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
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
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
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
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
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
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 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 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 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.
