多径干扰是超宽带(ultra-wideband,UWB)定位误差的主要来源之一,超宽带信号的非视距传播会导致通信和定位精度的可靠性降低。因此,准确识别定位过程中的非视距(non line of sight,NLOS)传播信号是提高定位精度的重要措施。针对超宽带信...多径干扰是超宽带(ultra-wideband,UWB)定位误差的主要来源之一,超宽带信号的非视距传播会导致通信和定位精度的可靠性降低。因此,准确识别定位过程中的非视距(non line of sight,NLOS)传播信号是提高定位精度的重要措施。针对超宽带信号的非视距传播识别问题,该文提出一种新的基于信道冲激响应(channel impulse response,CIR)特征参量—上升时间与峰值时间和(sum of rise time and peak time,Sum_T)与未检测到峰值(undetected peak,UD-P)联合的NLOS识别方法。实验结果表明,典型室内办公环境下NLOS信号的识别率可以达到95.75%,该方法在定位系统中的使用将有助于提升定位精度。展开更多
超宽带(Ultra Wide Band,UWB)信号具有极高的时间分辨率,测距精度高、穿透能力强、抗多径效应好,适用于高精度室内定位系统的设计。基于UWB的室内定位系统中,非视距传播对定位精度有重要影响。针对UWB信号室内传播存在视距(Line of Sigh...超宽带(Ultra Wide Band,UWB)信号具有极高的时间分辨率,测距精度高、穿透能力强、抗多径效应好,适用于高精度室内定位系统的设计。基于UWB的室内定位系统中,非视距传播对定位精度有重要影响。针对UWB信号室内传播存在视距(Line of Sight,LOS)和非视距(Non Line of Sight,NLOS)情形,采用卷积神经网络(Convolutional Neural Network,CNN)识别NLOS信号。为避免有限数据集导致过拟合,采用dropout降低神经元之间的依赖,提高NLOS信号识别率。完成NLOS识别后使用LS/WLS算法做定位,结果表明该方法能将定位误差降低一半,显著提高了定位精度。展开更多
This paper considers the non-line-of-sight(NLOS)vehicle localization problem by using millimeter-wave(MMW)automotive radar.Several preliminary attempts for NLOS vehicle detection are carried out and achieve good resul...This paper considers the non-line-of-sight(NLOS)vehicle localization problem by using millimeter-wave(MMW)automotive radar.Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results.Firstly,an electromagnetic(EM)wave NLOS multipath propagation model for vehicle scene is established.Subsequently,with the help of available multipath echoes,a complete NLOS vehicle localiza-tion algorithm is proposed.Finally,simulation and experimental results validate the effectiveness of the established EM wave propagation model and the proposed NLOS vehicle localization algorithm.展开更多
近年来,室内定位技术得到了广泛的应用,超宽带(Ultra-wideband,UWB)凭借其独特的优势,在室内定位领域脱颖而出。但是在复杂的室内环境中,信号传播易受到非视距(Non Line of Sight,NLOS)障碍物遮挡,产生NLOS误差。针对传统NLOS障碍物识...近年来,室内定位技术得到了广泛的应用,超宽带(Ultra-wideband,UWB)凭借其独特的优势,在室内定位领域脱颖而出。但是在复杂的室内环境中,信号传播易受到非视距(Non Line of Sight,NLOS)障碍物遮挡,产生NLOS误差。针对传统NLOS障碍物识别方法,识别率低以及需要具体考虑应用场景等问题,提出了一种基于PCA-FCM的模糊聚类的识别方法,达到92%的识别率。首先,根据视距(Line of Sight,LOS)信号与非视距信号之间的差异,选取了信号强度、平均过量时延等6个信道特征参数;其次,鉴于主成分分析(Principal Components Analysis,PCA)模型在特征提取方面的优势,用PCA模型对特征参数进行降维处理;最后,用模糊C均值(Fuzzy C-Means,FCM)算法对降维之后的目标函数进行优化,得到聚类中心的隶属度,从而提高NLOS信号识别率。展开更多
This paper proposes a robust vector tracking loop structure based on potential bias analysis. The influence of four kinds of biases on the existing two implementations of Vector Tracking Loops(VTLs) is illustrated by ...This paper proposes a robust vector tracking loop structure based on potential bias analysis. The influence of four kinds of biases on the existing two implementations of Vector Tracking Loops(VTLs) is illustrated by theoretical analysis and numerical simulations, and the following findings are obtained. Firstly, the initial user state bias leads to steady navigation solution bias in the relative VTL, while new measurements can eliminate it in the absolute VTL. Secondly, the initial code phase bias is transferred to the following navigation solutions in the relative VTL, while new measurements can eliminate it in