现代电子技术2026，Vol.49Issue(16)：54-61,8.DOI:10.16652/j.issn.1004-373X.2026.16.009
基于翘曲高斯过程与JAYA-KELM的指纹定位算法
Fingerprint localization algorithm based on warped gaussian process and JAYA-KELM
摘要
Abstract
In allusion to the challenges of fingerprint database expansion errors and insufficient online positioning accuracy in WiFi fingerprinting localization,a novel fingerprint localization algorithm that integrates warped gaussian process regression(WGPR)with JAYA-optimized kernel extreme learning machine(KELM)is proposed.In the offline phase,the MaxMean is used to select high-information access points,thereby reducing redundancy and enhancing feature quality.WGPR is then introduced to map the received signal strength(RSS)from a non-Gaussian space into a latent Gaussian space by means of nonlinear warping function.A composite kernel function is incorporated to strengthen the modeling capability for multi-scale signal variations and construct a highly robust fingerprint database.In the online phase,the JAYA algorithm is used to optimize the hyperparameters of the KELM model,enhancing its nonlinear fitting capability and generalization performance,and ultimately achieving high-precision mapping from RSS to position coordinates.The experimental results in indoor environments demonstrate that,in comparison with the other algorithms,the proposed WGPR-JAYA-KELM model can realize an average positioning error of 0.988 1 m under complex conditions,significantly improving both localization accuracy and robustness.关键词
WiFi指纹定位/翘曲高斯过程回归/极限学习机/JAYA算法/接收信号强度/定位精度Key words
WiFi fingerprinting localization/warped gaussian process regression/kernel extreme learning machine/JAYA algorithm/received signal strength/localization accuracy分类
信息技术与安全科学引用本文复制引用
周军,张英汉..基于翘曲高斯过程与JAYA-KELM的指纹定位算法[J].现代电子技术,2026,49(16):54-61,8.基金项目
吉林省教育厅产业化培育资助项目(JJKH20240147CY) （JJKH20240147CY）
