CN113440120A - Millimeter wave radar-based method for detecting respiration and heartbeat of person - Google Patents

Millimeter wave radar-based method for detecting respiration and heartbeat of person Download PDF

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CN113440120A
CN113440120A CN202110671304.0A CN202110671304A CN113440120A CN 113440120 A CN113440120 A CN 113440120A CN 202110671304 A CN202110671304 A CN 202110671304A CN 113440120 A CN113440120 A CN 113440120A
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魏少明
张驰
王俊
王岩松
洪文衍
耿雪胤
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Abstract

The invention discloses a millimeter wave radar-based method for detecting respiration and heartbeat of people, which belongs to the field of radar signal processing and specifically comprises the following steps: firstly, a radar transmits a linear frequency modulation pulse train signal to a target human body to be detected and receives a pulse echo signal containing clutter; forming a sampling matrix through deskewing and discretizing; then, performing fast fourier transform on each row of the matrix to obtain a time-distance image Rp (m, n); taking each column of Rp as a distance unit, and eliminating clutter of all the distance units by using the circle center of a constellation diagram; secondly, taking the noise power average value of each distance unit as a threshold, adopting a unit average constant false alarm to detect a target existing in each distance unit, and performing Doppler demodulation according to the position of the target; finally, reconstructing the separated waveforms of respiration and heartbeat for the demodulation result; carrying out period estimation on single breath or heartbeat in the reconstructed waveform; outputting a respiration heartbeat waveform and a real-time frequency estimation of the pulse signal; the invention has simple algorithm and reduces the calculation amount.

Description

Millimeter wave radar-based method for detecting respiration and heartbeat of person
Technical Field
The invention belongs to the field of radar signal processing, and particularly relates to a millimeter wave radar-based method for detecting the respiration and heartbeat of a person, which realizes non-contact monitoring of the respiration and heartbeat states of a target person to provide health information.
Background
The respiration and heartbeat parameters are important judgment basis for judging whether the heart and lung activities of the human body are normal or not, and the occurrence of a plurality of sudden diseases can generally cause the heart and lung activities of the human body to be abnormal, so that the respiration and heartbeat parameters are monitored in real time, and the method has very important significance in the field of medical monitoring.
Conventional respiratory parameter detection methods include pressure sensor methods, volume measurement methods, palpation measurement methods, and the like, and measurement methods of heartbeat parameters include electrocardiography, finger pressure pulse measurement methods, heart sound methods, and the like, and are contact measurement methods, and contact with a subject through electrodes, sensors, and the like is required.
The basic principle of the radar-based vital signal detection technology is as follows: the radar emits electromagnetic waves with specific waveforms, echoes are generated after the electromagnetic waves irradiate the moving chest wall, and the displacement information of the thoracic cavity containing breathing and heartbeat parameters is obtained by demodulating the echoes modulated by the Doppler of the chest wall of the human body. The technology can realize long-distance and non-contact detection, does not wear the burden of the sensor under long-time monitoring, and can be used in medical treatment, monitoring and other scenes.
There are three main radar systems applied in the field of life signal detection, which are Continuous Wave (CW) radar, pulse Ultra Wide Band (UWB) radar, and linear Frequency Modulated Continuous Wave (FMCW) radar. The continuous wave radar system is simple and has no ranging capability, so that the continuous wave radar system has weak resistance to interference and clutter and cannot distinguish a plurality of targets. The UWB radar and the FMCW radar can inhibit interference of a part of noise waves through distance resolution and distinguish a plurality of targets at different distances; however, the UWB radar has higher sampling requirement, and the corresponding millimeter wave product is expensive; therefore, the FMCW millimeter wave radar becomes a good choice.
However, the current method for extracting respiratory and heartbeat signals of a human body by adopting an FMCW millimeter wave radar mainly faces two problems:
first, the range selectivity of the FMCW radar is still insufficient to suppress all clutter, such as that at the same range as the target. Since the target body motion of interest is contained in the phase of the received signal, it is very sensitive to clutter of all non-target signals, and it is necessary to eliminate clutter signals in the background without destroying the target signal, otherwise the doppler demodulation result will be in serious error.
Secondly, it is necessary to select an appropriate demodulation algorithm, extract the motion information contained in the echo phase at each time correctly and efficiently, and restore it to a meaningful time-domain motion waveform in time. The human chest movement is a complex movement with constantly changing movement speed and direction, and the radar signal wavelength of the millimeter wave frequency band is close to the amplitude of the respiratory movement, so that a demodulation method different from a low-frequency band radar needs to be designed.
