To solve the problem of variations in radio frequency characteristics among different devices,transfer learning is applied to transform device diversity to domain adaptation in the indoor localization algorithm.A robu...To solve the problem of variations in radio frequency characteristics among different devices,transfer learning is applied to transform device diversity to domain adaptation in the indoor localization algorithm.A robust indoor localization algorithm based on the aligned fingerprints and ensemble learning called correlation alignment for localization(CALoc)is proposed with low computational complexity.The second-order statistical properties of fingerprints in the offline and online phase are needed to be aligned.The real-time online calibration method mitigates the impact of device heterogeneity largely.Without any time-consuming deep learning retraining process,CALoc online only needs 0.11 s.The effectiveness and efficiency of CALoc are verified by realistic experiments.The results show that compared to the traditional algorithms,a significant performance gain is achieved and that it achieves better positioning accuracy with a 19%improvement.展开更多
WLAN mdoor location method based on artificial neural network （ANN） is analyzed. A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving stor...WLAN mdoor location method based on artificial neural network （ANN） is analyzed. A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving storage cost of the radio map establishment and enhancing real-time capacity in the on-line phase. According to the analysis of SNR distributions of recorded beacon signal samples and discussion about the multi-mode phenomenon, the one map method is proposed for the purpose of simplifying ANN input values and increasing location performances. Based on the simulations and comparison analysis with other two typical indoor location methods, K-nearest neighbor （KNN） and probability, the feasibility and effectiveness of ANN-based indoor location method are verified with average location error of 2.37m and location accuracy of 78.6% in 3m.展开更多
基金The National Key Research and Development Program of China(No.2018YFB1802400)the National Natural Science Foundation of China(No.61571123)the Research Fund of National M obile Communications Research Laboratory,Southeast University(No.2020A03)
文摘To solve the problem of variations in radio frequency characteristics among different devices,transfer learning is applied to transform device diversity to domain adaptation in the indoor localization algorithm.A robust indoor localization algorithm based on the aligned fingerprints and ensemble learning called correlation alignment for localization(CALoc)is proposed with low computational complexity.The second-order statistical properties of fingerprints in the offline and online phase are needed to be aligned.The real-time online calibration method mitigates the impact of device heterogeneity largely.Without any time-consuming deep learning retraining process,CALoc online only needs 0.11 s.The effectiveness and efficiency of CALoc are verified by realistic experiments.The results show that compared to the traditional algorithms,a significant performance gain is achieved and that it achieves better positioning accuracy with a 19%improvement.
文摘WLAN mdoor location method based on artificial neural network （ANN） is analyzed. A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving storage cost of the radio map establishment and enhancing real-time capacity in the on-line phase. According to the analysis of SNR distributions of recorded beacon signal samples and discussion about the multi-mode phenomenon, the one map method is proposed for the purpose of simplifying ANN input values and increasing location performances. Based on the simulations and comparison analysis with other two typical indoor location methods, K-nearest neighbor （KNN） and probability, the feasibility and effectiveness of ANN-based indoor location method are verified with average location error of 2.37m and location accuracy of 78.6% in 3m.
