CN108614259A - A kind of heartbeat respiratory characteristic monitoring method based on ultra-wideband radar sensors - Google Patents
A kind of heartbeat respiratory characteristic monitoring method based on ultra-wideband radar sensors Download PDFInfo
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- CN108614259A CN108614259A CN201810411059.8A CN201810411059A CN108614259A CN 108614259 A CN108614259 A CN 108614259A CN 201810411059 A CN201810411059 A CN 201810411059A CN 108614259 A CN108614259 A CN 108614259A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/0209—Systems with very large relative bandwidth, i.e. larger than 10 %, e.g. baseband, pulse, carrier-free, ultrawideband
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/887—Radar or analogous systems specially adapted for specific applications for detection of concealed objects, e.g. contraband or weapons
- G01S13/888—Radar or analogous systems specially adapted for specific applications for detection of concealed objects, e.g. contraband or weapons through wall detection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- Physics & Mathematics (AREA)
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- General Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The present invention discloses a kind of heartbeat respiratory characteristic monitoring method based on ultra-wideband radar sensors, is related to heartbeat signal detection technique field, includes the following steps：Using the breath signal detected and heartbeat signal as echo-signal；Echo-signal is pre-processed, and selected distance door obtains one-dimensional signal；Obtained one-dimensional signal is subjected to mode decomposition with classical mode decomposition algorithm, obtains mode number K and this K specific mode；According to mode number K and this K specific mode, after carrying out mode decomposition to the one-dimensional signal in step 2 by variation mode decomposition algorithm, Breathing mode and heartbeat mode are extracted；The Breathing mode extracted and heartbeat mode are subjected to Spectrum Conversion respectively and obtain respective frequency domain information, single goal respiration rate per minute and beats can be directly obtained by frequency domain information.The present invention solves the problems, such as that current method anti-interference is poor, accuracy is low and modal overlap.
Description
Technical field
The present invention relates to heartbeat signal detection technique field more particularly to a kind of heartbeats based on ultra-wideband radar sensors
Respiratory characteristic monitoring method.
Background technology
The ultra-wideband radio equipment code of Federal Communications Commission (FCC) publication defines ultra-broadband signal, i.e.,
Absolute bandwidth is more than the signal of 500MHz.There are two types of ultra-broadband signal main signal forms：Burst pulse carrier format UWB and modulation
Carrier format UWB.Bioradar has more and more researchs in human body physical sign context of detection.Whether in common health control,
Or the health status of the detection after natural calamity occurs to life, heartbeat and breathing is all particularly important.
The existing sign detection method to human body has contact and two kinds contactless.Contact method needs electrode or biography
Sensor and human contact acquire signal, there is certain application limitation, for example, when skin injury or other do not allow contact to survey
The case where amount.Non-contact method has the modes such as laser, infrared, radar signal.But it when natural calamity occurs, often has
Many ruins stop infrared and laser propagation, cannot achieve the purpose that measure human body physical sign.It is to apply model based on radar signal
Most wide one kind is enclosed, and ultra-wideband radar signal is even more to have the advantages such as penetration power is strong, transmission power is low, resolution of ranging is high.
Therefore there are also scholars to carry out sign detection to human body using ultra-wideband radar signal these years.But because radar signal
More clutter and noise are had, the harmonic wave also having between breathing and heartbeat influences, so detected human posture and fortune
Dynamic state can influence the accuracy rate of detection.
It is existing that mainly to have two parts, one kind to the untouchable radar detection algorithm of human body be the signal based on Fourier transformation
Processing method, one kind are classical mode decomposition algorithm (EMD algorithms).
Signal processing algorithm based on Fourier transformation is to use a series of means of filtering, extraction breathing, heartbeat waveform,
And frequency domain is fourier transformed by correlation, it is breathed, the frequency domain character of heartbeat.This method can only obtain global characteristics,
It is unable to get the instantaneous situation of human body physical sign.And this method requires the pure property of original signal very high：When clutter and make an uproar
When sound is more, it is difficult to extract desired signal feature.