Furthermore, since the non-contact measurement is susceptible to external and noise interference, the demodulation algorithm needs to have sufficient reliability to overcome these influencing factors, give the correct target chest movement so as to extract the information of interest such as respiration and heartbeat from it, and estimate the frequencies of the two, respectively.
Disclosure of Invention
Aiming at the problems, the invention provides a method for detecting the respiration and heartbeat of a person based on a millimeter wave radar, which can better inhibit clutter contained in a target signal, demodulate the micro motion of the thoracic cavity of a human body more accurately and reliably, and carry out corresponding respiration and heartbeat waveform separation and frequency estimation on the basis.
The millimeter wave radar-based method for detecting the breathing and heartbeat of the person comprises the following specific steps:
firstly, aiming at a target human body to be detected, selecting an FMCW radar to transmit M linear frequency modulation pulse train signals to the target human body;
secondly, receiving each pulse echo signal containing a human body target echo signal and clutter by a radar;
thirdly, respectively carrying out deskew processing on echo signals under each pulse received by the radar, sampling the echo signals into discrete signals, and forming the discrete signals of all the pulses into a sampling matrix;
step four, performing fast Fourier transform on each row of the sampling matrix respectively to obtain a time-distance image Rp (m, n);
step five, taking each row of the time-distance image Rp as a distance unit, and eliminating clutter of all the distance units by using the circle center of a constellation diagram;
and step six, calculating the noise power near each distance unit, taking the average value as a judgment threshold, detecting the target existing in each distance unit by adopting a unit average constant false alarm, and giving the distance unit where each human body target is located.
Seventhly, performing Doppler demodulation on the target human body according to the position of the target human body;
step eight, utilizing an improved EMD algorithm to separate respiration and heartbeat of a demodulation result and reconstructing a waveform;
step nine, using a short-period average amplitude difference function AMDF to perform period estimation on single breath or heartbeat in the reconstructed waveform;
step ten, outputting respiration heartbeat waveforms and real-time frequency estimation of the M pulse signals;
including the separated respiration and heartbeat time domain waveforms; and the respiration and heartbeat frequency estimated by the AMDF, and the acquisition of time domain waveform and frequency information of respiration and heartbeat signals by the radar is completed.
The invention has the advantages that:
1) when human motion is extracted, the traditional method directly conducts transverse filtering in slow time and can destroy direct current components in target signals.
2) Compared with the traditional method for directly demodulating the phase, the millimeter wave radar-based method for detecting the breathing heartbeat of the person adopts the micro Doppler demodulation phase information of signal autocorrelation, can reduce the influence of random motion of a target human body on the breathing heartbeat extraction, and has better stability. Meanwhile, the algorithm is simpler, and the calculated amount is reduced.
3) The method for detecting the respiration and heartbeat of the person based on the millimeter wave radar adopts the AMDF to carry out frequency estimation on the respiration and heartbeat first-order difference waveform subjected to autocorrelation Doppler phase demodulation, and compared with the traditional autocorrelation or FFT method, the AMDF is more suitable for signals with a single period and more harmonic waves, and is lower in calculated amount.
Drawings
FIG. 1 is a flow chart of a radar echo signal processing method of the present invention;
FIG. 2 is a flowchart of a method for detecting respiration and heartbeat of a person based on a millimeter wave radar.
Detailed Description
The following describes embodiments of the present invention in detail and clearly with reference to the examples and the accompanying drawings.
The invention relates to a human respiration heartbeat waveform extraction and frequency estimation method based on an FMCW millimeter wave radar, as shown in figure 1, firstly, the radar transmits a linear frequency modulation pulse train signal to a target human body and receives an echo signal, the echo signal is subjected to deskew filtering and is dispersed into a sampling matrix, each row in the matrix is respectively subjected to FFT (fast Fourier transform) to obtain a time-distance image Rp (m, n); taking each column of Rp as a distance unit, and eliminating clutter of all the distance units by using the circle center of a constellation diagram; detecting targets existing in each distance unit by adopting CFAR, giving the distance unit where each human body target is located, and performing Doppler demodulation; separating respiration and heartbeat waveforms by adopting an improved EEMD algorithm, and finally performing cycle estimation on single respiration or heartbeat in a reconstructed waveform by using AMDF; outputting respiration heartbeat waveforms and real-time frequency estimation of the M pulse signals; by optimizing clutter elimination, Doppler demodulation and frequency estimation calculation methods, the accuracy of the radar in human body respiration heartbeat waveform detection and frequency estimation is improved, and the maximum effective detection distance under the same transmitting power is increased.
