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
The total cross-eye gain of multiple-element retrodirective cross-eye jamming(MRCJ) in the presence of the platform skin return is a distribution rather than a constant value, due to the random variation in the phase ...The total cross-eye gain of multiple-element retrodirective cross-eye jamming(MRCJ) in the presence of the platform skin return is a distribution rather than a constant value, due to the random variation in the phase of the skin return. Although the median value of the total cross-eye gain distribution had been analyzed in previous studies, the extreme values providing useful indications of the upper and lower bounds of the total cross-eye gain have not been analyzed until now. In this paper, the cumulative distribution function and the extreme values of the total cross-eye gain of MRCJ are derived. The angular error induced in threat monopulse radar as a figure of merit is used to analyze the performance of MRCJ system. Simulation results demonstrate the variation of the angular error and discuss the proper value of jamming-to-signal ratio(JSR) making the MRCJ system more effective in consideration of the whole distribution of the total cross-eye gain.展开更多
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。
基金supported by the Weapons and Equipment Research Foundation of China(304070102)
文摘The total cross-eye gain of multiple-element retrodirective cross-eye jamming(MRCJ) in the presence of the platform skin return is a distribution rather than a constant value, due to the random variation in the phase of the skin return. Although the median value of the total cross-eye gain distribution had been analyzed in previous studies, the extreme values providing useful indications of the upper and lower bounds of the total cross-eye gain have not been analyzed until now. In this paper, the cumulative distribution function and the extreme values of the total cross-eye gain of MRCJ are derived. The angular error induced in threat monopulse radar as a figure of merit is used to analyze the performance of MRCJ system. Simulation results demonstrate the variation of the angular error and discuss the proper value of jamming-to-signal ratio(JSR) making the MRCJ system more effective in consideration of the whole distribution of the total cross-eye gain.
