In order to achieve higher accuracy and lower cost of indoor localization,we propose a positioning method using multiple input and multiple output(MIMO)channel state information(CSI)as a fingerprint.The method can be ...In order to achieve higher accuracy and lower cost of indoor localization,we propose a positioning method using multiple input and multiple output(MIMO)channel state information(CSI)as a fingerprint.The method can be divided into three stages,feature extraction,offline training and online localization.In the feature extraction,the segmented average and principal component analysis(PCA)are used to reduce the data dimension and decrease system complexity.In the offline training,the deep neural network(NN)model is trained to implement the position classification.In the online localization,the data are input into the trained NN model first,and then its output is further processed by weighted k-nearest neighbor(WKNN)technology to estimate the position.Experimental results show that the proposed method can significantly reduce the positioning error compared to other methods and the average error is 1.39m in a complex indoor environment.展开更多
基金the National Natural Science Foundation of China under Grants No.61671367the Key Research and Development Plan of Shaanxi Province under Grant No.2018GY-003+1 种基金the Research Foundation of Science and Technology on Communication Networks Laboratorythe Fundamental Research Funds for the Central Universities.
文摘In order to achieve higher accuracy and lower cost of indoor localization,we propose a positioning method using multiple input and multiple output(MIMO)channel state information(CSI)as a fingerprint.The method can be divided into three stages,feature extraction,offline training and online localization.In the feature extraction,the segmented average and principal component analysis(PCA)are used to reduce the data dimension and decrease system complexity.In the offline training,the deep neural network(NN)model is trained to implement the position classification.In the online localization,the data are input into the trained NN model first,and then its output is further processed by weighted k-nearest neighbor(WKNN)technology to estimate the position.Experimental results show that the proposed method can significantly reduce the positioning error compared to other methods and the average error is 1.39m in a complex indoor environment.