The millimeter wave radar-based method for detecting the breathing and heartbeat of the person comprises the following specific steps as shown in fig. 2:
firstly, aiming at a static target human body to be detected, selecting an FMCW radar to transmit M linear frequency modulation pulse train signals to the target human body;
the calculation formula of the chirp burst signal is as follows:
Figure BDA0003119390550000031
tmis a slow time, representing the time at which the mth pulse is transmitted; t is tmT is the repetition period of the pulse signal; the pulse repetition period T being the carrier frequency period in general
Figure BDA0003119390550000032
When each pulse signal has the same carrier phase at the start time, expressed with the slow time tmIndependently: m is the frame number of slow time, and M is the total number of transmitted pulses in the observation time range;
Figure BDA0003119390550000033
for a fast time, the propagation of the electromagnetic wave emitted by the radar is represented by the emission time tmA time of origin;
Figure BDA0003119390550000034
t is the total time; t ispIs the pulse width of the pulse; the pulse transmits a signal in a pulse width of one repetition period, and receives the signal in the rest of the period; exp (j2 pi fct) is a carrier frequency signal of a linear frequency modulation signal transmitted by a radar; gamma is the frequency modulation coefficient of the chirp signal, fcIs the starting frequency of the chirp signal; c is the speed of light, i.e., the speed at which the signal propagates.
Secondly, receiving each pulse echo signal containing a human body target echo signal and clutter by a radar;
target body initially at range radar R0The part is opposite to the radar and keeps static in the detection time, and the thorax fluctuating motion is caused only by life motions such as respiration, heartbeat and the like, so that the distance from the human body to the radar is slightly changed at each pulse moment.
Aiming at the mth pulse, the target human chest echo signal received by the radar
Figure BDA0003119390550000041
Comprises the following steps:
Figure BDA0003119390550000042
Atargetis the echo intensity of the chest of the target human body, c is the propagation velocity of the chirp signal, RmThe radial distance between the fluctuation of the target human thorax and the radar under the mth pulse; the fluctuation caused by the respiration, heartbeat and other life movements of the chest cavity of the human body is reflected, so that the distance to the radar is changed in slow time, the variation is extremely small and is far smaller than the resolution of the radar.
Meanwhile, the radar can also receive echoes from other objects in an observation range or objects such as target human limbs and trunks, and the echoes are collectively called clutter signals. These clutter producing objects may be anywhere, may be at the same location or at different locations from the human target of interest, and have random reflection intensities. The most important difference between clutter and target signal is that these objects remain stationary during the observation time, so the clutter signal does not change over slow time, denoted as
Figure BDA0003119390550000043
Figure BDA0003119390550000044
NcNumber of scattering centers to generate clutter i tableShowing the ith scattering center, the signal superposition of each scattering center being the clutter signal received by the radar each time
Figure BDA0003119390550000045
AiIs the echo intensity, R, of the ith scattering centeriIs the radial distance of the ith scattering center from the radar.
The radar transmits the m pulse, and the received echo signal is the target echo at the moment
Figure BDA0003119390550000046
And clutter signals
Figure BDA0003119390550000047
Superposition of (2):
Figure BDA0003119390550000048
thirdly, respectively carrying out deskew processing on echo signals under each pulse received by the radar, sampling the echo signals into discrete signals, and forming the discrete signals of all the pulses into a sampling matrix;
the method specifically comprises the following steps:
firstly, the received total signal and the transmitted signal conjugate are multiplied for deskew processing, and the calculation formula is as follows:
Figure BDA0003119390550000049
Figure BDA0003119390550000051
after deskew, each scattering point produces an amplitude A having
Figure BDA0003119390550000052
Initial phase, fast time frequency of
Figure BDA0003119390550000053
The frequency of the single-frequency signal reflects the distance R from the point to the radar; ignoring the third residual phase term
Figure BDA0003119390550000054
Since this value is extremely small.
