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
针对基于接收信号强度定位(received signal strength indication,RSSI)技术的无线保真(wireless fidelity,WiFi)室内定位技术,存在由于WiFi信号强度受多径效应和噪声的影响导致定位精度低和性能不稳定等的问题。与RSSI相比,信道状态信...针对基于接收信号强度定位(received signal strength indication,RSSI)技术的无线保真(wireless fidelity,WiFi)室内定位技术,存在由于WiFi信号强度受多径效应和噪声的影响导致定位精度低和性能不稳定等的问题。与RSSI相比,信道状态信息(channel status information,CSI)能有效避免多径效应给定位结果带来的不良影响,因此,采用CSI的值作为定位的特征值,建立Radio Map的位置指纹数据库,通过加权邻近算法匹配k组最近的指纹库数据估测出定位点位置。实验结果表明,与RSSI相比WiFi指纹定位采用CSI作为特征值提高了定位精度和稳定性。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.
文摘针对基于接收信号强度定位(received signal strength indication,RSSI)技术的无线保真(wireless fidelity,WiFi)室内定位技术,存在由于WiFi信号强度受多径效应和噪声的影响导致定位精度低和性能不稳定等的问题。与RSSI相比,信道状态信息(channel status information,CSI)能有效避免多径效应给定位结果带来的不良影响,因此,采用CSI的值作为定位的特征值,建立Radio Map的位置指纹数据库,通过加权邻近算法匹配k组最近的指纹库数据估测出定位点位置。实验结果表明,与RSSI相比WiFi指纹定位采用CSI作为特征值提高了定位精度和稳定性。
