多路径误差是全球卫星导航系统(global navigation satellite system,GNSS)精密数据处理中的主要误差源之一,非直射(non line of sight,NLOS)信号与多路径信号具有不同的信号特性,在厘米至毫米级别的GNSS定位中可能导致显著的误差。然...多路径误差是全球卫星导航系统(global navigation satellite system,GNSS)精密数据处理中的主要误差源之一,非直射(non line of sight,NLOS)信号与多路径信号具有不同的信号特性,在厘米至毫米级别的GNSS定位中可能导致显著的误差。然而目前针对载波相位观测值的多路径误差改正方法均未对两者进行有效区分。通过三维(3 dimensional,3D)点云数据检测静态测站处的NLOS载波信号,评估了半天球格网点模型(multi-point hemispherical grid model,MHGM)对多路径和NLOS信号的削弱效果,并在原有MHGM的基础上进一步提出剔除NLOS信号的多路径误差建模改进策略。实验中,模糊度固定时段内的双差残差统计显示,MHGM对多路径和NLOS误差分别有73.5%和81.2%的削弱,但MHGM改正后NLOS观测值的精度仍然显著低于多路径观测值。在将NLOS信号在多路径误差建模和应用阶段进行剔除之后,模糊度固定时段内载波相位双差观测值残差的均方根进一步降低,相比不剔除NLOS时提升了8.8%。动态定位测试结果表明,在多系统和可用卫星数量充足的情况下,MHGM对定位结果3D精度有68.6%的提升,而采用剔除NLOS信号的MHGM时,3D定位精度的改善率可以达到76.0%。展开更多
在城市复杂环境中,GNSS信号易发生折射、衍射、遮挡等现象,产生严重的多路径效应和非视距(none line of sight,NLOS)误差;特别是NLOS信号可能会导致数十、上百米的伪距和载波相位误差,严重影响复杂环境下的GNSS定位精度和稳定性。本文...在城市复杂环境中,GNSS信号易发生折射、衍射、遮挡等现象,产生严重的多路径效应和非视距(none line of sight,NLOS)误差;特别是NLOS信号可能会导致数十、上百米的伪距和载波相位误差,严重影响复杂环境下的GNSS定位精度和稳定性。本文重点分析了城市动态场景下GNSS NLOS信号在高度角、信噪比、伪距一致性、相位一致性、双差伪距残差和双差相位残差等6个方面的特征,并使用随机森林(random forests,RF)就信号特征对NLOS自主识别的影响加以初步分析。结果表明:80%以上的NLOS信号的高度角低于60度、信噪比低于45 dB-Hz;NLOS信号的伪距一致性和相位一致性会出现明显的离群现象,双差伪距残差和双差相位残差存在明显的大幅度波动。实时动态场景下的NLOS自主识别结果表明,高度角和信噪比对NLOS识别准确度的影响最大,伪距一致性和相位一致性次之;而双伪距残差和双差相位残差则是离线进行NLOS自主识别时的两个重要特征,仅使用高度角、信噪比、双差伪距残差、双差相位残差四个特征就能使基于随机森林的NLOS识别准确度达到80%左右。展开更多
针对室内环境中超宽带(Ultra-Wideband,UWB)信号易受障碍物遮挡导致非视距(Non Line of Sight,NLOS)误差的问题,本文提出了一种基于激光雷达(Light Detection And Ranging,LiDAR)点云识别UWB NLOS的融合定位方法,该方法利用LiDAR点云信...针对室内环境中超宽带(Ultra-Wideband,UWB)信号易受障碍物遮挡导致非视距(Non Line of Sight,NLOS)误差的问题,本文提出了一种基于激光雷达(Light Detection And Ranging,LiDAR)点云识别UWB NLOS的融合定位方法,该方法利用LiDAR点云信息辅助UWBNLOS识别,并通过UWB视距(LineofSight,LOS)测距值消除LiDAR同时定位与建图(Simultaneous Localization and Mapping,SLAM)过程中的累计误差,从而提高室内融合定位精度和鲁棒性。首先,采用八叉树对LiDAR点云进行处理,根据UWB基准站位置信息构建测距方向,并从LiDAR点云中提取测距方向上相关区域的点云数据。然后,通过3D Alpha Shape算法对所提取点云中可能阻碍UWB信号传播的障碍物进行轮廓提取。此外,根据分析提取的障碍物轮廓和UWB测距方向的空间关系,以此有效判定UWB信号是否存在NLOS测距情况。最后,剔除UWB测距过程中存在的NLOS测距值,通过紧组合方式,采用扩展卡尔曼滤波(EKF)将UWB LOS测距值和LiDAR SLAM的定位信息进行融合解算,消除LiDAR SLAM定位结果中的累积误差,以此提高融合定位精度和鲁棒性。为验证本文所提出的融合定位算法的有效性,通过搭建的融合定位实验平台在教学楼大厅进行了NLOS静态识别实验,在地下停车场进行了动态NLOS识别与动态定位实验。实验结果表明,该方法能够显著提高在室内复杂环境中的NLOS识别与定位的准确性,相较于单传感器定位与UWB原始测距值与LiDAR SLAM紧组合EKF的定位方法,NLOS识别准确率为93.22%,定位精度分别提高了49.24%、47.03%、96.13%,定位误差为0.067 m,实现了亚分米级室内定位。展开更多
Performance of non-line-of-sight(NLOS)ultraviolet(UV)communication is closely related with the communication range,system geometry and the atmosphere aerosol properties.In this paper,we investigate the path loss of th...Performance of non-line-of-sight(NLOS)ultraviolet(UV)communication is closely related with the communication range,system geometry and the atmosphere aerosol properties.In this paper,we investigate the path loss of the NLOS UV communication systems in both monodisperse and polydisperse aerosol systems based on the Monte-Carlo method.展开更多