Two end parts of a received signal are sampled and discarded, so that echo signals generated by each scattering point exist in sampling time, and therefore, the signal length difference caused by different time delays is not considered in a single pulse; sampling rate fsDepending on the maximum distance to the radar possible for the target, the echo signal frequency should be selected for this distance
Figure BDA0003119390550000055
Twice as much to avoid distance ambiguity. Sampling interval time
Figure BDA0003119390550000056
Sampling the de-skewed signal, wherein the discrete signal of the nth frequency domain sampling point in the mth pulse is as follows:
Figure BDA0003119390550000057
where n denotes the nth sample point within the pulse and m denotes the mth pulse. Each of the M pulses is performed by NsSub-sampling, N in each pulse in the sampling processsThe multiple sampling results are arranged in rows, each pulse is arranged in columns, and all sampling points of all pulses jointly form M × NsThe sampling matrix of (a); n is a radical ofsRepresenting the number of samples per pulse.
Step four, performing Fast Fourier Transform (FFT) on the sampling matrix in each pulse, namely each row of the sampling matrix, and acquiring a time-distance image Rp (m, n);
the calculation formula is as follows:
Figure BDA0003119390550000058
the FFT results in the spectrum of the echo signal, and n represents the nth frequency domain sample point, i.e., the nth range bin. After FFT, each scattering center is transformed to obtain a corresponding scattering center
Figure BDA0003119390550000059
Complex amplitude of frequency location:
Figure BDA00031193905500000510
forming a one-dimensional range profile at the time within each pulse; the time-distance image Rp at slow time is MxNsOf the matrix of (a).
Step five, taking each row of the time-distance image Rp as a distance unit, and eliminating clutter of all the distance units by using the circle center of a constellation diagram;
constellation circle center estimation is carried out on each distance unit independently to eliminate clutter processing, namely processing is carried out on each column of a time-distance image Rp to estimate and eliminate clutter in the distance unit; for each range bin, it consists of a slow time sequence x (m) of complex amplitude values after each pulse FFT over slow time. Assuming that the unit has superimposed thereon the human target signal and clutter, it can be expressed as:
Figure BDA0003119390550000061
the first term on the right side of the above equation represents the human target peak, the amplitude is constant and the phase is constantly changed, and the first term on the right side of the above equation is a complex vector with a direction change on the complex plane. The second term is the peak value of the clutter, the value of which remains unchanged over the slow time, and the corresponding constellation diagram is a point on the complex plane. Drawing a slow time sequence x (m) on a complex plane to form a constellation diagram, superposing signal vectors and clutter, wherein all points are positioned at different positions on a circumference, and a complex vector a + jb represented by circle center coordinates (a, b) is a complex amplitude corresponding to the clutter
Figure BDA0003119390550000062
Radius is the amplitude | A corresponding to the signaltargetL, |; because the phase of the signal complex vector is constantly changed, each point is distributed at different positions on the circumference, and the circle center can be estimated according to a plurality of points, so that the clutter signal in the distance unit can be obtained. The circle center estimation algorithm arranges the IQ channel sampling values of x (m) into a matrix form as follows:
Figure BDA0003119390550000063
Figure BDA0003119390550000064
Figure BDA0003119390550000065
wherein ImAn I-channel sample representing the mth pulse; qmRepresenting Q channel sampling of the mth pulse, namely a real part and an imaginary part of x (m), and forming an A matrix as a data matrix and a b matrix as an observation matrix; the X matrix is an unknown vector obtained by the belt calculation, wherein a and b represent coordinates of the circle center on a complex plane, and r is a radius.
Figure BDA0003119390550000066
Solution of1After the norm is minimized to x, the clutter amplitude in the distance unit can be obtained and subtracted from the signal to eliminate the clutter influence. And subtracting the clutter signals a + bj corresponding to each column of the time-range image matrix to obtain the time-range image matrix after removing the clutter.
And subtracting the complex vector a + bj corresponding to the circle center coordinate from all the time signals of each distance unit to eliminate clutter influence, and obtaining a time-distance image only containing a target and a noise signal.
And step six, calculating the noise power near each distance unit, taking an average value as a judgment threshold, detecting the target existing in each distance unit by adopting a unit average Constant False Alarm Rate (CFAR), and giving the distance unit where each human body target is located.
The step adopts the unit average constant false alarm rate detection (CA-CFAR), which is one of the processing methods of the average value (ML) CFAR. Under the condition that the background noise envelope of the radar echo signal is assumed to obey Rayleigh distribution, the average value of the noise power near the target is calculated and used for estimating the background noise power to be used as an adaptive threshold for judging whether the target exists or not.
Whether the target exists can be judged through the CFAR, and the distance unit where the target exists is known when the target exists.
