研究了一种基于时域射线追踪技术(Time Domain Ray Tracing)适用于大尺度室内环境传播建模的IR-UWB信号传播模型。该模型综合考虑室内传播存在的多径效应、阴影效应和穿墙效应等物理现象,引入墙体内部的时域传输系数和墙体至空气的时域...研究了一种基于时域射线追踪技术(Time Domain Ray Tracing)适用于大尺度室内环境传播建模的IR-UWB信号传播模型。该模型综合考虑室内传播存在的多径效应、阴影效应和穿墙效应等物理现象,引入墙体内部的时域传输系数和墙体至空气的时域透射系数,分析并解释了时域传播系数的物理意义。模型利用了IR-UWB信号时域极窄的特点,与传统的FDTD方法相比,能够显著提高大尺度环境下的计算效率。最后通过与实测结果的对比,验证了该模型的有效性,详细研究了模型的计算精度及误差成因。展开更多
人体呼吸系统相关疾病常常伴随着呼吸深度和节律的异常,因此呼吸信号监测和呼吸模式识别在医疗健康领域中尤其是对于睡眠监测、疾病预断具有重要意义。其中,非接触式的脉冲式超宽带雷达(Impulse Radio Ultra-Wideband,IR-UWB)因具有良...人体呼吸系统相关疾病常常伴随着呼吸深度和节律的异常,因此呼吸信号监测和呼吸模式识别在医疗健康领域中尤其是对于睡眠监测、疾病预断具有重要意义。其中,非接触式的脉冲式超宽带雷达(Impulse Radio Ultra-Wideband,IR-UWB)因具有良好的距离分辨率和穿透能力以及全天候全天时、安全无创的检测优势,正逐步成为睡眠健康监护领域中最关键的感知技术之一。然而受睡眠监测特定的室内场景影响,复杂的测量环境给呼吸模式特征的准确提取带来了限制和挑战,传统的雷达呼吸模式识别算法主要关注一维呼吸时、频域特征,而IR-UWB雷达目标回波信息分散在多个距离门内,使用一维特征识别准确率较低。为此,本文针对IR-UWB雷达中人体呼吸在时间上慢速起伏运动、在距离上是扩展目标的信号模型特点,提出了一种引入时距信息的IR-UWB雷达多域特征融合呼吸模式识别方法。算法在提取一维呼吸信号波形时、频域特征的基础上更进一步挖掘雷达二维时距图像中潜在的呼吸模式形态特征,通过多域特征融合实现呼吸模式的非接触式检测和识别。在图像处理上,针对图像受呼吸异常节律影响呈现局部粘连特性导致呼吸周期提取难的问题,提出一种通过相位矩阵图像处理来检测雷达图像中的呼吸时距条带从而获取图像特征的方法。实验结果表明,利用该算法提取的多域特征对六种呼吸模式进行机器学习的分类识别,可以实现96.3%的识别准确率。展开更多
针对无线传感器网络节点低成本、低运算能力的特点,基于脉冲超宽带技术的无线传感器网络提出了一种基于能量检测的两步测距法。这种方法针对DP(direct path)分量进行TOA(time of arrival)估计,具体包含对DP所在能量块的广义似然比检验...针对无线传感器网络节点低成本、低运算能力的特点,基于脉冲超宽带技术的无线传感器网络提出了一种基于能量检测的两步测距法。这种方法针对DP(direct path)分量进行TOA(time of arrival)估计,具体包含对DP所在能量块的广义似然比检验和能量块内对DP精确位置的极大似然估计两部分。给出了DP能量块检测概率和估计结果的闭合表达式,通过理论和数值分析了积分长度等系统参数对于TOA估计性能的影响,并建立了估计误差的数学模型。最后通过仿真结果进行了性能比较,并验证了分析结论。与传统方法比较的结果表明,该算法可以在复杂度较低的条件下取得一定的性能提升。展开更多
Ultra-wideband(UWB)through-wall radar has a wide range of applications in non-contact human information detection and monitoring.With the integration of machine learning technology,its potential prospects include the ...Ultra-wideband(UWB)through-wall radar has a wide range of applications in non-contact human information detection and monitoring.With the integration of machine learning technology,its potential prospects include the physiological monitoring of patients in the hospital environment and the daily monitoring at home.Although many target detection methods of UWB through-wall radar based on machine learning have been proposed,there is a lack of an opensource dataset to evaluate the performance of the algorithm.This published dataset is measured by impulse radio UWB(IR-UWB)through-wall radar system.Three test subjects are measured in different environments and several defined motion status.Using the presented dataset,we propose a human-motion-status recognition method using a convolutional neural network(CNN),and the detailed dataset partition method and the recognition process flow are given.On the well-trained network,the recognition accuracy of testing data for three kinds of motion status is higher than 99.7%.The dataset presented in this paper considers a simple environment.Therefore,we call on all organizations in the UWB radar field to cooperate to build opensource datasets to further promote the development of UWB through-wall radar.展开更多