Empirical mode decomposition algorithm (EMD algorithms) be according to the local extremum correlated characteristic of signal come by signal decomposition at one
The algorithm of serial mode, key are the decomposition of mode, and the mode after decomposition meets two conditions：(a) extreme value number and zero
Number is equal or one (b) any point of most differences at maximum and the envelope average value of minimum be zero.It is that cycle is calculated
Method, to the last a sequence is until monotonic function can not decompose again.EMD algorithms than the algorithm based on Fourier transformation more
Add and is suitable for breathing, this non-stationary signal of heartbeat, but its shortcomings of there are end effect and modal overlaps.
Invention content
It is an object of the invention to：To solve the existing heartbeat respiratory characteristic monitoring side based on ultra-wideband radar sensors
The problem of method anti-interference is poor, accuracy is low and modal overlap, the present invention provide a kind of based on ultra-wideband radar sensors
Heartbeat respiratory characteristic monitoring method.
Technical scheme is as follows：
A kind of heartbeat respiratory characteristic monitoring method based on ultra-wideband radar sensors, includes the following steps：
Step 1：Breath signal and heartbeat signal of the single goal under one or more states are detected, and is exhaled what is detected
Signal and heartbeat signal are inhaled as echo-signal；
Step 2：Echo-signal is removed clutter and noise pretreatment, and selected distance door obtains one-dimensional signal,
See Fig. 1；
Step 3：The one-dimensional signal obtained in step 2 is subjected to mode decomposition with classical mode decomposition algorithm, obtains mode
Number K and this K specific mode, are specifically shown in Fig. 2；
Step 4：According to mode number K and this K specific mode, by variation mode decomposition algorithm to one in step 2
After dimensional signal carries out mode decomposition, Breathing mode and heartbeat mode are extracted；
Step 5：The Breathing mode extracted and heartbeat mode are subjected to Spectrum Conversion respectively and obtain respective frequency domain information,
Single goal respiration rate per minute and beats can be directly obtained by frequency domain information；Meanwhile respectively to Breathing mode and
Heartbeat mode carries out time-frequency conversion and obtains respective Time-Frequency Information, and the real-time breathing that single goal is directly obtained from Time-Frequency Information is special
It seeks peace heartbeat feature.It is local quantity by Time-Frequency Information, is real time information, can reflects that the breathing of detected person, heartbeat are special in real time
Sign.
Specifically, the state includes stationary state, arms swing state, the state that remains where one is, forward-reverse state.
Preferably, in the step 2, the range gate when range gate of selection is signal amplitude maximum.
Specifically, the step 3 the specific steps are：
Step 3.1：Find out all local minizing points and the Local modulus maxima of one-dimensional signal s (t)；
Step 3.2：Cubic spline interpolation is used to the minimum point and maximum point that are obtained in step 3.1, is obtained up and down
Envelope Smax(i)(t) and Smin(i)(t), wherein Smax(i)(t) it is the envelope of maximum point, Smin(i)(t) it is the packet of minimum point
Network；
Step 3.3：Seek the mean value e of lower envelopei(t), function is：
Step 3.4：Ask signal s (t) and ei(t) difference Di(t)；Function is：
Di(t)=s (t)-ei(t) (2)
Step 3.5：Use Di(t) s (t) in replacing and repetition step:3.1-3.4；Work as Di(t) meet two items of mode
Part, i.e. (a) extreme value number and zero number it is equal or it is most difference one (b) any point at maximum and minimum envelope
Average value is zero, is stopped at this time, D at this timei(t) it is then first mode.
Step 3.6：First mode acquired in step 3.5 will be rejected in former one-dimensional signal s (t) repeats step 3.1-
3.5, then second mode can be obtained；
Step 3.7：Step 3.5 and 3.6 is repeated until ei(t) be monotonic function when, iteration terminates, and obtains the numerical value of K.
Specifically, the step 4 is as follows：
Step 4.1：Construct variation mode；
(3) formulaIt indicates each mode u of constructionk(t) Hilbert transform is carried out to be parsed
Signal, δ (t) are impulse signal；Indicate the analytic signal that will be obtained and mode respectively center frequency
Rate ωkMixing, carrys out mode frequency spectrum shift to base band with this, obtains demodulated signal, finally seek L to demodulated signal2Norm squared sum
Minimum value,Be all mode and.
