research article fmcw radar for small displacement...

Hindawi Publishing CorporationInternational Journal of Antennas and PropagationVolume 2013, Article ID 571986, 5 pageshttp://dx.doi.org/10.1155/2013/571986

Research ArticleFMCW Radar for Small Displacement Detection ofVital Signal Using Projection Matrix Method

Dan Zhang, Masahiko Kurata, and Takayuki Inaba

The Graduate School of Informatics and Engineering, University of Electro-Communications, 1-5-1 Chofugaoka,Chofu, Tokyo 182-8585, Japan

Correspondence should be addressed to Dan Zhang; [email protected]

Received 12 June 2013; Revised 7 October 2013; Accepted 13 November 2013

Academic Editor: Atsushi Mase

Copyright © 2013 Dan Zhang et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Small displacement detection has been studied for extracting heart rate signal from the respiratory variation component ofthe human body with the FMCW radar method. And a new signal processing method of vital signal has been proposed forsuppression of unrequired variation components called projectionmatrix method.We have presented experimental results of smalldisplacement detection to confirm the validity of the method.

1. Introduction

Small displacement detection technology in millimetre orderat microwave band is expected to a number of applications.Some examples have been proposed such as determiningbiological signals of heart rate and respiration by noncontactmeasuring the displacement of the surface of the bodyand searching survivors under rubble after earthquake bydetecting body movements [1–3]. We know that the CW(continuouswave) radar system andUWBpulse radar systemhave been used for measuring the small displacement [4].CW radar has the advantages of low power consumptionand simple radio architecture. Moreover, CW radar can alsocancel out clutter noise by proper adjustment of the radiofront-end architecture. The main advantages of the FMCW(frequency modulated continuous wave) radar are simplesolid-state transmitters, resistance to interception, and goodrange resolution. FMCW method performs the velocity andrange measurement, and it is considered a good solution fornoncontact vital signs detection [1–3].

However, the respiration signal dominates the spectra,and its harmonics may overwhelm the heartbeat signal,making the latter invisible in the spectral analysis sign.The conventional Fourier transform method cannot reliablyseparate the components. Some signal processing methodsare needed to solve this problem. A parametric and cyclic

optimization approach, called the RELAX algorithm, hasbeen discussed [5].Themethodmay take longer computationtime to accurately estimate closer signals [6]. Consideringthese problems, in this paper, we propose a method forsuppressing unnecessary periodic fluctuation componentwith a projection matrix. First we briefly introduce formulaof FMCW method and then consider the removal methodof the reflected waves from stationary objects that exist inthe periphery of the body. Last we have shown the resultsof the experiment with radar devices that comply withspecified low power radio station 24GHz. We have verifiedthe effectiveness of the proposed method for suppressingunnecessary fluctuation component.

2. Materials and Methods

2.1. Basic Formula. As we know, the FMCW method usestransmission waves swept linearly with times. From theFMCW principle, the received waves are mixed with thetransmission and then remove the sum signal by LPF (lowpass filter 5 kHz); the beat signal can be obtained as follows[7]:

𝐵 (𝑡) ≅ 𝐴 exp {𝑗 [2𝜋 (−2𝐵𝑅𝑐𝑇−2V𝜆) 𝑡 −4𝜋𝑅

𝜆]} . (1)

2 International Journal of Antennas and Propagation

Correlation matrixMeasurementdata

x11 x12x1M

x21 x22 x2M

xN1 xN2 xNM

expressed by correspondingeigenvectors en

obtained calculated

· · ·

· · ·

· · ·

⋱...

......

R = ∑N

n=1𝜆neneH

n

P = I − eneH

nThe projection matrixThe matrix Y = PX

Figure 1: Schematic diagram of the projection matrix method.

Distance

Body

Radar

Figure 2: Schematic diagram of the experiment.

LPFA/DSignal processing

Local

Tx

Rx

Figure 3: Block diagram of the radar system.

