wireless positioning technologies; Computers; Educational institutions; Genetic algorithms; Input variables; Radiofrequency identification; Training; Wireless sensor networks; GA-BP Neural Network; Index of Received Signal Strength; Indoor Positioning; RFID;
Conference_Titel :
Computer Science & Education (ICCSE), 2011 6th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-9717-1
DOI :
10.1109/ICCSE.2011.6028640