Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength...Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.展开更多
The increasing availability of ubiquitous sensor data on the built environment holds great potential for a new generation of travel and mobility research.Bluetooth technology,for example,is already vastly used in vehi...The increasing availability of ubiquitous sensor data on the built environment holds great potential for a new generation of travel and mobility research.Bluetooth technology,for example,is already vastly used in vehicular transportation management solutions and services.Current studies discuss the potential of this emerging technology for pedestrian mobility research,but it has yet to be examined in a large urban setting.One of the main problems is detecting pedestrians from Bluetooth records since their behavior and movement patterns share similarities with other urban transportation modes.This study aims to accurately detect pedestrians using a network of 65 Bluetooth detectors located in Tel-Aviv,Israel,which record on average over 60,000 unique daily Bluetooth Media-Access-Control addresses.We propose a detection methodology that includes system calibration,effective travel time calculation,and classification by velocity that takes into consideration the probability of vehicular traffic jams.An evaluation of the proposed methodology presents a promising pedestrian detection accuracy rate of 89%.We showcase the results of pedestrian traffic analysis,together with a discussion on the data analysis challenges and limitations.To the best of our knowledge,this work is the first to analyze pedestrian records detection from a Bluetooth network employed in a dynamic urban environment setting.展开更多
为解决室内非视距(non line of sight,NLOS)环境以及低信标部署密度下传统定位算法精度急剧下降的问题,提出一种基于测距修正的低功耗蓝牙(bluetooth low energy,BLE)和行人航位推算(pedestrian dead reckoning,PDR)融合定位方法。通过S...为解决室内非视距(non line of sight,NLOS)环境以及低信标部署密度下传统定位算法精度急剧下降的问题,提出一种基于测距修正的低功耗蓝牙(bluetooth low energy,BLE)和行人航位推算(pedestrian dead reckoning,PDR)融合定位方法。通过SketchUp室内3D建模软件联合射线追踪算法实现BLE的接收信号强度(received signal strength,RSS)快速构建,避免人工繁琐的RSS实地采集。设计一种基于卷积神经网络的变分自编码器(variational autoencoder based on convolution neural network,VAE-CNN)模型对BLE测距误差进行预测和修正,提升BLE定位精度。采用扩展卡尔曼滤波(extended Kalman filter,EKF)融合BLE和PDR的定位结果。实验结果表明,采用测距修正后的BLE测距定位以及EKF融合定位在NLOS以及信标部署密度低的环境下具有较好的定位性能。展开更多
Bluetooth技术可实现短距离、低功耗的无线通信和构成无线网络(Piconet and Scatternet),采用Bluetooth技术构建的数据采集无线网络系统(Data Acquisition Wireless Network System based on Bluetooth:DAWNS-B)便于进行集中测量和控制...Bluetooth技术可实现短距离、低功耗的无线通信和构成无线网络(Piconet and Scatternet),采用Bluetooth技术构建的数据采集无线网络系统(Data Acquisition Wireless Network System based on Bluetooth:DAWNS-B)便于进行集中测量和控制。介绍Bluetooth ASIC的结构特性以及DAWNS—B的结构原理、硬件电路接口和软件系统。DAWNS-B由前、后端单元构成,Bluetooth ASIC的HCI与PC计算机(或单片机)接口,Bluetooth协议软件栈控制Bluetooth ASIC对主机的HCI命令以及前、后端无线通信和网络通信,应用程序实现数据采集、处理DAWNS-B实现测控系统移动数据交换和无线网络功能,扩大和增强了数据采集系统的功能和应用场合。展开更多
基金supported by James M.Cox Foundation,National Institute on Deafness and Other Communication Disorders(grant no.1R21DC021029-01A1)Cox Enterprises Inc.,National Institute of Child Health and Human Development(grant no.AWD-006196-G1)Thrasher Research Fund Early Career Award Program.
文摘Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.
文摘The increasing availability of ubiquitous sensor data on the built environment holds great potential for a new generation of travel and mobility research.Bluetooth technology,for example,is already vastly used in vehicular transportation management solutions and services.Current studies discuss the potential of this emerging technology for pedestrian mobility research,but it has yet to be examined in a large urban setting.One of the main problems is detecting pedestrians from Bluetooth records since their behavior and movement patterns share similarities with other urban transportation modes.This study aims to accurately detect pedestrians using a network of 65 Bluetooth detectors located in Tel-Aviv,Israel,which record on average over 60,000 unique daily Bluetooth Media-Access-Control addresses.We propose a detection methodology that includes system calibration,effective travel time calculation,and classification by velocity that takes into consideration the probability of vehicular traffic jams.An evaluation of the proposed methodology presents a promising pedestrian detection accuracy rate of 89%.We showcase the results of pedestrian traffic analysis,together with a discussion on the data analysis challenges and limitations.To the best of our knowledge,this work is the first to analyze pedestrian records detection from a Bluetooth network employed in a dynamic urban environment setting.
文摘为解决室内非视距(non line of sight,NLOS)环境以及低信标部署密度下传统定位算法精度急剧下降的问题,提出一种基于测距修正的低功耗蓝牙(bluetooth low energy,BLE)和行人航位推算(pedestrian dead reckoning,PDR)融合定位方法。通过SketchUp室内3D建模软件联合射线追踪算法实现BLE的接收信号强度(received signal strength,RSS)快速构建,避免人工繁琐的RSS实地采集。设计一种基于卷积神经网络的变分自编码器(variational autoencoder based on convolution neural network,VAE-CNN)模型对BLE测距误差进行预测和修正,提升BLE定位精度。采用扩展卡尔曼滤波(extended Kalman filter,EKF)融合BLE和PDR的定位结果。实验结果表明,采用测距修正后的BLE测距定位以及EKF融合定位在NLOS以及信标部署密度低的环境下具有较好的定位性能。
文摘Bluetooth技术可实现短距离、低功耗的无线通信和构成无线网络(Piconet and Scatternet),采用Bluetooth技术构建的数据采集无线网络系统(Data Acquisition Wireless Network System based on Bluetooth:DAWNS-B)便于进行集中测量和控制。介绍Bluetooth ASIC的结构特性以及DAWNS—B的结构原理、硬件电路接口和软件系统。DAWNS-B由前、后端单元构成,Bluetooth ASIC的HCI与PC计算机(或单片机)接口,Bluetooth协议软件栈控制Bluetooth ASIC对主机的HCI命令以及前、后端无线通信和网络通信,应用程序实现数据采集、处理DAWNS-B实现测控系统移动数据交换和无线网络功能,扩大和增强了数据采集系统的功能和应用场合。