The time-distance image is first subjected to single-pulse linear detection, i.e., the absolute value of the complex amplitude value after FFT is taken. And summing the Rp matrixes after the absolute values are taken according to columns to obtain a one-dimensional range profile for CFAR target detection in distance.
The noise in each range bin is first estimated for a one-dimensional range profile sliding window process. The noise estimate may be expressed as:
Figure BDA0003119390550000071
xiand yiRefers to the reference cells that detect the leading and trailing edges of the cell, and N represents the number of sliding window reference cells on both sides. If the amplitude of the target unit is D, the judgment criterion at this time is as follows:
Figure BDA0003119390550000072
t is a constant whose value is determined by a given false alarm probability, determining the sensitivity of the CFAR detection.
Seventhly, performing Doppler demodulation on the target human body according to the position of the target human body;
knowing the position of the target, the phase term of the target within that position can be extracted
Figure BDA0003119390550000073
Obtaining the human thorax movement signal, namely the phase of the row of the distance unit of the position of the two-dimensional time distance image, namely the phase of the FFT peak value of the target echo at each moment, wherein the phase contains the target distance information R at the momentm；fcIs the carrier frequency; phi is a0Is the initial phase. The demodulation algorithm firstly carries out shift autocorrelation processing on an FFT peak value sequence of a target signal, and the original sequence is as follows:
Figure BDA0003119390550000074
and carrying out dislocation conjugate multiplication on the slow time signal sequence x (m), neglecting the amplitude, taking an imaginary part, and carrying out displacement conjugate multiplication to obtain:
Figure BDA0003119390550000075
the phase of the signal at this time is determined by the amount of change R in the target distance between two pulse timesm-Rm-1And (4) determining. The human body movement speed is very slow, so that the distance change between two pulses is a minimum value, and the imaginary part can be directly taken from the h (m) sequence to obtain the approximate value
Figure BDA0003119390550000076
The result can be regarded as the first difference of the time series of the distance from the target to the radar, the waveform of which is different from the actual motion waveform of the human body, but all motion information is covered.
Distance R from human target thorax to radarmThe respiratory and heartbeat motion, which are constantly changing, can be considered as a superposition of the respiratory and heartbeat motion. Approximating respiratory and heartbeat motion as periodic signals with different period lengths and amplitudes, can be expressed as the sum of a series of harmonics:
Figure BDA0003119390550000081
Figure BDA0003119390550000082
gr(m) is the waveform of the respiration,
Figure BDA0003119390550000083
Nris the number of harmonics of the breath, AriIs the amplitude of the breath; f. ofrIs the frequency of breathing; phi is ariIs the initial phase of respiration;
gh(m) is the waveform of the heartbeat,
Figure BDA0003119390550000084
Nhthe number of harmonics of the heartbeat, AhiThe amplitude of the heartbeat; f. ofhIs the frequency of the heartbeat; phi is ahiIs the initial phase of the heartbeat;
i is the harmonic order, the ith harmonic frequency is f. iHz, and breathing and heartbeat motions vary from person to person and vary with time, so that breathing and heartbeat frequencies, harmonic numbers, harmonic amplitudes, and initial phases are uncertain.
The respiratory and heartbeat movements are superposed to obtain the human thorax movement, and the distance radar R is used for the initial moment0At the m-th pulse time, the distance R from the thorax of the target human body to the radarmExpressed as:
Rm＝R0+gr(m)+gh(m)
autocorrelation demodulation differentiates the phases:
Figure BDA0003119390550000085
due to the pulse transmission period time TpVery short, and therefore the phase change of each sinusoidal function at two moments before and after is a minimum, one can perform a taylor expansion on each term and take a first order approximation:
Figure BDA0003119390550000086
the process of differentiating the phases therefore does not change the original frequency of the signal, but changes the energy distribution of the target spectrum, so that the energy of high frequency components, such as harmonics and heartbeat components, is relatively larger. It is necessary to use EMD decomposition and AMDF period estimation methods in subsequent frequency estimation to obtain more reliable respiratory and heartbeat frequency estimates using harmonic energy.
