一种改进的时间反转二阶段室内定位方法
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摘要
在非视距传输环境下,粗估计阶段接收信号强度(RSS)的特征维度较低会导致定位性能差。针对该问题,提出一种基于多维标度(MDS)算法改进的时间反转二阶段室内定位方法。对RSS和信道频率响应(CFR)分别进行特定参考点采集,采用线性时域滤波缩小信道状态信息的数据动态范围,利用RSS和MDS算法进行位置粗估计,确定待测点所在范围,构建指纹库。使用预处理过后的CFR与子指纹库中的各参考点处CFR计算组合时间反转共振能量(CTRRS)值,并搜索CTRRS最大值的参考点,实现精确定位。实验结果表明,与时间反转室内定位方法相比,改进方法的定位时间提升了56.5%。
In the non-sight distance transmission environment,the lower characteristic dimension of the Received Signal Strength(RSS) in the coarse estimation phase results in poor positioning performance.To address this problem,an improved time reversal two-stage indoor positioning method based on Multi-Dimensional Scale(MDS) algorithm is proposed.Specific reference point of the RSS and the Channel Frequency Response(CFR) are collected.Linear time domain filtering is adopted to narrow the dynamic data range of the Channel State Information(CSI).RSS and MDS algorithms are used for coarse location estimation to determine the location range of to-be-measured points,and the fingerprint database is constructed.The Combined Time Reversed Resonating Strength(CTRRS) value is calculated by using the pre-processed CFR and the CFR at each reference point in the fingerprint sub-library,and the reference point of the CTRRS maximum value is searched for precise positioning.Experimental results show that compared with the time reversal indoor positioning method,the positioning time of the improved method can be increased by 56.5%.
In the non-sight distance transmission environment,the lower characteristic dimension of the Received Signal Strength(RSS) in the coarse estimation phase results in poor positioning performance.To address this problem,an improved time reversal two-stage indoor positioning method based on Multi-Dimensional Scale(MDS) algorithm is proposed.Specific reference point of the RSS and the Channel Frequency Response(CFR) are collected.Linear time domain filtering is adopted to narrow the dynamic data range of the Channel State Information(CSI).RSS and MDS algorithms are used for coarse location estimation to determine the location range of to-be-measured points,and the fingerprint database is constructed.The Combined Time Reversed Resonating Strength(CTRRS) value is calculated by using the pre-processed CFR and the CFR at each reference point in the fingerprint sub-library,and the reference point of the CTRRS maximum value is searched for precise positioning.Experimental results show that compared with the time reversal indoor positioning method,the positioning time of the improved method can be increased by 56.5%.
引文
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[4] QIAN Kun,WU Chenshu,ZHOU Zimu,et al.Inferring motion direction using commodity Wi-Fi for interactive exergames[C]//Proceedings of CHI Conference on Human Factors in Computing Systems.New York,USA:ACM Press,2017:1961-1972.
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[6] 李华亮,钱志鸿,田洪亮.基于核函数特征提取的室内定位算法研究[J].通信学报,2017,38(1):158-167.
[7] 张勇,黄杰,徐科宇.基于PCA-LSSVR算法的WLAN室内定位方法[J].仪器仪表学报,2015,36(2):408-414.
[8] 王保云.物联网技术研究综述[J].电子测量与仪器学报,2009,23(12):1-7.
[9] 任颖,李华伟,吕海燕.物联网技术与应用研究[J].电脑知识与技术,2014,10(14):3252-3253,3269.
[10] WANG Xuyu,GAO Lingjun,MAO Shiwen,et al.CSI-based fingerprinting for indoor localization:a deep learning approach[J].IEEE Transactions on Vehicular Technology,2017,66(1):763-776.
[11] FANG S,CHANG W,YU T,et al.Channel state reconstruction using multilevel discrete wavelet transform for improved fingerprinting-based indoor localization[J].IEEE Sensors Journal,2016,16(21):7784-7791.
[12] ZHAO Linsheng,WANG Hongpeng,LI Pengpeng,et al.An improved WiFi indoor localization method combining channel state information and received signal strength [C]//Proceedings of the 36th Chinese Control Conference.Washington D.C.,USA:IEEE Press,2017:8964-8969.
[13] 李劲松,李云洲,季新生.基于时间反转的二阶段Wi-Fi室内定位方法[J].计算机应用研究,2018,35(10):3057-3061.
[14] 华海亮,关维国,刘志建,等.基于优化贝叶斯的室内WiFi与蓝牙融合定位算法[J].计算机工程,2016,42(11):114-119.
[15] LI Jinsong,LI Yunzhou,JI Xinsheng.A novel method of Wi-Fi indoor localization based on channel state information[C]//Proceedings of the 8th International Conference on Wireless Communications and Signal Processing.Washington D.C.,USA:IEEE Press,2016:1-5.
[16] CHEN Yan,HAN Feng,YANG Yuhan,et al.Time-reversal wireless paradigm for green internet of things:an overview[J].IEEE Internet of Things Journal,2014,1(1):81-98.
[17] CHEN Chen,CHEN Yan,HAN Yi,et al.Achieving centimeter accuracy indoor localization on WiFi platforms:a frequency hopping approach[J].IEEE Internet of Things Journal,2017,4(1):122-134.
[18] XIE Yaxiong,LI Zhenjiang,LI Mo.Precise power delay profiling with commodity WiFi[C]//Proceedings of the 21st Annual International Conference on Mobile Computing and Networking.New York,USA:ACM Press,2015:53-64.