作者: Lei Chen , Jiangfeng Wang , Jian Wang , Zhijun Gao , Jiakuan Dong
DOI: 10.1109/MITS.2019.2903433
关键词: Particle swarm optimization 、 Wireless sensor network 、 Base station 、 Global Positioning System 、 Algorithm 、 Wireless 、 Key (cryptography) 、 Convergence (routing) 、 Evolutionary algorithm 、 Computer science
摘要: As one of key technologies of connected vehicles (CVs) applications, wireless localization can provide accurate and reliable vehicle location for high occupancy tolling and safety critical vehicle applications, such as collision avoidance. Several artificial intelligence methods, such as back propagation neural network (BPNN) and particle swarm optimization (PSO) method, have been employed to optimize the pass-loss model and to improve the accuracy of wireless localization algorithm. However, in view of the stochasticity of initial …