Cell-free massive multiple-input multipleoutput(MIMO)is a promising technology for future wireless communications,where a large number of distributed access points(APs)simultaneously serve all users over the same time...Cell-free massive multiple-input multipleoutput(MIMO)is a promising technology for future wireless communications,where a large number of distributed access points(APs)simultaneously serve all users over the same time-frequency resources.Since users and APs may locate close to each other,the line-of-sight(Lo S)transmission occurs more frequently in cell-free massive MIMO systems.Hence,in this paper,we investigate the cell-free massive MIMO system with Lo S and non-line-of-sight(NLo S)transmissions,where APs and users are both distributed according to Poisson point process.Using tools from stochastic geometry,we derive a tight lower bound for the user downlink achievable rate and we further obtain the energy efficiency(EE)by considering the power consumption on downlink payload transmissions and circuitry dissipation.Based on the analysis,the optimal AP density and AP antenna number that maximize the EE are obtained.It is found that compared with the previous work that only considers NLo S transmissions,the actual optimal AP density should be much smaller,and the maximized EE is actually much higher.展开更多
The Ultra-Wideband(UWB)Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution.However,the None-Line-of-Sight(NLOS)propagation may reduce the ranging accur...The Ultra-Wideband(UWB)Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution.However,the None-Line-of-Sight(NLOS)propagation may reduce the ranging accuracy for UWB localization system in indoor environment.So it is important to identify LOS and NLOS propagations before taking proper measures to improve the UWB localization accuracy.In this paper,a deep learning-based UWB NLOS/LOS classification algorithm called FCN-Attention is proposed.The proposed FCN-Attention algorithm utilizes a Fully Convolution Network(FCN)for improving feature extraction ability and a self-attention mechanism for enhancing feature description from the data to improve the classification accuracy.The proposed algorithm is evaluated using an open-source dataset,a local collected dataset and a mixed dataset created from these two datasets.The experiment result shows that the proposed FCN-Attention algorithm achieves classification accuracy of 88.24%on the open-source dataset,100%on the local collected dataset and 92.01%on the mixed dataset,which is better than the results from other evaluated NLOS/LOS classification algorithms in most scenarios in this paper.展开更多
In the time-difference-of-arrival(TDOA)localization,robust least squares(LS)problems solved by mathematical programming were proven to be superior in mitigating the effects of non-line-of-sight(NLOS)propagation.Howeve...In the time-difference-of-arrival(TDOA)localization,robust least squares(LS)problems solved by mathematical programming were proven to be superior in mitigating the effects of non-line-of-sight(NLOS)propagation.However,the existing algorithms still suffer from two disadvantages:1)The algorithms strongly depend on prior information;2)The approaches do not satisfy the mean square error(MSE)optimal criterion of the measurement noise.To tackle the troubles,we first formulate an MSE minimization model for measurement noise by taking the source and the NLOS biases as variables.To obtain stable solutions,we introduce a penalty function to avoid abnormal estimates.We further tackle the nonconvex locating problem with semidefinite relaxation techniques.Finally,we incorporate mixed constraints and variable information to improve the estimation accuracy.Simulations and experiments show that the proposed method achieves consistent performance and good accuracy in dynamic NLOS environments.展开更多