文摘研究了一种基于时域射线追踪技术(Time Domain Ray Tracing)适用于大尺度室内环境传播建模的IR-UWB信号传播模型。该模型综合考虑室内传播存在的多径效应、阴影效应和穿墙效应等物理现象,引入墙体内部的时域传输系数和墙体至空气的时域透射系数,分析并解释了时域传播系数的物理意义。模型利用了IR-UWB信号时域极窄的特点,与传统的FDTD方法相比,能够显著提高大尺度环境下的计算效率。最后通过与实测结果的对比,验证了该模型的有效性,详细研究了模型的计算精度及误差成因。
文摘人体呼吸系统相关疾病常常伴随着呼吸深度和节律的异常,因此呼吸信号监测和呼吸模式识别在医疗健康领域中尤其是对于睡眠监测、疾病预断具有重要意义。其中,非接触式的脉冲式超宽带雷达(Impulse Radio Ultra-Wideband,IR-UWB)因具有良好的距离分辨率和穿透能力以及全天候全天时、安全无创的检测优势,正逐步成为睡眠健康监护领域中最关键的感知技术之一。然而受睡眠监测特定的室内场景影响,复杂的测量环境给呼吸模式特征的准确提取带来了限制和挑战,传统的雷达呼吸模式识别算法主要关注一维呼吸时、频域特征,而IR-UWB雷达目标回波信息分散在多个距离门内,使用一维特征识别准确率较低。为此,本文针对IR-UWB雷达中人体呼吸在时间上慢速起伏运动、在距离上是扩展目标的信号模型特点,提出了一种引入时距信息的IR-UWB雷达多域特征融合呼吸模式识别方法。算法在提取一维呼吸信号波形时、频域特征的基础上更进一步挖掘雷达二维时距图像中潜在的呼吸模式形态特征,通过多域特征融合实现呼吸模式的非接触式检测和识别。在图像处理上,针对图像受呼吸异常节律影响呈现局部粘连特性导致呼吸周期提取难的问题,提出一种通过相位矩阵图像处理来检测雷达图像中的呼吸时距条带从而获取图像特征的方法。实验结果表明,利用该算法提取的多域特征对六种呼吸模式进行机器学习的分类识别,可以实现96.3%的识别准确率。
文摘针对无线传感器网络节点低成本、低运算能力的特点,基于脉冲超宽带技术的无线传感器网络提出了一种基于能量检测的两步测距法。这种方法针对DP(direct path)分量进行TOA(time of arrival)估计,具体包含对DP所在能量块的广义似然比检验和能量块内对DP精确位置的极大似然估计两部分。给出了DP能量块检测概率和估计结果的闭合表达式,通过理论和数值分析了积分长度等系统参数对于TOA估计性能的影响,并建立了估计误差的数学模型。最后通过仿真结果进行了性能比较,并验证了分析结论。与传统方法比较的结果表明,该算法可以在复杂度较低的条件下取得一定的性能提升。
基金This work was supported by the National Key Research and Development Program of China(2018YFC0810202)the National Defence Pre-research Foundation of China(61404130119).
文摘Ultra-wideband(UWB)through-wall radar has a wide range of applications in non-contact human information detection and monitoring.With the integration of machine learning technology,its potential prospects include the physiological monitoring of patients in the hospital environment and the daily monitoring at home.Although many target detection methods of UWB through-wall radar based on machine learning have been proposed,there is a lack of an opensource dataset to evaluate the performance of the algorithm.This published dataset is measured by impulse radio UWB(IR-UWB)through-wall radar system.Three test subjects are measured in different environments and several defined motion status.Using the presented dataset,we propose a human-motion-status recognition method using a convolutional neural network(CNN),and the detailed dataset partition method and the recognition process flow are given.On the well-trained network,the recognition accuracy of testing data for three kinds of motion status is higher than 99.7%.The dataset presented in this paper considers a simple environment.Therefore,we call on all organizations in the UWB radar field to cooperate to build opensource datasets to further promote the development of UWB through-wall radar.
