发帖
雷达卡
摘要翻译：
基于接收信号强度的呼吸频率监测作为一种替代性非接触技术正在兴起。这些系统利用短程商品无线设备的无线电测量，这些测量因人的吸入和呼出运动而变化。使用这种测量的呼吸速率估计的成功取决于信噪比，信噪比随人的特性和测量系统的变化而变化。到目前为止，还没有一个模型允许评估不同的部署或系统配置，以成功地估计呼吸率。本文介绍了一种用于呼吸速率监测的接收信号强度模型。结果表明，线性尺度和对数尺度的测量具有相同的函数形式，在这两种情况下可以使用相同的估计技术。在不同的信噪比条件下，用三种估计器：批频率估计器、递归贝叶斯估计器和基于模型的估计器的性能验证了该模型的含义。结果表明，在不同的信噪比条件下，不同的估计器是有利的。
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英文标题：
《RSS Models for Respiration Rate Monitoring》
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作者：
H\"useyin Yi\u{g}itler, Ossi Kaltiokallio, Roland Hostettler, Riku
J\"antti, Neal Patwari, and Simo S\"arkk\"a
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最新提交年份：
2017
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
Received signal strength based respiration rate monitoring is emerging as an alternative non-contact technology. These systems make use of the radio measurements of short-range commodity wireless devices, which vary due to the inhalation and exhalation motion of a person. The success of respiration rate estimation using such measurements depends on the signal-to-noise ratio, which alters with properties of the person and with the measurement system. To date, no model has been presented that allows evaluation of different deployments or system configurations for successful breathing rate estimation. In this paper, a received signal strength model for respiration rate monitoring is introduced. It is shown that measurements in linear and logarithmic scale have the same functional form, and the same estimation techniques can be used in both cases. The implications of the model are validated under varying signal-to-noise ratio conditions using the performances of three estimators: batch frequency estimator, recursive Bayesian estimator, and model-based estimator. The results are in coherence with the findings, and they imply that different estimators are advantageous in different signal-to-noise ratio regimes.
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PDF链接：
https://arxiv.org/pdf/1711.09444
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