Addressing the challenges of passive Radio Frequency Identification(RFID)indoor localization technology in Non-Line-of-Sight(NLoS)and multipath environments,this paper presents an innovative approach by introducing a ...Addressing the challenges of passive Radio Frequency Identification(RFID)indoor localization technology in Non-Line-of-Sight(NLoS)and multipath environments,this paper presents an innovative approach by introducing a combined technology integrating an improved Kalman Filter with Space Domain Phase Difference of Arrival(SD-PDOA)and Received Signal Strength Indicator(RSSI).This methodology utilizes the distinct channel characteristics in multipath and NLoS contexts to effectively filter out interference and accurately extract localization information,thereby facilitating high precision and stability in passive RFID localization.The efficacy of this approach is demonstrated through detailed simulations and empirical tests conducted on a custom-built experimental platform consisting of passive RFID tags and an R420 reader.The findings are significant:in NLoS conditions,the four-antenna localization system achieved a notable localization accuracy of 0.25 m at a distance of 5 m.In complex multipath environments,this system achieved a localization accuracy of approximately 0.5 m at a distance of 5 m.When compared to conventional passive localization methods,our proposed solution exhibits a substantial improvement in indoor localization accuracy under NLoS and multipath conditions.This research provides a robust and effective technical solution for high-precision passive indoor localization in the Internet of Things(IoT)system,marking a significant advancement in the field.展开更多
The external electric field is a special strategy for generating novel structures.In this work,the intermolecular bonding and nonlinear optical properties are affected by applying an applied electric field of 0 to 9.7...The external electric field is a special strategy for generating novel structures.In this work,the intermolecular bonding and nonlinear optical properties are affected by applying an applied electric field of 0 to 9.77 V·nm^(-1)along the bonding direction of dimer of olympicenyl radical(OLY2).The results showed that the distance between molecular layers was lengthened gradually,and the 2e/20c bonds were weakened or even completely broken(the critical electric field is 9.77 V·nm^(-1))with the increase of the external electric field strength.Moreover,the introduction of an external electric field caused significant changes in the electronic properties of the OLY2,which induced charge transfer between inter-layer monomers,and enhanced the interlayer electrostatic interactions.Therefore,the external electric field not only regulates the interlayer interactions of molecules,but also causes the symmetric molecules to produce strong polarity,producing a large NLO response in OLY2.展开更多