Step 4.2：Solve variation mode；
Penalty coefficient α and Lagrangian λ (t) are introduced to (3) formula in step 4.1, the expression formula of extension is：
Constrained (3) formula is extended to non-binding (4) formula,For echo-signal s (t) and mode
SummationThe L of the two difference2Norm squared,For constraint function；Recycle alternating direction multiplier
Method acquires the mode u after the VMD in formula (4) is decomposedk(t)。
After adopting the above scheme, beneficial effects of the present invention are as follows:
(1) VMD algorithms are suitable for nonlinear and non local boundary value problem, and the field signal frequency applied in the past is higher, and the order of magnitude can
Up to it is tens of it is thousands of differ, and signal frequency difference is larger, therefore harmonic problem is smaller.And breath signal and heartbeat signal frequency it
Between differ less than 1HZ, there are serious harmonic waves in one-dimensional signal.VMD algorithms till now, are only applied to mechanical breakdown inspection from proposition
The fields such as survey, the present invention for the first time detect VMD algorithms applied to non-contact type human body sign, and have evaded the setting of VMD algorithms
The drawback of mode number inaccuracy realizes the human body physical sign detection of high accuracy.The research of EMD algorithms has endpoint
End effect is not present in effect, VMD algorithms.
(2) EMD algorithms have the drawbacks of modal overlap and end effect, therefore are first sensed to ULTRA-WIDEBAND RADAR by EMD algorithms
The collected data of device are handled, and after obtaining mode number, reapply VMD algorithms.It in this way can be in detection breathing, heartbeat
The shortcomings that evading falling algorithm can be breathed, the frequency domain character and time-frequency characteristics of heartbeat.
(3) it is different from EMD algorithms, VMD algorithms are a kind of methods of onrecurrent screening mode, and have theoretical foundation to prop up
Support, there is better noise robustness.
Description of the drawings
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the present invention
Example, for those of ordinary skill in the art, without creative efforts, can also obtain according to these attached drawings
Obtain other attached drawings.Shown in attached drawing, above and other purpose of the invention, feature and advantage will be more clear.In whole
Identical reference numeral indicates identical part in attached drawing.Actual size equal proportion scaling is not pressed deliberately draws attached drawing, emphasis
It is that the purport of the present invention is shown.
The one-dimensional signal obtained in step 2 in Fig. 1 embodiment of the present invention 1；
Fig. 2 is the specific mode decomposed in step 3 in the embodiment of the present invention 1；
Fig. 3 is the respiratory characteristic that step 5 obtains in the embodiment of the present invention 1；
Fig. 4 is the heartbeat feature that step 5 obtains in the embodiment of the present invention 1；
Fig. 5 is the time-frequency figure that step 5 obtains in the embodiment of the present invention 1.
Specific implementation mode
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
The every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
Before the method for carrying out the present invention, without query, ULTRA-WIDEBAND RADAR detecting system, ultra-wide are first put up
Band radar sensing system includes that the single base station radar sensor module of PulsOn 440, the infrared measurement machine, quasi- Yagi spark gap broadband are flat
Surface antenna and some auxiliary accessories.A kind of heartbeat respiratory characteristic monitoring method packet based on ultra-wideband radar sensors of the present invention
Include following steps：
Step 1：Breath signal and heartbeat signal of the single goal under one or more states are detected, and is exhaled what is detected
Signal and heartbeat signal are inhaled as echo-signal；The state include stationary state, arms swing state, the state that remains where one is,
Forward-reverse state；When natural calamity occurs, detecting state is mostly stationary state.
Step 2：Echo-signal is removed clutter and noise pretreatment, and selected distance door obtains one-dimensional signal,
See Fig. 1；Range gate herein is range gate when choosing signal amplitude maximum.
Step 3：The one-dimensional signal obtained in step 2 is subjected to mode decomposition with classical mode decomposition algorithm, obtains mode
Number K and this K specific mode, are specifically shown in Fig. 2；Step 3 the specific steps are：
Step 3.1：Find out all local minizing points and the Local modulus maxima of one-dimensional signal s (t)；
Step 3.2：Cubic spline interpolation is used to the minimum point and maximum point that are obtained in step 3.1, is obtained up and down
Envelope Smax(i)(t) and Smin(i)(t), wherein Smax(i)(t) it is the envelope of maximum point, Smin(i)(t) it is the packet of minimum point
Network；
Step 3.3：Seek the mean value e of lower envelopei(t), function is：
Step 3.4：Ask signal s (t) and ei(t) difference Di(t)；Function is：
Di(t)=s (t)-ei(t) (2)
Step 3.5：Use Di(t) s (t) in replacing and repetition step:3.1-3.4；Work as Di(t) meet two items of mode
Part, i.e. (a) extreme value number and zero number it is equal or it is most difference one (b) any point at maximum and minimum envelope
Average value is zero, is stopped at this time, D at this timei(t) it is then first mode.
