针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统...针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统基于CSI幅值的指纹定位基础上增加相位信息对定位结果进行修正,之后对RSSI指纹和PC-CSI指纹的定位结果加权重定位。实验结果表明,提出的加权融合指纹定位算法与基于CSI的主动定位算法相比,平均定位误差(mean position error,MPE)降低了36.2%,能满足室内定位需求。展开更多
Due to the fine-grained communication scenarios characterization and stability,Wi-Fi channel state information(CSI)has been increasingly applied to indoor sensing tasks recently.Although spatial variations are explici...Due to the fine-grained communication scenarios characterization and stability,Wi-Fi channel state information(CSI)has been increasingly applied to indoor sensing tasks recently.Although spatial variations are explicitlyreflected in CSI measurements,the representation differences caused by small contextual changes are easilysubmerged in the fluctuations of multipath effects,especially in device-free Wi-Fi sensing.Most existing datasolutions cannot fully exploit the temporal,spatial,and frequency information carried by CSI,which results ininsufficient sensing resolution for indoor scenario changes.As a result,the well-liked machine learning(ML)-based CSI sensing models still struggling with stable performance.This paper formulates a time-frequency matrixon the premise of demonstrating that the CSI has low-rank potential and then proposes a distributed factorizationalgorithm to effectively separate the stable structured information and context fluctuations in the CSI matrix.Finally,a multidimensional tensor is generated by combining the time-frequency gradients of CSI,which containsrich and fine-grained real-time contextual information.Extensive evaluations and case studies highlight thesuperiority of the proposal.展开更多
随着无线通信技术的发展,智能超表面(Reconfigurable Intelligent Surface, RIS)辅助通感一体化(Integrated Sensing and Communication, ISAC)技术越来越受到关注。针对RIS辅助ISAC系统中基站获取瞬时信道状态信息(Channel State Infor...随着无线通信技术的发展,智能超表面(Reconfigurable Intelligent Surface, RIS)辅助通感一体化(Integrated Sensing and Communication, ISAC)技术越来越受到关注。针对RIS辅助ISAC系统中基站获取瞬时信道状态信息(Channel State Information, CSI)开销过大的问题,提出了一种利用统计CSI的联合波束成形设计方案。利用统计信道状态信息,分析了系统遍历速率的确定性等价式;在感知性能约束下,通过最大化该确定性等价式,提出了一种设计最优基站发送协方差矩阵和RIS对角相移矩阵的交替优化算法;仿真结果验证了所提方案的有效性,并揭示了通信与感知性能之间的折中关系。展开更多
针对传统多输入多输出(Multiple-Input Multiple-Output,MIMO)系统下信道反馈开销大、发送端的最优预编码设计难以实现的问题,研究有限信道状态信息(Channel State Information,CSI)反馈下的联合预编码设计与码字搜索技术,提出一种基于...针对传统多输入多输出(Multiple-Input Multiple-Output,MIMO)系统下信道反馈开销大、发送端的最优预编码设计难以实现的问题,研究有限信道状态信息(Channel State Information,CSI)反馈下的联合预编码设计与码字搜索技术,提出一种基于离散傅里叶变换(Discrete Fourier Transform,DFT)码本的快速码字搜索算法。该算法利用MIMO信道天然具有的信道硬化特性,将理论性能最优但是计算复杂度极高的遍历式码字搜索算法转化为求解多个简单优化问题的快速码字搜索算法。仿真结果显示,该算法能够在性能损失较小的情况下大幅度降低码字搜索的计算复杂度。展开更多