Visibility conditions between antennas,i.e.Line-of-Sight(LOS)and Non-Line-of-Sight(NLOS)can be crucial in the context of indoor localization,for which detecting the NLOS condition and further correcting constant posit...Visibility conditions between antennas,i.e.Line-of-Sight(LOS)and Non-Line-of-Sight(NLOS)can be crucial in the context of indoor localization,for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning.In this paper a Deep Learning(DL)model to classify LOS/NLOS condition while analyzing two Channel Impulse Response(CIR)parameters:Total Power(TP)[dBm]and First Path Power(FP)[dBm]is proposed.The experiments were conducted using DWM1000 DecaWave radio module based on measurements collected in a real indoor environment and the proposed architecture provides LOS/NLOS identification with an accuracy of more than 100%and 95%in static and dynamic senarios,respectively.The proposed model improves the classification rate by 2-5%compared to other Machine Learning(ML)methods proposed in the literature.展开更多
针对目前基于信道脉冲响应(Channel Impulse Response,CIR)的非视距(None Line of Sight,NLoS)/视距(Line of Sight,LoS)识别方法精度低、泛化能力差的问题,提出了一种多层卷积神经网络(Convolutional Neural Network,CNN)与通道注意力...针对目前基于信道脉冲响应(Channel Impulse Response,CIR)的非视距(None Line of Sight,NLoS)/视距(Line of Sight,LoS)识别方法精度低、泛化能力差的问题,提出了一种多层卷积神经网络(Convolutional Neural Network,CNN)与通道注意力模块(Channel Attention Module,CAM)相结合的NLoS/LoS识别方法。在多层CNN中嵌入CAM提取原始CIR的时域数据特征,利用全局平均池化层代替全连接层进行特征整合并分类输出。使用欧洲地平线2020计划项目eWINE公开的数据集进行不同结构模型和不同识别方法的对比实验,结果表明,所提出的CNN-CAM模型LoS和NLoS召回率分别达到了92.29%与87.71%,准确率达到了90.00%,F1分数达到了90.22%。与现有多种传统识别方法相比,均具有更好的识别效果。展开更多
文摘在城市复杂环境中,GNSS信号易发生折射、衍射、遮挡等现象,产生严重的多路径效应和非视距(none line of sight,NLOS)误差;特别是NLOS信号可能会导致数十、上百米的伪距和载波相位误差,严重影响复杂环境下的GNSS定位精度和稳定性。本文重点分析了城市动态场景下GNSS NLOS信号在高度角、信噪比、伪距一致性、相位一致性、双差伪距残差和双差相位残差等6个方面的特征,并使用随机森林(random forests,RF)就信号特征对NLOS自主识别的影响加以初步分析。结果表明:80%以上的NLOS信号的高度角低于60度、信噪比低于45 dB-Hz;NLOS信号的伪距一致性和相位一致性会出现明显的离群现象,双差伪距残差和双差相位残差存在明显的大幅度波动。实时动态场景下的NLOS自主识别结果表明,高度角和信噪比对NLOS识别准确度的影响最大,伪距一致性和相位一致性次之;而双伪距残差和双差相位残差则是离线进行NLOS自主识别时的两个重要特征,仅使用高度角、信噪比、双差伪距残差、双差相位残差四个特征就能使基于随机森林的NLOS识别准确度达到80%左右。
文摘针对室内环境中超宽带(Ultra-Wideband,UWB)信号易受障碍物遮挡导致非视距(Non Line of Sight,NLOS)误差的问题,本文提出了一种基于激光雷达(Light Detection And Ranging,LiDAR)点云识别UWB NLOS的融合定位方法,该方法利用LiDAR点云信息辅助UWBNLOS识别,并通过UWB视距(LineofSight,LOS)测距值消除LiDAR同时定位与建图(Simultaneous Localization and Mapping,SLAM)过程中的累计误差,从而提高室内融合定位精度和鲁棒性。首先,采用八叉树对LiDAR点云进行处理,根据UWB基准站位置信息构建测距方向,并从LiDAR点云中提取测距方向上相关区域的点云数据。然后,通过3D Alpha Shape算法对所提取点云中可能阻碍UWB信号传播的障碍物进行轮廓提取。此外,根据分析提取的障碍物轮廓和UWB测距方向的空间关系,以此有效判定UWB信号是否存在NLOS测距情况。最后,剔除UWB测距过程中存在的NLOS测距值,通过紧组合方式,采用扩展卡尔曼滤波(EKF)将UWB LOS测距值和LiDAR SLAM的定位信息进行融合解算,消除LiDAR SLAM定位结果中的累积误差,以此提高融合定位精度和鲁棒性。为验证本文所提出的融合定位算法的有效性,通过搭建的融合定位实验平台在教学楼大厅进行了NLOS静态识别实验,在地下停车场进行了动态NLOS识别与动态定位实验。实验结果表明,该方法能够显著提高在室内复杂环境中的NLOS识别与定位的准确性,相较于单传感器定位与UWB原始测距值与LiDAR SLAM紧组合EKF的定位方法,NLOS识别准确率为93.22%,定位精度分别提高了49.24%、47.03%、96.13%,定位误差为0.067 m,实现了亚分米级室内定位。
基金supported in part by the National Natural Science Foundation of China(No.U1833111)。
文摘Performance of non-line-of-sight(NLOS)ultraviolet(UV)communication is closely related with the communication range,system geometry and the atmosphere aerosol properties.In this paper,we investigate the path loss of the NLOS UV communication systems in both monodisperse and polydisperse aerosol systems based on the Monte-Carlo method.