the absolute VTL. Thirdly, the user state bias induced by erroneous navigation solution of VTLs can be eliminated by both of the two VTLs. Fourthly,the multipath/NLOS likely affects the two VTLs, and the induced tracking bias in the duration of the multipath/NLOS would decrease the performance of VTLs. Based on the above analysis,a robust VTL structure is proposed, where the absolute VTL is selected for its robustness to the two kinds of initialization biases;meanwhile, the instant bias detection and correction method is used to improve the performance of VTLs in the duration of the multipath/NLOS. Numerical simulations and experimental results verify the effectiveness of the proposed robust VTL structure.展开更多
Non-line-of-sight(NLOS)multipath effect is the main factor that restricts the application of global navigation satellite system(GNSS)in complex environments,especially in urban canyon.The effective avoidance of NLOS s...Non-line-of-sight(NLOS)multipath effect is the main factor that restricts the application of global navigation satellite system(GNSS)in complex environments,especially in urban canyon.The effective avoidance of NLOS signals can significantly improve the positioning performance of GNSS receiver.In this paper,an NLOS/LOS classification model based on recurrent neural network is proposed to classify satellite signals received in urban canyon environments.The accuracy of classification is 91%,and the recognition rate of NLOS is 89%;the classification performance is better than that of traditional machine learning classification models such as support vector machine.For BeiDou navigation satellite system/global positioning system(BDS/GPS)fusion system,the least square algorithm and extended Kalman filter are used to estimate the position.The experimental results show that the three-dimensional positioning accuracy after NLOS recognition is improved about 60%on average compared with the traditional methods,and the positioning stability is also improved significantly.展开更多
文摘多径干扰是超宽带(ultra-wideband,UWB)定位误差的主要来源之一,超宽带信号的非视距传播会导致通信和定位精度的可靠性降低。因此,准确识别定位过程中的非视距(non line of sight,NLOS)传播信号是提高定位精度的重要措施。针对超宽带信号的非视距传播识别问题,该文提出一种新的基于信道冲激响应(channel impulse response,CIR)特征参量—上升时间与峰值时间和(sum of rise time and peak time,Sum_T)与未检测到峰值(undetected peak,UD-P)联合的NLOS识别方法。实验结果表明,典型室内办公环境下NLOS信号的识别率可以达到95.75%,该方法在定位系统中的使用将有助于提升定位精度。
文摘超宽带(Ultra Wide Band,UWB)信号具有极高的时间分辨率,测距精度高、穿透能力强、抗多径效应好,适用于高精度室内定位系统的设计。基于UWB的室内定位系统中,非视距传播对定位精度有重要影响。针对UWB信号室内传播存在视距(Line of Sight,LOS)和非视距(Non Line of Sight,NLOS)情形,采用卷积神经网络(Convolutional Neural Network,CNN)识别NLOS信号。为避免有限数据集导致过拟合,采用dropout降低神经元之间的依赖,提高NLOS信号识别率。完成NLOS识别后使用LS/WLS算法做定位,结果表明该方法能将定位误差降低一半,显著提高了定位精度。
基金supported by the National Natural Science Foundation of China(62201510,62001091,61801435,61871080,61801435)the Initial Scientific Research Foundation of University of Science and Technology of China(Y030202059018051)+2 种基金Yangtze River Scholar Program,Sichuan Science and Technology Program(2019JDJQ0014)111 Project(B17008)Henan Provincial Department of Science and Technology Research Project(202102210315,212102210029,202102210-137).