Step 3.6：First mode acquired in step 3.5 will be rejected in former one-dimensional signal s (t) repeats step 3.1-
3.5, then second mode can be obtained；
Step 3.7：Step 3.5 and 3.6 is repeated until ei(t) be monotonic function when, iteration terminates, and obtains the numerical value of K.
Step 4：According to mode number K and this K specific mode, by variation mode decomposition algorithm to one in step 2
After dimensional signal carries out mode decomposition, Breathing mode and heartbeat mode are extracted；
The step 4 is as follows：
Step 4.1：Construct variation mode；
(3) formulaIt indicates each mode u of constructionk(t) Hilbert transform is carried out to be parsed
Signal, δ (t) are impulse signal；Indicate the analytic signal that will be obtained and mode respectively center
Frequencies omegakMixing, carrys out mode frequency spectrum shift to base band with this, obtains demodulated signal, finally seek L to demodulated signal2Norm squared and
Minimum value,Be all mode and；
Step 4.2：Solve variation mode；
Penalty coefficient α and Lagrangian λ (t) are introduced to (3) formula in step 4.1, the expression formula of extension is：
Constrained (3) formula is extended to non-binding (4) formula,For echo-signal s (t) and mode
SummationThe L of the two difference2Norm squared,For constraint function；Recycle alternating direction multiplier
Method acquires the mode u after the VMD in formula (2) is decomposedk(t)。
Step 4.3：Arrange parameter：
α：Penalty coefficient can influence modal bandwidth, consider sample frequency and convergence rate, be set as 2500；
tau：Because noise is stronger, so taking 0
init：Centre frequency is initialized, takes 0
DC：In order to update first centre frequency, 1 is taken,
tol：The condition of convergence takes 10-5；
Step 5：The Breathing mode extracted and heartbeat mode are subjected to Spectrum Conversion respectively and obtain respective frequency domain information,
Single goal respiration rate per minute and beats can be directly obtained by frequency domain information；Meanwhile respectively to Breathing mode and
Heartbeat mode carries out time-frequency conversion and obtains respective Time-Frequency Information, and the real-time breathing that single goal is directly obtained from Time-Frequency Information is special
It seeks peace heartbeat feature.Time-Frequency Information is local quantity, is real time information, can reflect breathing, the heartbeat feature of detected person in real time.
In present embodiment, Spectrum Conversion is converted using Fourier transform, time-frequency conversion using Xi Teer.
VMD algorithms are suitable for nonlinear and non local boundary value problem, and the field signal frequency applied in the past is higher, and the order of magnitude is up to number
Ten it is thousands of differ, and signal frequency difference is larger, therefore harmonic problem is smaller.And phase between breath signal and heartbeat signal frequency
Difference is less than 1HZ, and there are serious harmonic wave in one-dimensional signal, in step 4.3, rational parameter is arranged in VMD algorithms, is extracted and exhales
It inhales, heartbeat, is not influenced from harmonic.VMD algorithms till now, are only applied to the fields such as mechanical fault detection from proposition, and the present invention is first
It is secondary to detect VMD algorithms applied to non-contact type human body sign, and evaded the disadvantage of VMD algorithms setting mode number inaccuracy
End realizes the human body physical sign detection of high accuracy.
EMD algorithms have the drawbacks of modal overlap and end effect, therefore are first adopted to ultra-wideband radar sensors by EMD algorithms
The data collected are handled, and after obtaining mode number, reapply VMD algorithms.It can evade in this way in detection breathing, heartbeat
The shortcomings that falling algorithm can be breathed, the frequency domain character and time-frequency characteristics of heartbeat.
Different from EMD algorithms, VMD algorithms are a kind of methods of onrecurrent screening mode, and have theoretical foundation to support, and are had
Better noise robustness.