基金supported in part by the National Natural Science Foundation of China under Grant 62171231in part by the Jiangsu Provincial Key Research and Development Program(No.BE2020084-1)。
文摘Cell-free massive multiple-input multipleoutput(MIMO)is a promising technology for future wireless communications,where a large number of distributed access points(APs)simultaneously serve all users over the same time-frequency resources.Since users and APs may locate close to each other,the line-of-sight(Lo S)transmission occurs more frequently in cell-free massive MIMO systems.Hence,in this paper,we investigate the cell-free massive MIMO system with Lo S and non-line-of-sight(NLo S)transmissions,where APs and users are both distributed according to Poisson point process.Using tools from stochastic geometry,we derive a tight lower bound for the user downlink achievable rate and we further obtain the energy efficiency(EE)by considering the power consumption on downlink payload transmissions and circuitry dissipation.Based on the analysis,the optimal AP density and AP antenna number that maximize the EE are obtained.It is found that compared with the previous work that only considers NLo S transmissions,the actual optimal AP density should be much smaller,and the maximized EE is actually much higher.
基金supported by the National Key Research and Development Program of China[grant No.2016YF B0502200]the Postdoctoral Research Foundation of China[grant No.2020M682480]the Fundamental Research Funds for the Central Universities[grant No.2042021kf0009]。
文摘The Ultra-Wideband(UWB)Location-Based Service is receiving more and more attention due to its high ranging accuracy and good time resolution.However,the None-Line-of-Sight(NLOS)propagation may reduce the ranging accuracy for UWB localization system in indoor environment.So it is important to identify LOS and NLOS propagations before taking proper measures to improve the UWB localization accuracy.In this paper,a deep learning-based UWB NLOS/LOS classification algorithm called FCN-Attention is proposed.The proposed FCN-Attention algorithm utilizes a Fully Convolution Network(FCN)for improving feature extraction ability and a self-attention mechanism for enhancing feature description from the data to improve the classification accuracy.The proposed algorithm is evaluated using an open-source dataset,a local collected dataset and a mixed dataset created from these two datasets.The experiment result shows that the proposed FCN-Attention algorithm achieves classification accuracy of 88.24%on the open-source dataset,100%on the local collected dataset and 92.01%on the mixed dataset,which is better than the results from other evaluated NLOS/LOS classification algorithms in most scenarios in this paper.
基金supported by the National Natural Science Foundation of China under Grant No.62101370。
文摘In the time-difference-of-arrival(TDOA)localization,robust least squares(LS)problems solved by mathematical programming were proven to be superior in mitigating the effects of non-line-of-sight(NLOS)propagation.However,the existing algorithms still suffer from two disadvantages:1)The algorithms strongly depend on prior information;2)The approaches do not satisfy the mean square error(MSE)optimal criterion of the measurement noise.To tackle the troubles,we first formulate an MSE minimization model for measurement noise by taking the source and the NLOS biases as variables.To obtain stable solutions,we introduce a penalty function to avoid abnormal estimates.We further tackle the nonconvex locating problem with semidefinite relaxation techniques.Finally,we incorporate mixed constraints and variable information to improve the estimation accuracy.Simulations and experiments show that the proposed method achieves consistent performance and good accuracy in dynamic NLOS environments.
基金supported in part by the Joint Project of National Natural Science Foundation of China(U22B2004,62371106)in part by China Mobile Research Institute&X-NET(Project Number:2022H002)+6 种基金in part by the Pre-Research Project(31513070501)in part by National Key R&D Program(2018AAA0103203)in part by Guangdong Provincial Research and Development Plan in Key Areas(2019B010141001)in part by Sichuan Provincial Science and Technology Planning Program of China(2022YFG0230,2023YFG0040)in part by the Fundamental Enhancement Program Technology Area Fund(2021-JCJQ-JJ-0667)in part by the Joint Fund of ZF and Ministry of Education(8091B022126)in part by Innovation Ability Construction Project for Sichuan Provincial Engineering Research Center of Communication Technology for Intelligent IoT(2303-510109-04-03-318020).
