Doppler Radar Techniques for Distinct Respiratory Pattern Recognition and Subject Identification
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2017-08
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University of Hawaii at Manoa
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Stationary continuous wave Doppler radar has been used for displacement measurement and vital signs detection in many state-of-the-art studies. However, further improvement, i.e., accurate radar characterization of respiration, may allow sleep diagnostics and unique subject identification. A low distortion DC coupled system with high signal to noise ratio is required for such chacterization and classification, which is especially critical with small signals, as with through wall measurements which suffer from poor signal to noise ration (SNR). This thesis proposes techniques to improve the signal to noise ratio by DC offset management and using the method of zooming in on fractions of the respiratory cycle waveform. A month-long study of six human subjects was performed and the developed Doppler radar system with classification algorithms has shown promising results in unique identification (vital signs based fingerprint) with more than 90% accuracy. Measurement time sample can be as low as 30 seconds. Neural networks, minimum distance classifiers, and majority vote algorithms were fused on multi-feature spaces to make classification decisions. Training and testing were performed on the extracted features such as variation in their breathing energy, frequency, and patterns captured by the radar. The system has shown the advantages of non-contact unique-identification where camera-based systems are not preferred or are incapable. This study also has an impact on radar-based breathing pattern classification for health diagnostics. This research also investigates the poor SNR problem associated with mobile platform as measurements, which become challenging due to motion artifacts induced by the platform. To implement a feasible field applicable solution, low intermediate frequency (IF) techniques for non-invasive detection of vital signs from a mobile short-range Doppler radar platform were proposed and validated through mechanical and human experiments. A low IF radar architecture using RF tags is employed to extract desired vital signs motion information even in the presence of large platform motion. Upon researching SNR improvements and developing algorithms, this research took one step further for unique identification of human subjects behind the walls. Many potential applications for such technology, such as security, health monitoring, IoT, virtual reality, and health diagnostics, will benefit from such ubiquitous monitoring, leading to have benefits for human society and sustainable existence of humans and nature.
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Radio frequency identification systems, Doppler radar, Respiration--Measurement, Pattern recognition systems
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