Repeating to send signal and obtain a beat signal of (1),we should pay attention to the phase term 4𝜋 ⋅ 𝑅/𝜆, the phaseof the distance 𝑅 will vary 2𝜋, according as the distance 𝑅varies 𝜆/2. Thus, it is possible to measure small change in thedistance 𝑅 by the phase term with high sensitivity. The phasevalues will be calculated by 𝐼 and 𝑄 signals of the beat signaland then obtained by Fourier transform.

2.2. Interference Removal. If there are some other stationaryobjects around the body, theymay interfere with the reflectedwave received. Considering the interference, (1) can beconverted to

𝐵 (𝑡) ≅ 𝐴 exp {𝑗 [2𝜋 (−2𝐵𝑅𝑐𝑇−2V𝜆) 𝑡 −4𝜋𝑅

𝜆]}

+ 𝐴𝑠exp {𝑗 [2𝜋 (−

2𝐵𝑅𝑠

𝑐𝑇) 𝑡 −4𝜋𝑅𝑠

𝜆]} .

(2)

With the passage of time, the second term in the equationfor the objects will have no variation, so we can remove theterm by subtraction [7].

2.3. Projection Matrix. The heart rate signal must beextracted from the respiratory variation component, whenwemeasure the displacement of the body surface. The method

of HPF (high pass filter) has been used for separating heartsignal components from a respiratory fluctuation component[8, 9]. However, this method cannot separate the harmoniccomponents from the respiratory variation, and it is hardto reproduce heart rate waveform and measure the heartrate [10]. So we propose a method for suppressing unneces-sary periodic fluctuation component with projection matrix[11]. The method can reduce the effects of harmonics. Theschematic diagram of the method is shown in Figure 1.

First, we obtain the sampling period of unnecessaryfluctuation component by performing Fourier transform forthe measurement data. We can obtain the vector X =[𝑥1, 𝑥2, . . . , 𝑥

𝑚], where 𝑥 express sampling dates whose

period number is𝑁; that is, each 𝑥𝑖has𝑁 sampling dates.

From the vector X, the correlation matrix can be calcu-lated as follows:

R = ⟨XX𝐻⟩ , (3)

where the operation ⟨⟩ represents the average.The correlation matrix R can be expressed with eigen-

value decomposition as follows:

R =𝑁

∑

𝑛=1

𝜆𝑛e𝑛e𝐻𝑛, (4)

International Journal of Antennas and Propagation 3

Disp

lace

men

t (m

m)

−4

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Time (s)0 5 10 15 20

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er (d

B)

−60

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Frequency (Hz)0 0.5 1 1.5 2

1

TPM

4

TPM

(a) Conventional method

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(b) Projection matrix method

Figure 4: Signal processing results at the distance of 30 cm.

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4 International Journal of Antennas and Propagation

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m)

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−5

−2.5

0

2.5

5

7.5

101

TPM

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where 𝜆𝑛are the eigenvalues and e

𝑛are the corresponding

eigenvectors.Because the vector X is divided by the period of

unnecessary fluctuation component, the largest eigenvaluecorresponds to the fluctuation component. The matrix Scorresponding to the unnecessary fluctuation componentis obtained from the eigenvector e

𝑖corresponding to the

maximum eigenvalue as follows:

S = e𝑖e𝐻𝑖. (5)

Then, the projectionmatrix for the suppression of unnec-essary fluctuation component is defined as

P = I − S, (6)where I is a𝑁×𝑁 identity matrix. We perform the followingprocessing for the vectorX to suppress unnecessary variationcomponent:

Y = PX. (7)The projection matrix P is orthogonal to the matrix S

corresponding to unnecessary fluctuation component of thevector X.

3. Results and Discussion

The experiment is performed using the software radar devel-oped by our laboratory as shown in the Figure 2. The target

is a human body. The propagation frequency is 24.15GHz,sampling frequency is 10 kHz, sweep bandwidth is 72MHz,and sweep time is 25.6ms in the FMCWmethod, respectively.The block diagram of the radar system has been shownin Figure 3. Usually, the data may be measured in threeconditions such as the orientation of the body, the distanceto radar, and the radar irradiation position on body. In thistime, the front of body is facing the radar and the irradiationposition is selected as the largest displacement caused byheart beating. The distance to radar is changed to 30 cm,40 cm, and 200 cm in our measurement, respectively.

