Multiphase Continuous-Wave Doppler Radar With Multiarc Circle Fitting Algorithm for Small Periodic Displacement Measurement

Park, Jae‐Hyun; Yang, Jong‐Ryul

doi:10.1109/tmtt.2020.3041264

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…Different obstructions such as box, glass, and cloth are placed between the radar and the measured object. A set of radar parameters are estimated by the least square-based ellipse fitting algorithm [29][30][31] Journal of Sensors distances and obstructions, while the total residual phase error and the ratio of amplitudes remain nearly unchanged.…”

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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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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“…Nonetheless, the estimation of the arc center can be compromised when signals have a low amplitude [20,23], especially if circle fitting methods are used. In fact, low-amplitude signals could be recurring for several reasons, such as multipath signal degradation, or when the vital signs are acquired in an alternate chest-wall position such as sideways rather than in front [24], or finally when the subject has lower chest-wall displacement.…”

Section: Introductionmentioning

confidence: 99%

“…As in cases similar to or worse than the one presented in Figure 4, the formed arcs are not sufficient to estimate the corresponding fitting circle, and exploratory techniques can be implemented to search for more information in order to enhance the CDC offsets estimation. For instance, in [20,23], authors present approaches where multiarcs are induced to gather more CDC information by changing purposely the target's distance in relation to the radar before starting the vital signs acquisition [20] or by tuning the local oscillators with different angles during the down-conversion of the received signal [23]. Nonetheless, real case scenarios present other challenges, which have a direct impact on the arc center estimation.…”

Section: Introductionmentioning

confidence: 99%

“…This means that in offline signal processing, the CDC offsets removal cannot be performed using the full signal at once. The solutions presented in [20,23] were developed assuming that the scenario is fully static or that the signal's characteristics, such as its amplitude, do not change over time. On the other hand, [22] uses a dynamic CDC offset cancellation with the goal to account for such environment changes.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Digital Signal Processing Algorithm for Vital Signs Extraction in Continuous-Wave Radars

et al. 2021

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Radar systems have been widely explored as a monitoring tool able to assess the subject’s vital signs remotely. However, their implementation in real application scenarios is not straightforward. Received signals encompass parasitic reflections that occur in the monitoring environment. Generally, those parasitic components, often treated as a complex DC (CDC) offsets, must be removed in order to correctly extract the bio-signals information. Fitting methods can be used, but their implementation were revealed to be challenging when bio-signals are weak or when these parasitic reflections arise from non-static targets, changing the CDC offset properties over time. In this work, we propose a dynamic digital signal processing algorithm to extract the vital signs from radar systems. This algorithm includes a novel arc fitting method to estimate the CDC offsets on the received signal. The method revealed being robust to weaker signals, presenting a success rate of 95%, irrespective of the considered monitoring conditions. Furthermore, the proposed algorithm is able to adapt to slow changes in the propagation environment.

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“…The phase-detection accuracy of CW/FSK radars determines the detection performance of the displacement measurement with sub-wavelength precision and the vital signal detection obtained from the movement of the surface of the human body [ 13 , 14 ]. A circle fitting method has been proposed to reduce the error in detecting the phase difference caused by the non-ideal characteristics of clutter in the surroundings or radar hardware and the imbalance between in-phase (I) and quadrature (Q) signals [ 15 , 16 , 17 ]. This method can increase the accuracy of the phase measurement in the baseband by approximating the trajectory of the measured I/Q signals to a circle [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Detrending Technique for Denoising in CW Radar

Lee

Park

Yang

2021

Sensors

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A detrending technique is proposed for continuous-wave (CW) radar to remove the effects of direct current (DC) offset, including DC drift, which is a very slow noise that appears near DC. DC drift is mainly caused by unwanted vibrations (generated by the radar itself, target objects, or surroundings) or characteristic changes in components in the radar owing to internal heating. It reduces the accuracy of the circle fitting method required for I/Q imbalance calibration and DC offset removal. The proposed technique effectively removes DC drift from the time-domain waveform of the baseband signals obtained for a certain time using polynomial fitting. The accuracy improvement in the circle fitting by the proposed technique using a 5.8 GHz CW radar decreases the error in the displacement measurement and increases the signal-to-noise ratio (SNR) in vital signal detection. The measurement results using a 5.8 GHz radar show that the proposed technique using a fifth-order polynomial fitting decreased the displacement error from 1.34 mm to 0.62 mm on average when the target was at a distance of 1 m. For a subject at a distance of 0.8 m, the measured SNR improved by 7.2 dB for respiration and 6.6 dB for heartbeat.

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Multiphase Continuous-Wave Doppler Radar With Multiarc Circle Fitting Algorithm for Small Periodic Displacement Measurement

Cited by 10 publications

References 15 publications

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

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

Dynamic Digital Signal Processing Algorithm for Vital Signs Extraction in Continuous-Wave Radars

Detrending Technique for Denoising in CW Radar

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