一种基于视距路径识别的设备无关室内定位算法
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摘要
Wi-Fi技术的广泛应用和部署催生了许多基于Wi-Fi的室内定位技术。近年来,基于Wi-Fi的设备无关定位算法引起了研究人员的广泛注意。设备无关定位算法不需要目标对象携带无线传输设备,而是通过测量目标对象对无线信号传输的影响来反向推断目标对象的位置。由于不需要目标对象携带相关设备,因此可以广泛应用于多种场合,如老人健康护理等。已有的设备无关定位技术通常需要事先采集训练数据,因此容易受室内复杂多变的环境干扰,导致定位精度下降。提出一种基于视距路径检测的设备无关定位算法。利用物理层信道状态信息CSI,可以判断一对无线收发设备之间的路径是否是视距LoS路径。在此基础上,提出一个新的设备无关定位算法,该算法在监测区域部署一组Wi-Fi收发装置,对任意一对无线设备,通过识别它们之间是否存在视距路径来判断目标对象是否在这对设备的菲涅耳区域内。此外,还提出一种基于投票的方法来获得目标对象的最可能位置。在实际设备上的实验结果表明,该定位算法可以达到0.5m左右的精度,并且不需事先训练,具有较高的实时性。
The widespread application and deployment of Wi-Fi technology has spawned many Wi-Fibased indoor positioning technologies.In recent years,Wi-Fi-based device-free positioning algorithm has attracted the attention of researchers.The device-free positioning algorithm does not require the target object to carry the wireless transmission device,but instead estimates the target object's position by measuring the impact of the target object on the wireless signal transmission.Because it does not need the target to carry the relevant equipment,it can be widely used in many occasions such as elderly health care.Existing device-free positioning technologies usually require pre-acquisition of training data,so they are easily affected by the indoor complex and changeable environments to result in the degradation of localization accuracy.This paper proposes a device-free localization algorithm based on line-of-sight path detection.By using the Channel State Information(CSI),we can determine whether the path between a pair of wireless transceivers is a Line of Sight(LoS)path.On this basis,we propose a new device-free positioning algorithm.The algorithm deploys a set of Wi-Fi transceivers in the monitoring area.For any pair of wireless devices,we determine whether the target object is within the Fresnel zone of the device by identifying whether there is a line-of-sight path between them or not.We propose a polling-based approach to get the most probable location of the target.The experimental results on the actual equipment show that the proposal can achieve an accuracy of about 0.5 meters,does not require prior training,and has higher real-time performance.
The widespread application and deployment of Wi-Fi technology has spawned many Wi-Fibased indoor positioning technologies.In recent years,Wi-Fi-based device-free positioning algorithm has attracted the attention of researchers.The device-free positioning algorithm does not require the target object to carry the wireless transmission device,but instead estimates the target object's position by measuring the impact of the target object on the wireless signal transmission.Because it does not need the target to carry the relevant equipment,it can be widely used in many occasions such as elderly health care.Existing device-free positioning technologies usually require pre-acquisition of training data,so they are easily affected by the indoor complex and changeable environments to result in the degradation of localization accuracy.This paper proposes a device-free localization algorithm based on line-of-sight path detection.By using the Channel State Information(CSI),we can determine whether the path between a pair of wireless transceivers is a Line of Sight(LoS)path.On this basis,we propose a new device-free positioning algorithm.The algorithm deploys a set of Wi-Fi transceivers in the monitoring area.For any pair of wireless devices,we determine whether the target object is within the Fresnel zone of the device by identifying whether there is a line-of-sight path between them or not.We propose a polling-based approach to get the most probable location of the target.The experimental results on the actual equipment show that the proposal can achieve an accuracy of about 0.5 meters,does not require prior training,and has higher real-time performance.
引文
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[7]石柯,陈洪生,张仁同.一种基于支持向量回归的802.11无线室内定位方法[J].软件学报,2014,25(11):2636-2651.
[11]肖亚龙,张士庚,王建新.一种基于多维标度和区域细化的无线室内定位方法[J].计算机学报,2017,40(8):1918-1932.
[2]Su X Y,Chu C C,Prabhu B S,et al.Identification and data capture[M]∥Yan Lu,Zhang Yan,Yang L T,et al translation.The Internet of Things:From RFID to the Next-Generation Pervasive Networked Systems,2007.
[3]Han D,Jung S H,Lee M,et al.Building apractical Wi-Fibased indoor navigation system[J].IEEE Pervasive Computing,2014,13(2):72-79.