At the same time, the human body moves RmMay also include the distance changes caused by random motion of the human body during the measurement process. The random motion occurs at any time, with any magnitude and direction. Can be expressed as a series of step functions:
Aiu(m-mi)
Aithe amplitude of the movement, and the positive and negative of the movement reflect the direction of the movement; u (m-m)i) Is a unit step function, and represents that the motion occurs in miThe time of day. In the presence of random motion, the autocorrelation demodulation result is:
Figure BDA0003119390550000091
the result h (m) comprises the first order difference of the respiratory waveform
Figure BDA0003119390550000092
First order difference between waveform and heartbeat waveform
Figure BDA0003119390550000093
Waveform, and first order difference of random motion
Figure BDA0003119390550000094
The amplitudes of the harmonics of the respiration and heartbeat waveforms are weighted differently after differentiation, but the frequencies are unchanged. The random motion is represented by a step function u (m-m)i) Becomes the impulse function delta (m-m)i) The influence range can be from the original miAfter the moment becomes m onlyiThe time of day.
Step eight, utilizing an improved EMD algorithm to separate the respiration sequence from the heartbeat sequence of the demodulation result h (m), and reconstructing a waveform;
the coherent demodulated breath and heartbeat are superimposed in the sequence of demodulation results and need to be separated to obtain separate breath and heartbeat waveforms. Because the frequency variation range of the signal is large and is not suitable for direct filtering, an improved empirical mode decomposition (EEMD) algorithm is adopted for separating respiration from heartbeat.
Firstly, the Empirical Mode Decomposition (EMD) algorithm comprises the steps of:
step a: and solving all maximum values and minimum values of the signal h (m), fitting all maximum value points of the signal to form an upper envelope curve, and fitting all minimum value points to form a lower envelope curve.
Step b: calculating the mean of the upper and lower envelope
Figure BDA0003119390550000095
Then subtracting the envelope mean value from the original signal h (m)
Figure BDA0003119390550000096
Obtain the signal c (m), i.e
Figure BDA0003119390550000097
Step c: and (c) judging whether the number of the extreme points and the zero points of the signals c (m) is different by one at most and whether the average value of the envelope line is zero, if the two conditions are not met, making the signal c (m) equal to the original signal h (m), repeating the previous steps until the conditions are met, stopping iteration, and taking the c (m) as a first Intrinsic Mode Function (IMF) component. Subtracting the first IMF component from the original signal h (m) to obtain a first residual component r1(m)。
Step d: and (c) continuously repeating the steps a-c by regarding the residual component as the original signal until the residual component is monotonous. At this point a total of k IMF components are obtained.
Improved empirical mode decomposition (EEMD) adds white noise prior to EMD decomposition of the signal. And adding white noise to the signal for multiple times and carrying out average processing on the decomposed IMF as a decomposition result. If the number of repetitions is sufficient, the white noise added with an average value of 0 can be removed by an averaging process, and the decomposition effect on the signal itself can be improved.
EMD decomposes the signal into a series of eigenmode components IMF; the separated IMF is one of respiration, heartbeat or noise signals, and each IMF is judged through a frequency spectrum: performing FFT on each IMF, searching a frequency spectrum peak value of each IMF, and if the frequency spectrum peak value falls within a respiratory or heartbeat frequency range, considering the IMF as a respiratory or heartbeat component; otherwise, the IMF is noise; and adding all IMFs belonging to the respiratory or heartbeat components, namely reconstructing a waveform by the obtained respiratory or heartbeat signal.
Step nine, using a short-period average amplitude difference function AMDF to perform period estimation on single breath or heartbeat in the reconstructed waveform;
the period is estimated for a reconstructed signal containing only a single signal component using a periodic short time average amplitude difference function (AMDF) method. Firstly, calculating a signal sequence delay shift subtraction sequence R by using AMDFn(k) The calculation formula is as follows:
Figure BDA0003119390550000101
wherein x (i) is a respiratory or heartbeat waveform sequence reconstructed by EMD, and has a length of N; rnIs the subtracted amplitude difference function, and k is the number of delay points.
The first term on the right contains N-k terms for summation, which is the maximum number of k delay difference terms that can be allowed by the N-length sequence. In order to avoid the decrease of the number of the summation terms along with the increase of k, the number of the summation terms corresponding to each k is filled up to N through the second term.
When k happens to be the signal period or an integer multiple of the period, the amplitude difference function RnA minimum value occurs. Searching all amplitude difference functions R in a given respiratory or heartbeat cycle rangenWhen a plurality of minimum value points exist in the range, selecting a minimum value point position with the shortest period as the period estimation of the signal; calculating confidence through cycle lengthThe number frequency. This allows the signal period to be estimated and avoids estimating multiples of the period.
