High Dynamic-Range Motion Imaging Based on Linearized Doppler Radar Sensor

Lv, Qinyi; Ye, Dexin; Qiao, Shan; Salamin, Yannick; Huangfu, Jiangtao; Li, Changzhi; Ran, Lixin

doi:10.1109/tmtt.2014.2342663

Cited by 81 publications

(18 citation statements)

References 30 publications

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“…To build the Doppler radar sensor quickly, an instrument-based Doppler radar sensor was realized by utilizing general-purpose RF and communication instruments that are widely used and To show whether the value of M in Equation (12) affected the extracted accuracy of the RR and HR, Figure 5 shows the extracted vital sign results with different values of M using the N-DCT under the third-order polynomial fitting. It can be seen from Figure 5 that when the value of M was too small, i.e., M ≤ 2 11 , the accuracy of the extracted results was degraded.…”

Section: Methodsmentioning

confidence: 99%

“…Although great theoretical and technological efforts have been made in the past few decades [11][12][13][14][15], such as the use of machine learning algorithms to improve the detection accuracy of HR [15], Sensors 2020, 20, 4183 2 of 17 this detection method still has not been widely applied. This is mainly caused by a bottleneck where weak vital signs tend to be submerged by large-scale and fast random body movements (RBMs) [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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Vital Sign Detection during Large-Scale and Fast Body Movements Based on an Adaptive Noise Cancellation Algorithm Using a Single Doppler Radar Sensor

Yang

Shi

Zhao

et al. 2020

Sensors

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The non-contact detection of human vital signs (i.e., respiration rate (RR) and heartbeat rate (HR)) using a continuous-wave (CW) Doppler radar sensor has great potential for intensive care monitoring, home healthcare, etc. However, large-scale and fast random body movement (RBM) has been a bottleneck for vital sign detection using a single CW Doppler radar. To break this dilemma, this study proposed a scheme combining adaptive noise cancellation (ANC) with polynomial fitting, which could retrieve the weak components of both respiration and heartbeat signals that were submerged under serious RBM interference. In addition, the new-type discrete cosine transform (N-DCT) was introduced to improve the detection accuracy. This scheme was first verified using a numerical simulation. Then, experiments utilizing a 10-GHz Doppler radar sensor that was built from general-purpose radio frequency (RF) and communication instruments were also carried out. No extra RF/microwave components and modules were needed, and neither was a printed circuit board nor an integrated-chip design required. The experimental results showed that both the RR and HR could still be extracted during large-scale and fast body movements using only a single Doppler radar sensor because the RBM noises could be greatly eliminated by utilizing the proposed ANC algorithm.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vital Sign Detection during Large-Scale and Fast Body Movements Based on an Adaptive Noise Cancellation Algorithm Using a Single Doppler Radar Sensor

Yang

Shi

Zhao

et al. 2020

Sensors

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“…Further processing of these signals to obtain the desired Doppler signals requires an arctangent demodulation procedure that involves the processes of dc calibration and phase unwrapping [29], [30]. The details of the comparison between the simulated and experimental results are discussed in the following subsection.…”

Section: B Experimental Setup For Validation Of Simulation Resultsmentioning

confidence: 99%

Gesture Sensing Using Retransmitted Wireless Communication Signals Based on Doppler Radar Technology

Wang

Tang

Chiu

et al. 2015

IEEE Trans. Microwave Theory Techn.

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This paper presents a Doppler radar based on an injection-locked quadrature receiver (ILQR) architecture that can use the wireless communication signals from an input cable or an antenna to perform gesture sensing at a short distance. Since the proposed radar does not require an illumination source, radio interference does not occur. To study parametrically the effect of the signal parameters on radar detection performance, a simulation was carried out by modeling the radar system in the discrete-time domain, and the results were verified experimentally using an ac- tuator. In demonstrated applications, the radar uses an input 20 MHz Long-Term Evolution (LTE) signal or captures an ambient Wi-Fi signal to detect several gestures quite successfully.Index Terms-Doppler radar, gesture sensor, injection locking, injection-locked quadrature receiver, injection pulling, radio interference, Wi-Fi radar, wireless communication signal.

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“…Ideally, the locus of error-free measurements from the I/Q channels of Doppler radar is a circle centered at the origin. However, the shape of an ideal circle can be easily altered by errors in practice [28].…”

Section: Signal Preprocessingmentioning

confidence: 99%

Accurate and Contactless Vital Sign Detection in Short Time Window with 24 GHz Doppler Radar

Tang

2021

Journal of Sensors

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Breathing and heartbeat are critical vital signs which reflect the health status of human beings. Aiming to accurately measure the vital sign in short time window, a novel signal processing method for Doppler radar vital sign detection is proposed. Firstly, a two-step I/Q mismatch correction method which, respectively, estimates the time invariant phase imbalance and gain ratio of I/Q channels in the calibration step and the direct-current offsets during normal operation has been proposed. By decreasing the number of estimation parameters from 5 to 2, the parameters can be effectively estimated with data distributed over shorter arc lengths. Then, to solve the discontinuity occurred in arctangent demodulation, the displacement information of chest movement is extracted from the calibrated I/Q signals by extended differentiate and cross multiply algorithm. Finally, instead of Fourier transform-based methods which require long time windows to guarantee sufficient frequency resolution, the optimal parameters of respiration and heartbeat are found by the intelligent search of the differential evolution algorithm. The experimental results show that the proposed method can accurately measure respiratory rate and heartbeat rate with a short time window. For the 8 s time window, the mean absolute errors of respiration and heartbeat were 0.52 bpm and 0.79 bpm, respectively, demonstrating its promise in real-time applications.

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High Dynamic-Range Motion Imaging Based on Linearized Doppler Radar Sensor

Cited by 81 publications

References 30 publications

Vital Sign Detection during Large-Scale and Fast Body Movements Based on an Adaptive Noise Cancellation Algorithm Using a Single Doppler Radar Sensor

Vital Sign Detection during Large-Scale and Fast Body Movements Based on an Adaptive Noise Cancellation Algorithm Using a Single Doppler Radar Sensor

Gesture Sensing Using Retransmitted Wireless Communication Signals Based on Doppler Radar Technology

Accurate and Contactless Vital Sign Detection in Short Time Window with 24 GHz Doppler Radar

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