The experiment results using the FMCW method areshown in Figures 4(a), 5(a), and 6(a), and after using theprojection matrix method, the results are shown in Figures4(b), 5(b), and 6(b), respectively. First we obtain the timedomain as shown in the left figures and then convert them tospectrum as shown in the right figures. We can easily knowthat the high peak near 0.25Hz is the respiration rate in eachFigure (a), and we also obviously find that the heartbeat rateis about 0.9Hz in each Figure (b). Especially, the signal isrelatively weak in the distance of 200 cm, and the heartbeatis not obvious. After importing the method, it is possible toclearly show the heart signal.

Using the proposed projectionmatrixmethod, even if thefrequency of the heart rate and respiration frequency are closeto each other, it is possible to separate and extract the heartrate signal.

International Journal of Antennas and Propagation 5

4. Conclusions

We have performed small displacement measurement withthe FMCW radar method for extracting the heart rate signalof the humanbody.Andwehave proposed a signal processingmethod for suppressing unnecessary periodic fluctuationcomponent with the projection matrix. From the experimentresults, the effectiveness of the method has been confirmed.

References

[1] C. Li, Y. Xiao, and J. Lin, “Experiment and spectral analysis ofa low-power K a-band heartbeat detector measuring from foursides of a human body,” IEEE Transactions onMicrowaveTheoryand Techniques, vol. 54, no. 12, pp. 4464–4471, 2006.

[2] M. Wakayama, H. Ezaki, I. Arai, and T. Miwa, “Non-contactmeasurement of heart rate using FM-CW radar,” IEICE Tech-nical Report SANE2005-3, 2005, (Japanese).

[3] I. Arai, “Life-detection radar for rescue purpose,” IEICE Tech-nical Report SANE99-100, 2000, (Japanese).

[4] C. Li and J. Lin, “Recent advances in doppler radar sensors forpervasive healthcare monitoring,” in Proceedings of the Asia-PacificMicrowave Conference (APMC ’10), pp. 283–290,WE4C-5, Yokohama, Japan, December 2010.

[5] C. Li, J. Ling, J. Li, and J. Lin, “Accurate doppler radar non-contact vital sign detection using the RELAX algorithm,” IEEETransactions on Instrumentation and Measurement, vol. 59, no.3, pp. 687–695, 2010.

[6] N. Shahid, D. Fang, W. Sheng, and X. Ye, “The high resolutionestimate—a comparative study,” in Proceedings of the Interna-tional Conference on Computational Electromagnetics and itsApplications (ICCEA ’99), pp. 262–265, 1999.

[7] M. Kurata and T. Inaba, “Basic study of small displacementdetection using microwave radar,” IEICE Technical ReportSANE2012-135, 2013.

[8] B. Lohman, O. Boric-Lubecke, V. M. Lubecke, P. W. Ong, andM. M. Sondhi, “A digital signal processor for doppler radarsensing of vital signs,” IEEE Engineering inMedicine and BiologyMagazine, vol. 21, no. 5, pp. 161–164, 2002.

[9] M. Wakayama, H. Ezaki, I. Arai, and T. Miwa, “Non-contactmeasurement of heart rate using FM-CW radar,” IECE Tech-nical Report SANE2005-3.

[10] G. Lu, F. Yang, X. Jing, and J.Wang, “Contact-freemeasurementof heartbeat signal via a doppler radar using adaptive filtering,”in Proceedings of the 2nd International Conference on ImageAnalysis and Signal Processing (IASP ’10), pp. 89–92, April 2010.

[11] T. J. Nohara, P. Weber, and A. Premji, “Adaptive mainbeamjamming suppression for multi-function radars,” in Proceedingsof the IEEE National Radar Conference, pp. 207–212, May 1998.

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