Step ten, outputting respiration heartbeat waveforms and real-time frequency estimation of the M pulse signals;
outputting the measurement results of the total M pulse signals of the section, including the respiration and heartbeat time domain waveforms separated in the step eight; and the respiration and heartbeat frequency estimated by the AMDF in the ninth step, so that the acquisition of time domain waveform and frequency information of respiration and heartbeat signals by the radar is completed.

Claims (10)

1. A millimeter wave radar-based method for detecting respiration and heartbeat of a person is characterized by comprising the following specific steps:
firstly, aiming at a target human body to be detected, selecting an FMCW radar to transmit M linear frequency modulation pulse train signals to the target human body; each pulse echo signal received by the radar comprises a human body target echo signal and clutter; after the echo signals under each pulse are subjected to deskew processing and sampling, discrete signals of all pulses form a sampling matrix;
then, performing fast Fourier transform on each row of the sampling matrix respectively to obtain a time-distance image Rp (m, n); each row of the time-distance image Rp is taken as a distance unit, and clutter of all the distance units is eliminated by using the circle center of a constellation diagram;
then, calculating the noise power of each distance unit, taking an average value as a judgment threshold, and detecting a target existing in each distance unit by adopting a unit average constant false alarm;
finally, Doppler demodulation is carried out according to the positions of targets in the distance units, and the demodulation result is reconstructed into the separated waveforms of respiration and heartbeat by using an improved EMD (empirical mode decomposition) algorithm; using a periodic short-time average amplitude difference function AMDF to perform periodic estimation on single breath or heartbeat; and finally, outputting respiration heartbeat waveforms and real-time frequency estimation of the M pulse signals.
2. The millimeter wave radar-based method for detecting respiration and heartbeat of a person according to claim 1, wherein the M linear fm pulse train signals are calculated according to the following formula:
Figure FDA0003119390540000011
tmis a slow time, representing the time at which the mth pulse is transmitted; t is tmT is the repetition period of the pulse signal; m is the number of frames of the slow time,
Figure FDA0003119390540000012
for a fast time, the propagation of the electromagnetic wave emitted by the radar is represented by the emission time tmA time of origin;
Figure FDA0003119390540000013
t is the total time; t ispIs the pulse width of the pulse; exp (j2 pi fct) is a carrier frequency signal of a linear frequency modulation signal transmitted by a radar; gamma is the frequency modulation coefficient of the chirp signal, fcIs the starting frequency of the chirp signal; and c is the speed of light.
3. The method for detecting the respiration and the heartbeat of the person based on the millimeter wave radar as claimed in claim 1, wherein the echo signal of the m-th pulse received by the radar is the superposition of a human target signal and a clutter signal:
Figure FDA0003119390540000014
wherein the echo signal of the human target
Figure FDA0003119390540000015
Comprises the following steps:
Figure FDA0003119390540000016
Atargetis the target human thoraxEcho intensity of RmThe radial distance between the fluctuation of the target human thorax and the radar under the mth pulse;
the clutter signal is represented as
Figure FDA0003119390540000017
Figure FDA0003119390540000018
NcThe number of scattering centers to generate clutter, i denotes the ith scattering center, AiIs the echo intensity, R, of the ith scattering centeriIs the radial distance of the ith scattering center from the radar.
4. The method for detecting the respiration and the heartbeat of the person based on the millimeter wave radar as claimed in claim 1, wherein the sampling matrix is formed by the following specific processes:
firstly, the received mth pulse echo signal is multiplied by the transmitted signal conjugate for deskewing, and the calculation formula is as follows:
Figure FDA0003119390540000021
then, sampling the deskewed signal, and for the discrete signal of the nth frequency domain sampling point in the mth pulse:
Figure FDA0003119390540000022
Nsrepresenting the number of samples per pulse; t issRepresents the sampling interval time;
finally, each pulse is performed by NsSub-sampling, all sampling points of all pulses together constituting M × NsThe sampling matrix of (2).
5. The millimeter wave radar-based method for detecting breathing and heartbeat of a person, according to claim 1, wherein the time-distance image Rp (m, n) is calculated by the formula:
Figure FDA0003119390540000023
n represents the nth frequency domain sample point, i.e. the nth distance unit; the time-distance image Rp at slow time is MxNsOf the matrix of (a).