碘化铯(CsI)光阴极响应灵敏度是软X射线条纹相机用于X射线能谱定量诊断的重要参数,其理论计算具有重要指导意义.目前的理论解析模型基于薄膜光阴极产生次级电子的一维随机行走模型发展而来,具体包括X射线正入射、能量大于1 ke V条件下的...碘化铯(CsI)光阴极响应灵敏度是软X射线条纹相机用于X射线能谱定量诊断的重要参数,其理论计算具有重要指导意义.目前的理论解析模型基于薄膜光阴极产生次级电子的一维随机行走模型发展而来,具体包括X射线正入射、能量大于1 ke V条件下的Henke模型,以及变角度入射、光阴极厚度大于100 nm条件下的Fraser模型,都存在一定局限性.本文进一步引入次级电子输运概率的基础表达式,推导了CsI光阴极在更大参数范围内(X射线能量0.1—10 ke V、光阴极厚度10—200 nm)响应灵敏度随X射线能量E、光阴极厚度t、X射线与阴极表面夹角θ变化的一般表达式.最后,将本文的理论计算结果与Henke模型、Fraser模型、文献及北京同步辐射的实验数据分别进行了比较和讨论分析,验证了计算模型的准确性和普适性,并且为高时间分辨光谱定量测量实验中Cs I光阴极的优化设计提供了理论参考.展开更多
The THGEM detector without and with a CsI has been tested successfully. The optimal parameters of THGEM have been determined from eight samples. The UV photoelectric effect of the CsI photocathode is observed. The cha...The THGEM detector without and with a CsI has been tested successfully. The optimal parameters of THGEM have been determined from eight samples. The UV photoelectric effect of the CsI photocathode is observed. The changing tendency related to the extraction efficiency (εextr) versus the extraction electric field is measured, and several electric fields influencing the anode current are adjusted to adapt to the THGEM detector with a reflective CsI photocathode.展开更多
采用小样本学习技术设计了基于CSI的场景鲁棒性跌倒检测系统(FDFL,fall detection system based on few-shot learning)。现有基于Wi-Fi无线信道状态信息(CSI,channel state information)的跌倒检测方法跨场景应用性能退化明显,通常需...采用小样本学习技术设计了基于CSI的场景鲁棒性跌倒检测系统(FDFL,fall detection system based on few-shot learning)。现有基于Wi-Fi无线信道状态信息(CSI,channel state information)的跌倒检测方法跨场景应用性能退化明显,通常需要在每个应用场景采集并标记大量的CSI样本,给大规模部署造成极高的成本。为此,引入了小样本学习的方法,可以在陌生场景标注样本数量不足的情况下仍然保持高准确率的跌倒检测性能。所提FDFL主要分为源域的元训练和目标域的元学习两个阶段。源域的元训练阶段包含数据预处理和分类训练两个部分,数据预处理阶段将采集的原始CSI幅度和相位数据进行去噪、分段等操作;分类训练阶段利用大量处理好的源域数据样本训练一个基于卷积神经网络的CSI特征提取器。在目标域的元学习阶段,基于元训练模块中训练的特征提取器对目标域中采样的少量标注样本进行有效的特征提取,进而训练生成一个轻量型机器学习分类器对跨场景下的跌倒行为进行检测。通过多个不同场景下的实验,FDFL在只需要目标域少量样本下即可以实现对跌倒、坐着、步行、坐下的四分类任务达到95.52%的平均识别准确率,并且对测试环境、人员目标、设备位置等因素的变化保持鲁棒的检测准确性。展开更多
文摘针对基于RSSI和CSI的指纹定位技术易受环境干扰、定位精度较低的问题,提出了一种基于RSSI指纹和相位修正信道状态信息(phase correct based channel state information,PC-CSI)指纹的加权融合指纹定位技术。基于PC-CSI的指纹定位在传统基于CSI幅值的指纹定位基础上增加相位信息对定位结果进行修正,之后对RSSI指纹和PC-CSI指纹的定位结果加权重定位。实验结果表明,提出的加权融合指纹定位算法与基于CSI的主动定位算法相比,平均定位误差(mean position error,MPE)降低了36.2%,能满足室内定位需求。
基金the National Natural Science Foundation of China under Grant 61771258 and Grant U1804142the Key Science and Technology Project of Henan Province under Grants 202102210280,212102210159,222102210192,232102210051the Key Scientific Research Projects of Colleges and Universities in Henan Province under Grant 20B460008.