文摘Addressing the challenges of passive Radio Frequency Identification(RFID)indoor localization technology in Non-Line-of-Sight(NLoS)and multipath environments,this paper presents an innovative approach by introducing a combined technology integrating an improved Kalman Filter with Space Domain Phase Difference of Arrival(SD-PDOA)and Received Signal Strength Indicator(RSSI).This methodology utilizes the distinct channel characteristics in multipath and NLoS contexts to effectively filter out interference and accurately extract localization information,thereby facilitating high precision and stability in passive RFID localization.The efficacy of this approach is demonstrated through detailed simulations and empirical tests conducted on a custom-built experimental platform consisting of passive RFID tags and an R420 reader.The findings are significant:in NLoS conditions,the four-antenna localization system achieved a notable localization accuracy of 0.25 m at a distance of 5 m.In complex multipath environments,this system achieved a localization accuracy of approximately 0.5 m at a distance of 5 m.When compared to conventional passive localization methods,our proposed solution exhibits a substantial improvement in indoor localization accuracy under NLoS and multipath conditions.This research provides a robust and effective technical solution for high-precision passive indoor localization in the Internet of Things(IoT)system,marking a significant advancement in the field.
基金Foundation of Science and Technology Department of Jilin Province(20230101043JC).
文摘The external electric field is a special strategy for generating novel structures.In this work,the intermolecular bonding and nonlinear optical properties are affected by applying an applied electric field of 0 to 9.77 V·nm^(-1)along the bonding direction of dimer of olympicenyl radical(OLY2).The results showed that the distance between molecular layers was lengthened gradually,and the 2e/20c bonds were weakened or even completely broken(the critical electric field is 9.77 V·nm^(-1))with the increase of the external electric field strength.Moreover,the introduction of an external electric field caused significant changes in the electronic properties of the OLY2,which induced charge transfer between inter-layer monomers,and enhanced the interlayer electrostatic interactions.Therefore,the external electric field not only regulates the interlayer interactions of molecules,but also causes the symmetric molecules to produce strong polarity,producing a large NLO response in OLY2.
基金supported under ministry subsidy for research for Gdansk University of Technology。
文摘Visibility conditions between antennas,i.e.Line-of-Sight(LOS)and Non-Line-of-Sight(NLOS)can be crucial in the context of indoor localization,for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning.In this paper a Deep Learning(DL)model to classify LOS/NLOS condition while analyzing two Channel Impulse Response(CIR)parameters:Total Power(TP)[dBm]and First Path Power(FP)[dBm]is proposed.The experiments were conducted using DWM1000 DecaWave radio module based on measurements collected in a real indoor environment and the proposed architecture provides LOS/NLOS identification with an accuracy of more than 100%and 95%in static and dynamic senarios,respectively.The proposed model improves the classification rate by 2-5%compared to other Machine Learning(ML)methods proposed in the literature.
文摘针对目前基于信道脉冲响应(Channel Impulse Response,CIR)的非视距(None Line of Sight,NLoS)/视距(Line of Sight,LoS)识别方法精度低、泛化能力差的问题,提出了一种多层卷积神经网络(Convolutional Neural Network,CNN)与通道注意力模块(Channel Attention Module,CAM)相结合的NLoS/LoS识别方法。在多层CNN中嵌入CAM提取原始CIR的时域数据特征,利用全局平均池化层代替全连接层进行特征整合并分类输出。使用欧洲地平线2020计划项目eWINE公开的数据集进行不同结构模型和不同识别方法的对比实验,结果表明,所提出的CNN-CAM模型LoS和NLoS召回率分别达到了92.29%与87.71%,准确率达到了90.00%,F1分数达到了90.22%。与现有多种传统识别方法相比,均具有更好的识别效果。
