Research on Indoor Location Technology based on Back Propagation Neural Network and Taylor Series
The traditional indoor location algorithm based on distance-loss model mostly turn received signal strength indicator RSSI into distance, and then through the location-distance algorithm to achieve positioning. These algorithms need fit the wireless signal propagation model parameters A and N through experience or large amounts of data, so they are dependent on experience and are not strong universal algorithms for location of the different environment, also low accuracy. After lots of research and analysis of radio signal propagation model and the traditional indoor location algorithm, a new indoor location algorithm uses BP neural network to fit the distance-loss model is proposed. From a number of distances between reference nodes and blind node, Taylor series expansion algorithm is used to determine the coordinates of the blind node. Finally, the experiment result shows that the new algorithm improves the positioning accuracy and universality, compared with the traditional positioning algorithms.
Indoor location Back propagation neural network (BPNN) Received signal strength indicator (RSSI) Zigbee Taylor Series.
SHI Xiao-Wei ZHANG Hui-Qing
College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
1898-1902
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）