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Fast selection of time-interleaved samples for wireless healthcare monitoring with pulse radar

机译:通过脉冲雷达快速选择时间交错的样本进行无线医疗监控

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In this paper, we exploit using ultra-wide band(UWB) pulse radar for non-contact healthcare monitoring such as respiration or heartbeats rates. The transmitter sends periodic Gaussian pulses towards the person under monitoring. The pulses arrive at the person and reflect back to the receiver. The receiver samples the reflected signal in RF domain directly by time-interleaved sampling. The 16 time-interleaved analog-to-digital converters (ADC) lead to equivalent sampling rate of 20-GSamples/s. The respiration and heartbeats information are captured in the reflected signal and processed by the system. We have developed a successive interference cancelation algorithm at the receiver to post-process the signals and extract the respiration rate and the heartbeats rate successively. Furthermore, instead of taking all the received samples, a low complexity scheme is developed to select from the 16 interleaved ADC subchannels. We find that only a subset of the 16 interleaved samples are sufficient to maximize the signal-to-noise ratio. Also,the proposed scheme does not require prior knowledge of the pulse waveform. The performance evaluation based on the mean square error (MSE) of estimated heartbeats rate is also presented.
机译:在本文中,我们利用超宽带(UWB)脉冲雷达进行非接触式医疗保健监测,例如呼吸或心跳率。发射机向被监视的人发送周期性的高斯脉冲。脉冲到达人并反射回接收器。接收机通过时间交错采样直接在RF域中采样反射信号。 16个时间交错的模数转换器(ADC)导致等效采样率为20-GSamples / s。呼吸和心跳信息在反射信号中捕获并由系统处理。我们在接收机处开发了一种连续的干扰消除算法,对信号进行后处理,并依次提取呼吸频率和心跳频率。此外,代替获取所有接收的样本,开发了一种低复杂度的方案以从16个交错的ADC子通道中进行选择。我们发现16个交错样本中只有一个子集足以最大化信噪比。而且,所提出的方案不需要脉搏波形的先验知识。还提出了基于估计心跳率的均方误差(MSE)的性能评估。

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