Embodiment 1
Include the following steps：
Step 1：Detection single goal in static, arms swing, remain where one is, the breathing letter under the various states such as forward-reverse
Number and heartbeat signal, and using the breath signal detected and heartbeat signal as echo-signal；
Step 2：Echo-signal is removed clutter and noise pretreatment, and selected distance door obtains one-dimensional signal,
See Fig. 1；Range gate herein is range gate when choosing signal amplitude maximum.
Step 3：The one-dimensional signal obtained in step 2 is subjected to mode decomposition with classical mode decomposition algorithm, obtains mode
Number K and this K specific mode, are specifically shown in Fig. 2；Step 3 the specific steps are：By the one-dimensional signal mode in step 2 point
Solution, classical mode decomposition method obtains 6 intrinsic mode and 1 residual after handling one-dimensional signal, thus may determine that one-dimensional letter
Number mode number be 6.
Step 4：According to mode number 6 and this 6 specific mode, by variation mode decomposition algorithm to one in step 2
After dimensional signal carries out mode decomposition, Breathing mode and heartbeat mode are extracted.
The step 4 is as follows：
Step 4.1：Construct variation mode；
(1) formulaIt indicates first by each mode uk(t) it carries out Hilbert transform and obtains parsing letter
Number, δ (t) is impulse signal；Indicate the analytic signal that will be obtained and mode respectively centre frequency
ωkMixing, carrys out mode frequency spectrum shift to base band with this, obtains demodulated signal, finally seek L to demodulated signal2Norm squared sum is most
Small value,Be all mode and；
Step 4.2：Solve variation mode；
Penalty coefficient α and Lagrangian λ (t) are introduced to (1) formula in step 4.1, the expression formula of extension is：
Constrained (1) formula is extended to non-binding (2) formula,For echo-signal s (t) and mode
SummationThe L of the two difference2Norm squared,For constraint function；Recycle alternating direction multiplier
Method acquires the mode u after the VMD in formula (2) is decomposedk(t)。
Specifically, VMD algorithms are to use Matlab softwares, the parameter being arranged in VMD algorithms as follows：
α：Penalty coefficient can influence modal bandwidth, consider sample frequency and convergence rate, be set as 2500；
tau：Because noise is stronger, so taking 0；
init：Centre frequency is initialized, takes 0；
DC：In order to update first centre frequency, 1 is taken,
tol：The condition of convergence takes 10-5；
Step 5：The Breathing mode extracted and heartbeat mode are subjected to Spectrum Conversion respectively and obtain respective frequency domain information,
As shown in Figure 3 and Figure 4, single goal respiration rate per minute and beats can be directly obtained by frequency domain information；Meanwhile point
It is other that respective Time-Frequency Information is obtained to Breathing mode and the progress Hilbert transform of heartbeat mode, as shown in figure 5, from Time-Frequency Information
On directly obtain the real-time respiratory characteristic and heartbeat feature of single goal.Time-Frequency Information is local quantity, is real time information, can be real-time
Reflect breathing, the heartbeat feature of detected person.
While carrying out step 1 and step 5, the ECG monitor data of human body can also be acquired, in step 1 and step
After 5 carry out, ULTRA-WIDEBAND RADAR detection data and traditional ECG monitor data are compared, error is obtained.The present invention is super
While the non-contact detecting of broadband, to it is static when human body carried out hospital's cardiac monitoring profession detect, can obtain per minute
Breathing error less than 1 time, heartbeat be less than 6 times, error belongs to normal range (NR).
Existing mode setting：When the signal that the signal of processing is simulation, when not being that true measurement obtains, it is equivalent to oneself
It is aware of the composition of signal in advance, the noise of signal, clutter etc. have all been known, has been equivalent to and knows mode number.
Or processing is compared with purified signal：Such as the actual signal of rectification circuit, there is no more serious harmonic waves, miscellaneous
Wave, noise problem, therefore can not consider these problems when setting mode number.
And the one-dimensional signal that the present invention is handled therefore cannot neglect because being untouchable detection and there are more serious harmonic waves
Depending on harmonic wave, clutter, noise problem, these problems are present in environment and intrinsic nor known.So EMD is needed to calculate
Method obtains mode number in advance.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Belong to those skilled in the art in the technical scope disclosed by the present invention, the change or replacement that can be readily occurred in all are answered
It is included within the scope of the present invention.
