Axial-Decoupled Indoor Positioning Based on Location Fingerprints
Indoor positioning using location fingerprints,which are received signal strength(RSS)from wireless access points(APs),has become a hot research topic during the last a few years.Traditional pattern classification based fingerprinting localization methods suffer high computational burden and require a large number of classifiers to determine the object location.To handle this problem,axial-decoupled indoor positioning based on location-fingerprints is proposed in this paper.The purpose is to reduce the decision complexity while keeping localization accuracy through computing the position on X-and Y-axis independently.First,the framework of axial-decoupled indoor positioning using location fingerprints is given.Then,the training and decision process of the proposed axial-decoupled indoor positioning is described in detail.Finally,pattern classifiers including the least squares support vector machine(LS-SVM),support vector machine(SVM)and traditional k-nearest neighbors(K-NN)are adopted and embedded in the proposed framework.Experimental results illustrate the effectiveness of the proposed axial-decoupled positioning method.
Location fingerprint Axial-decoupled Indoor positioning Pattern classification
Wei Yanhua Zhou Yan Wang Dongli Wang Xianbing
Institute of Control Engineering,Xiangtan University,Xiangtan 411105,China
国际会议
第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)
成都
英文
13-26
2016-11-03（万方平台首次上网日期，不代表论文的发表时间）