基于PCOS-ELM的室内指纹定位算法

Indoor fingerprint localization algorithm based on PCOS-ELM

针对传统室内指纹定位算法存在定位精度低、对环境适应能力差的问题,提出了一种基于并行混沌优化的在线连续极限学习机(PCOS-ELM)定位算法。离线阶段,通过并行混沌优化算法(PCOA)对极限学习机的隐含层节点参数进行寻优并构建高精度初始定位模型;在线阶段,利用在线连续极限学习机(OS-ELM)使新增位置指纹数据对定位模型进行动态调整,以适应室内环境的变化。结果表明:提出的PCOS-ELM定位算法具有更高的定位精度和更好的环境适应性。

Aiming at the problems of low localization precision and poor adaptive ability to environment of traditional indoor fingerprint localization algorithm, a new algorithm based on parallel chaos optimization online sequential extreme learning machine （PCOS-ELM）is proposed. In offline phase, parallel chaos optimization algorithm（PCOA） is used for searching the optimization parameters of hidden layer nodes in the extreme learning machine,which constructs an initial positioning model with high precision; In the online phase, use online sequential extreme learning machine （OS-ELM） to make the new added location fingerprint data online dynamically adjust localization model so as to adapt indoor environment changes. The results show that PCOS-ELM localizaition algorithm has higher localization precision and better adaptive ability to environment.