Year: 2022
Type: article
Abstract: This paper proposes a method based on AP clustering and an improved particle swarm algorithm for radio frequency identification (RFID) indoor positioning, called the AP-PSO method. Firstly, an AP clustering algorithm is used to cluster the RSSI values of the experimental region tags with similarity, in order to achieve the division of tagged regions, reduce the search area of the later improved particle swarm algorithm, and reduce the search time. Secondly, the learning factor of the particle swarm algorithm is dynamically adjusted, in order to improve the search ability and convergence speed of the global optimal solution of particles. The experimental results show that the algorithm can effectively achieve RFID indoor positioning of the tags to be measured, with high positioning accuracy and with the algorithm spending less time.
Source: Journal of Sensors
DOI: 10.1155/2022/4121016
Authors: Zhang Manman, Li Peng, Xu He, Wang Ruchuan
Institution: Nanjing University of Posts and Telecommunications
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,830
Awards: G1157344054, G1323003055, 面向泛在弱终端的感控网络多维度均衡化技术研究, G1909186236, G3011794039, G3078441299, G3363098678, G3520471775, G3647447380, G383277235, G4182526848, G4233282613, G428703930, G4414949486, G4762094954, G478566191, G5219783487, G6105858398, G6378474779, G6672977052, G6706491307, G7104655431, G7601404510, G7785462871, G8313843206, G872256573, 基于无人机的空间传感网多源数据收集技术研究, G8987041287
OpenAlex ID: W4293095280