Claims (5)
1. a kind of heartbeat respiratory characteristic monitoring method based on ultra-wideband radar sensors, which is characterized in that include the following steps：
Step 1：Breath signal and heartbeat signal of the single goal under one or more states are detected, and the breathing detected is believed
Number and heartbeat signal as echo-signal；
Step 2：Echo-signal is removed clutter and noise pretreatment, and selected distance door obtains one-dimensional signal；
Step 3：The one-dimensional signal obtained in step 2 is subjected to mode decomposition with classical mode decomposition algorithm, obtains mode number
K；
Step 4：According to mode number K and this K specific mode, by variation mode decomposition algorithm to the one-dimensional letter in step 2
After number carrying out mode decomposition, Breathing mode and heartbeat mode are extracted；
Step 5：The Breathing mode extracted and heartbeat mode are subjected to Spectrum Conversion respectively and obtain respective frequency domain information, by frequency
Domain information can directly obtain single goal respiration rate per minute and beats；Meanwhile respectively to Breathing mode and heartbeat
Mode carry out time-frequency conversion obtain respective Time-Frequency Information, directly obtained from Time-Frequency Information single goal real-time respiratory characteristic and
Heartbeat feature.
2. special according to a kind of heartbeat respiratory characteristic monitoring method based on ultra-wide band radar sensor that right is wanted to state required by 1
Sign is, in the step 1, the state includes stationary state, arms swing state, the state that remains where one is, forward-reverse shape
State.
3. special according to a kind of heartbeat respiratory characteristic monitoring method based on ultra-wide band radar sensor that right is wanted to state required by 1
Sign is, in the step 2, the range gate when range gate of selection is signal amplitude maximum.
4. a kind of heartbeat respiratory characteristic monitoring method based on ultra-wide band radar sensor according to claim 1, special
Sign is, the step 3 the specific steps are：
Step 3.1：Find out all local minizing points and the Local modulus maxima of one-dimensional signal s (t)；
Step 3.2：Cubic spline interpolation is used to the minimum point and maximum point that are obtained in step 3.1, obtains lower envelope
Smax(i)(t) and Smin(i)(t), wherein Smax(i)(t) it is the envelope of maximum point, Smin(i)(t) it is the envelope of minimum point；
Step 3.3：Seek the mean value e of lower envelopei(t), function is：
Step 3.4：Ask signal s (t) and ei(t) difference Di(t)；Function is：
Di(t)=s (t)-ei(t) (2)
Step 3.5：Use Di(t) s (t) in replacing and repetition step:3.1-3.4；Work as Di(t) meet two conditions of mode, i.e.,
(a) extreme value number and zero number is equal or one (b) any point of most differences at maximum and minimum envelope it is average
Value is zero, is stopped at this time, D at this timei(t) it is then first mode.
Step 3.6：First mode acquired in step 3.5 will be rejected in former one-dimensional signal s (t) repeats step 3.1-3.5,
Second mode then can be obtained；
Step 3.7：Step 3.5 and 3.6 is repeated until ei(t) be monotonic function when, iteration terminates, and obtains the numerical value of K.
5. a kind of heartbeat respiratory characteristic monitoring method based on ultra-wide band radar sensor according to claim 1-3,
It is characterized in that, the step 4 is as follows：
Step 4.1：Construct variation mode；
(3) formulaIt indicates each mode u of constructionk(t) it carries out Hilbert transform and obtains parsing letter
Number, δ (t) is impulse signal；Indicate the analytic signal that will be obtained and mode respectively centre frequency
ωkMixing, carrys out mode frequency spectrum shift to base band with this, obtains demodulated signal, finally seek L to demodulated signal2Norm squared sum is most
Small value,Be all mode and；
Step 4.2：Solve variation mode；
Penalty coefficient α and Lagrangian λ (t) are introduced to (3) formula in step 4.1, the expression formula of extension is：
Constrained (3) formula is extended to non-binding (4) formula,For echo-signal s (t) and mode summationThe L of the two difference2Norm squared,For constraint function；Alternating direction multipliers method is recycled to ask
Obtain the mode u after the VMD in formula (3) is decomposedk(t)。
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| CN110575166A (en) * | 2019-09-30 | 2019-12-17 | 北京信息科技大学 | Method and device for time-frequency analysis of human electroencephalogram signals |
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