文摘This paper considers the non-line-of-sight(NLOS)vehicle localization problem by using millimeter-wave(MMW)automotive radar.Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results.Firstly,an electromagnetic(EM)wave NLOS multipath propagation model for vehicle scene is established.Subsequently,with the help of available multipath echoes,a complete NLOS vehicle localiza-tion algorithm is proposed.Finally,simulation and experimental results validate the effectiveness of the established EM wave propagation model and the proposed NLOS vehicle localization algorithm.
文摘近年来,室内定位技术得到了广泛的应用,超宽带(Ultra-wideband,UWB)凭借其独特的优势,在室内定位领域脱颖而出。但是在复杂的室内环境中,信号传播易受到非视距(Non Line of Sight,NLOS)障碍物遮挡,产生NLOS误差。针对传统NLOS障碍物识别方法,识别率低以及需要具体考虑应用场景等问题,提出了一种基于PCA-FCM的模糊聚类的识别方法,达到92%的识别率。首先,根据视距(Line of Sight,LOS)信号与非视距信号之间的差异,选取了信号强度、平均过量时延等6个信道特征参数;其次,鉴于主成分分析(Principal Components Analysis,PCA)模型在特征提取方面的优势,用PCA模型对特征参数进行降维处理;最后,用模糊C均值(Fuzzy C-Means,FCM)算法对降维之后的目标函数进行优化,得到聚类中心的隶属度,从而提高NLOS信号识别率。
基金co-supported by the Scientific Research Program of Tianjin Municipal Education Commission, China (No. 2021KJ042)the Special Project of the National Science Foundation of China (No. U2133204)。
文摘This paper proposes a robust vector tracking loop structure based on potential bias analysis. The influence of four kinds of biases on the existing two implementations of Vector Tracking Loops(VTLs) is illustrated by theoretical analysis and numerical simulations, and the following findings are obtained. Firstly, the initial user state bias leads to steady navigation solution bias in the relative VTL, while new measurements can eliminate it in the absolute VTL. Secondly, the initial code phase bias is transferred to the following navigation solutions in the relative VTL, while new measurements can eliminate it in the absolute VTL. Thirdly, the user state bias induced by erroneous navigation solution of VTLs can be eliminated by both of the two VTLs. Fourthly,the multipath/NLOS likely affects the two VTLs, and the induced tracking bias in the duration of the multipath/NLOS would decrease the performance of VTLs. Based on the above analysis,a robust VTL structure is proposed, where the absolute VTL is selected for its robustness to the two kinds of initialization biases;meanwhile, the instant bias detection and correction method is used to improve the performance of VTLs in the duration of the multipath/NLOS. Numerical simulations and experimental results verify the effectiveness of the proposed robust VTL structure.
文摘Non-line-of-sight(NLOS)multipath effect is the main factor that restricts the application of global navigation satellite system(GNSS)in complex environments,especially in urban canyon.The effective avoidance of NLOS signals can significantly improve the positioning performance of GNSS receiver.In this paper,an NLOS/LOS classification model based on recurrent neural network is proposed to classify satellite signals received in urban canyon environments.The accuracy of classification is 91%,and the recognition rate of NLOS is 89%;the classification performance is better than that of traditional machine learning classification models such as support vector machine.For BeiDou navigation satellite system/global positioning system(BDS/GPS)fusion system,the least square algorithm and extended Kalman filter are used to estimate the position.The experimental results show that the three-dimensional positioning accuracy after NLOS recognition is improved about 60%on average compared with the traditional methods,and the positioning stability is also improved significantly.