[4]Wang J,Jiang H B,Xiong J,et al.LiFS:Low human effort,device-free localization with fine-grained subcarrier information[C]∥Proc of the 22nd Annual International Conference on Mobile Computing and Networking,2016:243-256.
[5]Wilson J,Patwari N.Radio tomographic imaging with wireless networks[J].IEEE Transactions on Mobile Computing,2010,9(5):621-632.
[6]Chang N,Rashidzadeh R,Ahmadi M.Robust indoor positioning using differential Wi-Fi access points[J].IEEE Transactions on Consumer Electronics,2010,56(3):1860-1867.
[7]Shi Ke,Chen Hong-sheng,Zhang Ren-tong.Indoor location method based on support vector regression in 802.11wireless environment[J].Journal of Software,2014,25(11):2636-2651.(in Chinese)
[8]Seifeldin M,Saeed A,Kosba A E,et al.Nuzzer:A large-scale device-free passive localization system for wireless environments[J].IEEE Transactions on Mobile Computing,2013,12(7):1321-1334.
[9]Paramvir B,Padmanabhan V N.RADAR:An in-building RF-based user location and tracking system[C]∥Proc of IEEE INFOCOM’2000,2000:775-784.
[10]Youssef M,Agrawala A K.The Horus location determination system[J].Wireless Networks,2008,14(3):357-374.
[11]Xiao Ya-long,Zhang Shi-geng,Wang Jian-xin.An indoor localization algorithm based on multidimensional scaling and region refinement[J].Chinese Journal of Computers,2017,40(8):1918-1932.(in Chinese)
[12]Redzic M D,Brennan C,O’Connor N E.SEAMLOC:Seamless indoor localization based on reduced number of calibration points[J].IEEE Transactions on Mobile Computing,2014,13(6):1326-1337.
[13]Han C M,Wu K S,Wang Y X,et al.WiFall:Device-free fall detection by wireless networks[C]∥Proc of IEEE IN-FOCOM’2014,2014:271-279.
[14]Zhou Z M,Yang Z,Wu C S,et al.Lifi:Line-of-sight identification with wifi[C]∥Proc of IEEE INFOCOM’2014,2014:2688-2696.
[15]Zhou Z M,Yang Z,Wu C S,et al.WiFi-based indoor line-ofsight identification[J].IEEE Transactions on Wireless Communications,2015,14(11):6125-6136.
[16]Wilson J,Patwari N.A fade-level skew-laplace signal strength model for device-free localization with wireless networks[J].IEEE Transactions on Mobile Computing,2012,11(6):947-958.
[17]Sen S,Lee J,Kim K H,et al.Avoiding multipath to revive inbuilding WiFi localization[C]∥Proc of the 11th Annual International Conference on Mobile Systems,Applications,and Services,2013:249-262.
[18]Wu K S,Xiao J,Yi Y W,et al.CSI-based indoor localization[J].IEEE Transactions on Parallel and Distributed Systems,2013,24(7):1300-1309.
[19]Rai A,Chintalapudi K K,Padmanabhan V N,et al.Zee:Zero-effort crowdsourcing for indoor localization[C]∥Proc of the 18th Annual International Conference on Mobile Computing and Networking,2012:293-304.
[20]Wu C S,Yang Z,Liu Y H.Smartphones based crowdsourcing for indoor localization[J].IEEE Transactions on Mobile Computing,2015,14(2):444-457.
[21]Cao Y B,Yang Y J,Liu W H.E-FallD:A fall detection system using android-based smartphone[C]∥Proc of 2012 9th IEEE International Conference on Fuzzy Systems and Knowledge Discovery(FSKD),2012:1509-1513.
[22]Wu C S,Yang Z,Zhou Z M,et al.PhaseU:Real-time LOS identification with WiFi[C]∥Proc of INFOCOM’2015,2015:2038-2046.
[23]Goldsmith A.Wireless communications[M].Cambridge:Cambridge,England:Cambridge University Press,2005.
[24]Guo X N,Zhang D,Wu K S,et al.MODLoc:Localizing multiple objects in dynamic indoor environment[J].IEEETransactions on Parallel and Distributed Systems,2014,25(11):2969-2980.
[25]Adib F,Kabelac Z,Katabi D.Multi-person localization via RF body reflections[C]∥Proc of NSDI,2015:279-292.
[7]石柯,陈洪生,张仁同.一种基于支持向量回归的802.11无线室内定位方法[J].软件学报,2014,25(11):2636-2651.
[11]肖亚龙,张士庚,王建新.一种基于多维标度和区域细化的无线室内定位方法[J].计算机学报,2017,40(8):1918-1932.