电子科技大学学报2026，Vol.55Issue(3)：398-410,13.DOI:10.12178/1001-0548.2025032
基于CSI的单AP港口人员指纹定位方法
Single AP port personnel fingerprint location method based on CSI
摘要
Abstract
The port is an important logistics and transportation hub in our country,the high-precision port personnel positioning system plays an important role in the process of safety production and management.By combining radial basis function(RBF)and extreme learning machine(ELM),an RBF-ELM port personnel location method based on channel state information(CSI)is proposed to realize personnel location by a single access point(AP).In the offline training stage,the positioning area is first gridded and the CSI data of each grid position is measured;position fingerprints are constructed by fusing the amplitude difference and linearly reconstructed phase information,and dimensionality reduction was performed by principal component analysis(PCA);subsequently,simulated annealing(SA)is embedded into quantum particle swarm optimization(QPSO)to improve the global search ability,find the optimal parameters of the model,and thereby improve the accuracy and generalization ability of positioning model.In the online prediction stage,the prediction results of the model are matched with labels and coordinates in the location library by weighted K-nearest neighbors(WKNN)matching algorithm to obtain more accurate positioning coordinates.At the same time,the weight update method of dynamic forgetting factor is adopted,and only part of calibration data can be collected to complete the update of positioning model weight,which effectively alleviates the problem of positioning accuracy decline caused by changes in time and environment.In the simulated port scenario,the probability of the positioning accuracy of the system reaching 1m in line-of-sight(LOS)environment is more than 92%,the static average error is 0.58 m,and the dynamic average error is 0.86 m.The static average positioning error of 0.67 m can also be obtained in non-line-of-sight(NLOS)environment.Compared with other positioning methods,the proposed algorithm has improved positioning a ccuracy,timeliness and stability.关键词
港口人员定位/信道状态信息/极限学习机/量子粒子群/动态权值更新Key words
port personnel location/channel state information/extreme learning machine/quantum particle swarm/dynamic weight update分类
交通工程引用本文复制引用
姜浩,郭子坚,孙瑾..基于CSI的单AP港口人员指纹定位方法[J].电子科技大学学报,2026,55(3):398-410,13.基金项目
国家自然科学基金(52272318) （52272318）