文摘Due to the fine-grained communication scenarios characterization and stability,Wi-Fi channel state information(CSI)has been increasingly applied to indoor sensing tasks recently.Although spatial variations are explicitlyreflected in CSI measurements,the representation differences caused by small contextual changes are easilysubmerged in the fluctuations of multipath effects,especially in device-free Wi-Fi sensing.Most existing datasolutions cannot fully exploit the temporal,spatial,and frequency information carried by CSI,which results ininsufficient sensing resolution for indoor scenario changes.As a result,the well-liked machine learning(ML)-based CSI sensing models still struggling with stable performance.This paper formulates a time-frequency matrixon the premise of demonstrating that the CSI has low-rank potential and then proposes a distributed factorizationalgorithm to effectively separate the stable structured information and context fluctuations in the CSI matrix.Finally,a multidimensional tensor is generated by combining the time-frequency gradients of CSI,which containsrich and fine-grained real-time contextual information.Extensive evaluations and case studies highlight thesuperiority of the proposal.
文摘碘化铯(CsI)光阴极响应灵敏度是软X射线条纹相机用于X射线能谱定量诊断的重要参数,其理论计算具有重要指导意义.目前的理论解析模型基于薄膜光阴极产生次级电子的一维随机行走模型发展而来,具体包括X射线正入射、能量大于1 ke V条件下的Henke模型,以及变角度入射、光阴极厚度大于100 nm条件下的Fraser模型,都存在一定局限性.本文进一步引入次级电子输运概率的基础表达式,推导了CsI光阴极在更大参数范围内(X射线能量0.1—10 ke V、光阴极厚度10—200 nm)响应灵敏度随X射线能量E、光阴极厚度t、X射线与阴极表面夹角θ变化的一般表达式.最后,将本文的理论计算结果与Henke模型、Fraser模型、文献及北京同步辐射的实验数据分别进行了比较和讨论分析,验证了计算模型的准确性和普适性,并且为高时间分辨光谱定量测量实验中Cs I光阴极的优化设计提供了理论参考.
基金Supported by National Natural Science Foundation of China (10775181,10775151)A. Breskin of Weizmann Inst., P. Picchi and A.Braem of CERN for their friendly discussions andkind supportthe NNSF of China for support of this work
文摘The THGEM detector without and with a CsI has been tested successfully. The optimal parameters of THGEM have been determined from eight samples. The UV photoelectric effect of the CsI photocathode is observed. The changing tendency related to the extraction efficiency (εextr) versus the extraction electric field is measured, and several electric fields influencing the anode current are adjusted to adapt to the THGEM detector with a reflective CsI photocathode.
文摘采用小样本学习技术设计了基于CSI的场景鲁棒性跌倒检测系统(FDFL,fall detection system based on few-shot learning)。现有基于Wi-Fi无线信道状态信息(CSI,channel state information)的跌倒检测方法跨场景应用性能退化明显,通常需要在每个应用场景采集并标记大量的CSI样本,给大规模部署造成极高的成本。为此,引入了小样本学习的方法,可以在陌生场景标注样本数量不足的情况下仍然保持高准确率的跌倒检测性能。所提FDFL主要分为源域的元训练和目标域的元学习两个阶段。源域的元训练阶段包含数据预处理和分类训练两个部分,数据预处理阶段将采集的原始CSI幅度和相位数据进行去噪、分段等操作;分类训练阶段利用大量处理好的源域数据样本训练一个基于卷积神经网络的CSI特征提取器。在目标域的元学习阶段,基于元训练模块中训练的特征提取器对目标域中采样的少量标注样本进行有效的特征提取,进而训练生成一个轻量型机器学习分类器对跨场景下的跌倒行为进行检测。通过多个不同场景下的实验,FDFL在只需要目标域少量样本下即可以实现对跌倒、坐着、步行、坐下的四分类任务达到95.52%的平均识别准确率,并且对测试环境、人员目标、设备位置等因素的变化保持鲁棒的检测准确性。
