电讯技术2023，Vol.63Issue(12)：1876-1884,9.DOI:10.20079/j.issn.1001-893x.230831004
一种基于大规模MIMO系统的三维空间指纹定位方法
A Three-dimensional Space Fingerprint Localization Method Based on Massive MIMO System
摘要
Abstract
In order to solve the problems of existing fingerprint localization technology,such as large amount of fingerprint data,difficulty in storage and processing,and insufficient adaptability to positioning in complex spaces,a three-dimensional indoor space fingerprint localization solution based on massive multiple-input multiple-output(MIMO)system is proposed.First,an Angle Delay Channel Frequency Power(ADCFP)fingerprint matrix with faster processing speed and smaller storage requirements is proposed.Secondly,a new similarity criterion,namely chi-square distance,is introduced to improve the positioning accuracy,and then an improved power Weighted K-Nearest Neighbor(WKNN)matching algorithm is proposed.The impact of the power value on the weight reduction speed is different,and different weights are allocated according to the fingerprint similarity.Finally,three types of compressed fingerprints are obtained by using row-by-column compression of ADCFP,further reducing the amount of fingerprint data.And the Central Angle of Arrival(CAOA)Clustering Algorithm is introduced to shorten the positioning time.The simulation results show that the ADCFP fingerprint matrix can offer a 89.2% reliability for 2 m accuracy.The average positioning error using chi-square distance is reduced by 5.63% compared with that using the Manhattan distance.The improved power WKNN algorithm reduces the average localization error by 4.45% compared with the traditional WKNN algorithm.The introduction of CAOA Clustering Algorithm can increase the localization speed to 1.72 times that of the non-clustering case.The average localization error is reduced by 44.05% compared with the K-means Clustering Algorithm,and the positioning performance is greatly improved.关键词
三维室内空间/指纹定位/大规模MIMO/加权K近邻(WKNN)/中心到达角(CAOA)聚类Key words
3D indoor space/fingerprint localization/massive MIMO/weighted K-nearest neighbor(WKNN)/central angle of arrival(CAOA)clustering分类
信息技术与安全科学引用本文复制引用
贺晨琳,王霄峻,汪磊..一种基于大规模MIMO系统的三维空间指纹定位方法[J].电讯技术,2023,63(12):1876-1884,9.基金项目
国家重点研发计划(2022YFC38010000) （2022YFC38010000）
中央高校基本科研业务费专项资金(2242022k60001) （2242022k60001）
东南大学院系联合基金(2242023K40015) （2242023K40015）
