Measurement Method for Imbalance Factors in Direct-Conversion Quadrature Radar Systems

Park, Byung-Kwon; Yamada, Shuhei; Lubecke, Victor M.

doi:10.1109/lmwc.2007.895742

Cited by 29 publications

(15 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the DC-offset and , the amplitude and the phase errors and between the channels should be estimated [16]. This was performed by using a wall plate of an automatic miller machine as a radar target, which can be programmed to move accurately to the desired position with a desired velocity.…”

Section: A Correction Of Amplitude and Phase Imbalancesmentioning

confidence: 99%

Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

2012

View full text Add to dashboard Cite

Microwave doppler radar offers significant improvements for unobtrusive heart and respiration measurement. Radar monitoring enables non-contact measurement, through clothing, of heart and respiration rate, which is desired in several applications ranging from medical sleep laboratory measurements to home health care measurements and stress monitoring. The use of high frequency radar ( 10 GHz) instead of lower frequencies ( 2.4 GHz) increases the signal-to-noise-ratio of the signal and enables the utilization of commercial radar modules. However, if high frequency radar is used, linear combining of quadrature radar channels is inadequate. Instead, a nonlinear channel combining algorithm is needed. The combining can be performed with an arctangent function if center, amplitude error, and phase error are estimated accurately and corrected. In this paper, we show that the Levenberg-Marquardt (LM) center estimation algorithm outperforms the state-of-the-art center estimation algorithm precision-wise and is computationally less complex. The simulated results show that the root mean squared error with the LM method is always less than 1%, while it is around 5%-13% with the compared method, depending on the breathing signal model used. In addition, the computational complexity of the LM method stays almost constant as the size of the data set increases, whereas with the reference method, it increases exponentially. In this paper, the LM method is validated both with simulations and with real data.Index Terms-Biomedical signal processing, Doppler radar measurement, non-contact heart and respiration measurement, physiological monitoring, remote sensing.

show abstract

Section: A Correction Of Amplitude and Phase Imbalancesmentioning

confidence: 99%

Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

2012

View full text Add to dashboard Cite

show abstract

“…These are the amplitude and the phase imbalance that exist between the I and Q channels [10], [11]. Thus, the baseband signals become (3) Now in the IQ plane, the data does not form an arc of the circle, but an arc of an ellipse instead.…”

Section: Microwave Doppler Monitoring Of Heartmentioning

confidence: 99%

“…Whereas in the aforementioned applications, the accuracy of the displacement measurement, particularly changes in the breathing amplitude, are very important features. However, real radar components suffer from problems with amplitude and phase imbalance due to hardware imperfections [10], [11]. To ensure accurate displacement calculation and signal interpretation, the imbalances need to be accurately measured and compensated for.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quadrature Imbalance Compensation With Ellipse-Fitting Methods for Microwave Radar Physiological Sensing

Zakrzewski

Singh

Yavari

et al. 2014

IEEE Trans. Microwave Theory Techn.

View full text Add to dashboard Cite

Accurate displacement measurement using quadrature Doppler radar requires amplitudes and phase imbalance compensation. Previously, this imbalance calibration has required cumbersome hardware modifications and thus can only be performed in a laboratory setting. Recently, a data-based method that does not require hardware modifications has been proposed. This simplifies the calibration process and allows the calibration to be performed on-site periodically. The method is called ellipse fitting. In this paper, the different factors affecting imbalance estimation accuracy, namely, arc length, initial phase angle, and noise level were thoroughly investigated. The Levenberg-Marquardt (LM) algorithm is proposed for the first time to increase the estimation accuracy as compared to the previously suggested algebraic fitting. Comprehensive simulations and experimental data show that the algebraic fitting method results in biased estimates. The proposed LM method has also been demonstrated to be more robust to noise, varying arc lengths, and different initial angles. The LM method reaches sufficient imbalance estimation accuracy with an arc length of 40% and a noise level of 1.5%.

show abstract

“…In addition, there are applications where obtrusive contact electrodes could disturb the measurement itself such as sleep quality monitoring in a medical sleep laboratory. The current challenges with radar monitoring, however, include removal of motion and respiration artifacts of the patient [1,2] and the background, development of a non-linear channel combining algorithm [3,4], estimation of amplitude and phase imbalance of the two radar channels [5], and development of rate detection methods [6][7][8]. Rate detection should be robust for the changes in the signal waveform due to the measurement position.…”

Section: Introductionmentioning

confidence: 99%

Separating respiration artifact in microwave doppler radar heart monitoring by Independent Component Analysis

Zakrzewski

Vanhala

2010

2010 IEEE Sensors

View full text Add to dashboard Cite

With a microwave radar, chest wall movements originating from cardiac and respiratory activity can be recorded non-contactly. A major challenge is how to separate the desired low-amplitude cardiac signal from large-amplitude artifacts, such as respiration. Commonly, the separation is performed with a narrow band-pass filter. This causes the signal edges to be rounded, which complicates the accurate timing estimation in the heart rate variability (HRV) analysis. In addition, the harmonics of the respiration signal might fall into the same frequency spectrum as the cardiac signal. In this study, we recorded data with two radars and studied signal separation using a complex-valued Independent Component Analysis (ICA). After ICA, the respiratory signal is greatly attenuated, although still present, in two of the independent components (ICs). However, respiration harmonics are reduced as well, and thus, the residual respiratory signal can be removed by filtering.

show abstract

Measurement Method for Imbalance Factors in Direct-Conversion Quadrature Radar Systems

Cited by 29 publications

References 8 publications

Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

Quadrature Imbalance Compensation With Ellipse-Fitting Methods for Microwave Radar Physiological Sensing

Separating respiration artifact in microwave doppler radar heart monitoring by Independent Component Analysis

Contact Info

Product

Resources

About