6. The method for detecting the respiration and the heartbeat of the person based on the millimeter wave radar as claimed in claim 1, wherein the step of eliminating the clutter of all the distance units by using the circle center of the constellation diagram specifically comprises the following steps:
first, the slow time series x (m) is calculated, as follows:
Figure FDA0003119390540000024
then, the slow time sequence x (m) is drawn on a complex plane to form a constellation diagram, all the points are located at different positions on a circle, and a complex vector a + jb represented by a circle center coordinate (a, b) is a complex amplitude corresponding to the clutter
Figure FDA0003119390540000025
Radius is the amplitude | A corresponding to the signaltarget|；
And finally, subtracting the complex vector a + jb corresponding to the circle center coordinate from all time signals of each distance unit to eliminate clutter influence, and obtaining a time-distance image only containing a target and a noise signal.
7. The method for detecting the respiration and the heartbeat of the person based on the millimeter wave radar as claimed in claim 1, wherein the specific process of the Doppler demodulation is as follows:
first, calculating the thoracic reach of the target bodyDistance Rm；
The expression is as follows:
Rm＝R0+gr(m)+gh(m)+∑Ai·u(m-mi)
R0representing the distance between the human body target and the radar at the initial moment;
gr(m) is the waveform of the respiration,
Figure FDA0003119390540000031
Nris the number of harmonics of the breath, AriIs the amplitude of the breath; f. ofrIs the frequency of breathing; phi is ariIs the initial phase of respiration;
gh(m) is the waveform of the heartbeat,
Figure FDA0003119390540000032
Nhthe number of harmonics of the heartbeat, AhiThe amplitude of the heartbeat; f. ofhIs the frequency of the heartbeat; phi is ahiIs the initial phase of the heartbeat;
∑Ai·u(m-mi) Is the random movement of the human body during the measurement process; a. theiThe amplitude of the movement, and the positive and negative of the movement reflect the direction of the movement; u (m-m)i) Is a step function, indicating that the motion occurs at miTime of day; multiple incidental movements may occur within M pulses;
then, using the distance RmCalculating a slow time signal sequence x (m) in a distance unit where the human body is located;
expressed as:
Figure FDA0003119390540000033
finally, carrying out dislocation conjugate multiplication on the slow time signal sequence x (m), neglecting the amplitude and taking an imaginary part to obtain a demodulation result h (m):
Figure FDA0003119390540000034
the result h (m) is the first order difference of the micromotion of the human thoracic motion, including the first order difference of the respiration waveform
Figure FDA0003119390540000035
First order difference between waveform and heartbeat waveform
Figure FDA0003119390540000036
Waveform, and first order difference of random motion
Figure FDA0003119390540000037
And (4) waveform.
8. The millimeter wave radar-based method for detecting the respiration and the heartbeat of a person according to claim 1, wherein the reconstruction of the separated waveforms of the respiration and the heartbeat from the demodulation result by using an improved EMD algorithm is specifically as follows:
the EMD decomposes the signal into a series of intrinsic mode components IMF, the IMF after separation is one of respiration, heartbeat or noise signals, and each IMF is judged through a frequency spectrum: performing FFT on each IMF, searching a frequency spectrum peak value of each IMF, and if the frequency spectrum peak value falls within a respiratory or heartbeat frequency range, considering the IMF as a respiratory or heartbeat component; otherwise, the IMF is noise; and adding all IMFs belonging to the respiratory or heartbeat components, namely reconstructing a waveform by the obtained respiratory or heartbeat signal.
9. The millimeter wave radar-based method for detecting the respiration and the heartbeat of a person according to claim 1, wherein the periodic short-time average amplitude difference function AMDF is used for periodic estimation of a single respiration or heartbeat in the reconstructed waveform, and specifically comprises:
AMDF calculates signal sequence time delay shift and subtracts sequence Rn(k)
Figure FDA0003119390540000041
Wherein x (i) is the input reconstructed respiratory or heartbeat waveform sequence, length N; rnIs the amplitude difference function after subtraction, k is the number of time delay points;
when k happens to be the signal period or an integer multiple of the period, the amplitude difference function RnA minimum occurs; for respiration waveform gr(m) and heartbeat waveform gh(m) its fundamental and harmonic have the same minimum point at the respiratory and heartbeat cycles; thus, all amplitude difference functions R are searched for within a given respiratory or heartbeat cycle rangenWhen a plurality of minimum value points exist in the range, the minimum value point position with the shortest period is selected as the period estimation of the signal.
10. The millimeter wave radar-based detection method for respiratory and heartbeat of people, according to claim 1, wherein the final output of the respiratory and heartbeat waveforms and the real-time frequency estimation of the M pulse signals comprises: the separated respiratory and heartbeat time domain waveforms, and the AMDF estimated respiratory and heartbeat frequencies.
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