对于高时间分辨率的超宽带(ultra wideband,UWB)信号来说,在测距应用中主要通过估计信号到达时间(time of arrival,TOA)来计算距离。文章提出了一种基于动态阈值检测的TOA估计算法以提高测距精度并降低算法复杂度。对接收方收到的匹配...对于高时间分辨率的超宽带(ultra wideband,UWB)信号来说,在测距应用中主要通过估计信号到达时间(time of arrival,TOA)来计算距离。文章提出了一种基于动态阈值检测的TOA估计算法以提高测距精度并降低算法复杂度。对接收方收到的匹配滤波输出脉冲进行峰值检测,确定直达单径(direct path,DP)的检测区间;设定一个能够反映出信号和信道特性的联合度量参数,根据该参数的不同设置相应的最佳阈值因子,在检测区间中通过阈值检测搜索DP精确位置对应的时刻,得到TOA的估计值。仿真采用IEEE802.15.4a标准信道,其结果表明所提算法适用于不同信噪比和延时特性的信道,并兼顾运算复杂度与算法精度。展开更多
随着信息技术的发展和日益增长的位置服务需求,室内定位技术经成为了当下最热门的定位研究领域之一。在室内定位中比较常用的算法包括信号到达时间(Time of Arrival,TOA)、信号到达时间差(Time Difference of Arrival,TDOA)、信号到达角...随着信息技术的发展和日益增长的位置服务需求,室内定位技术经成为了当下最热门的定位研究领域之一。在室内定位中比较常用的算法包括信号到达时间(Time of Arrival,TOA)、信号到达时间差(Time Difference of Arrival,TDOA)、信号到达角(Angle of Arrival,AOA)等。这些定位技术都依赖于客户端与Wi-Fi接入点(Access Point,AP)处于可视环境(Line of Sight,LOS),当客户端与AP处于非可视环境(Non Line of Sight,NLOS)时,会造成定位精度严重下降。基于此,本文在传统的多信号分类算法(Multiple Signal Classification,MUSIC)的基础上结合OFDM信号的子载波特性,提出一种基于仿射传播聚类的LOS/NLOS环境识别算法用于提高基于AOA的室内定位的精度。该算法分为两步,先对无线信号的AOA和TOA进行估计,其次利用聚类算法对信号的AOA和TOA信息进行聚类,利用聚类的结果判断当前环境属于LOS环境还是NLOS环境。仿真结果表明,本文提出的算法具有良好的识别性能。展开更多
This paper presents a source localization algorithm based on the source signal's time-difference-of-arrival(TDOA) for asynchronous wireless sensor network.To obtain synchronization among anchors,all anchors broadc...This paper presents a source localization algorithm based on the source signal's time-difference-of-arrival(TDOA) for asynchronous wireless sensor network.To obtain synchronization among anchors,all anchors broadcast signals periodically,the clock offsets and skews of anchor pairs can be estimated using broadcasting signal's time-of-arrivals(TOA) at anchors.A kalman filter is adopted to improve the accuracy of clock offsets and track the clock drifts due to random fluctuations.Once the source transmits signal,the TOAs at anchors are stamped respectively and source's TDOA error due to clock offset and skew of anchor pair can be mitigated by a compensation operation.Based on a Gaussian noise model,maximum likelihood estimation(MLE) for the source position is obtained.Performance issues are addressed by evaluating the Cramer-Rao lower bound and the selection of broadcasting period.The proposed algorithm is simple and effective,which has close performance with synchronous TDOA algorithm.展开更多
文摘对于高时间分辨率的超宽带(ultra wideband,UWB)信号来说,在测距应用中主要通过估计信号到达时间(time of arrival,TOA)来计算距离。文章提出了一种基于动态阈值检测的TOA估计算法以提高测距精度并降低算法复杂度。对接收方收到的匹配滤波输出脉冲进行峰值检测,确定直达单径(direct path,DP)的检测区间;设定一个能够反映出信号和信道特性的联合度量参数,根据该参数的不同设置相应的最佳阈值因子,在检测区间中通过阈值检测搜索DP精确位置对应的时刻,得到TOA的估计值。仿真采用IEEE802.15.4a标准信道,其结果表明所提算法适用于不同信噪比和延时特性的信道,并兼顾运算复杂度与算法精度。
文摘随着信息技术的发展和日益增长的位置服务需求,室内定位技术经成为了当下最热门的定位研究领域之一。在室内定位中比较常用的算法包括信号到达时间(Time of Arrival,TOA)、信号到达时间差(Time Difference of Arrival,TDOA)、信号到达角(Angle of Arrival,AOA)等。这些定位技术都依赖于客户端与Wi-Fi接入点(Access Point,AP)处于可视环境(Line of Sight,LOS),当客户端与AP处于非可视环境(Non Line of Sight,NLOS)时,会造成定位精度严重下降。基于此,本文在传统的多信号分类算法(Multiple Signal Classification,MUSIC)的基础上结合OFDM信号的子载波特性,提出一种基于仿射传播聚类的LOS/NLOS环境识别算法用于提高基于AOA的室内定位的精度。该算法分为两步,先对无线信号的AOA和TOA进行估计,其次利用聚类算法对信号的AOA和TOA信息进行聚类,利用聚类的结果判断当前环境属于LOS环境还是NLOS环境。仿真结果表明,本文提出的算法具有良好的识别性能。
基金supported by the National Natural Science Foundation of China under Grant No.61571452 and No.61201331
文摘This paper presents a source localization algorithm based on the source signal's time-difference-of-arrival(TDOA) for asynchronous wireless sensor network.To obtain synchronization among anchors,all anchors broadcast signals periodically,the clock offsets and skews of anchor pairs can be estimated using broadcasting signal's time-of-arrivals(TOA) at anchors.A kalman filter is adopted to improve the accuracy of clock offsets and track the clock drifts due to random fluctuations.Once the source transmits signal,the TOAs at anchors are stamped respectively and source's TDOA error due to clock offset and skew of anchor pair can be mitigated by a compensation operation.Based on a Gaussian noise model,maximum likelihood estimation(MLE) for the source position is obtained.Performance issues are addressed by evaluating the Cramer-Rao lower bound and the selection of broadcasting period.The proposed algorithm is simple and effective,which has close performance with synchronous TDOA algorithm.
