摘要
动态环境室内定位容易受到人员随机行动、障碍物等环境的干扰,信号强度的时变性、数据采集的不稳定性对定位算法产生很大的影响。针对该问题,本文提出了一种基于粒子群优化在线顺序极限学习机算法(Particle swarm optimization online sequential extreme learning machine,PSO⁃OS⁃ELM)。该算法继承了在线顺序极限学习机(Online sequential extreme learning machine,OS⁃ELM)算法的数据采集成本低、适应环境变化快、收敛速度较快且定位精度较高等特性,同时又利用粒子群优化(Particle swarm optimization,PSO)解决OS⁃ELM算法中奇异值问题和鲁棒性问题。在3种不同环境下采集数据,将PSO⁃OS⁃ELM算法、OS⁃ELM算法和WKNN算法进行实验对比。实验结果表明:在动态变化的室内环境中,PSO⁃OS⁃ELM算法定位误差较小且鲁棒性增强,优于其他算法;平均定位误差相较于其他算法减少了约15%;算法耗时性相较于传统定位算法加权K近邻算法(Weighted K⁃nearest neighbor,WKNN)算法减少了约55%。
Indoor positioning in dynamic environment is easy to be interfered by human’s random actions and obstacles.The time-varying signal strength and the instability of data acquisition have a great impact on the positioning algorithm.To solve the problem,this paper proposes an online sequential extreme learning machine algorithm based on particle swarm optimization named PSO-OS-ELM.The algorithm inherits the characteristics of the on-line sequential extreme learning machine(OS-ELM)algorithm,such as low data acquisition cost,fast adaptation to environmental changes,fast convergence speed and high positioning accuracy.At the same time,the particle swarm optimization(PSO)is used to solve the singular value problem and instability problem in the OS-ELM algorithm.The PSO-OS-ELM algorithm,the OS-ELM algorithm and the weighted K-nearest neighbor(WKNN)algorithm are compared.The experimental results show that in the dynamic indoor environment,in terms of algorithm stability,the PSO-OS-ELM algorithm has smaller and stable positioning error,and is better than other algorithms.Compared with other algorithms,the average positioning error is reduced by about 15%.Compared with the traditional localization algorithm,the WKNN algorithm reduces the time consumption by about 55%.
作者
韩承毅
苏胜君
施伟斌
乐燕芬
李瑞祥
HAN Chengyi;SU Shengjun;SHI Weibin;LE Yanfen;LI Ruixiang(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《数据采集与处理》
CSCD
北大核心
2022年第6期1345-1352,共8页
Journal of Data Acquisition and Processing
关键词
粒子群优化
在线顺序极限学习机
接收信号强度
动态环境
室内定位
particle swarm optimization(PSO)
online sequential extreme learning machine(OS-ELM)
received signal strength
dynamic environment
indoor positioning
