CN113009476B - Signal extraction method and system based on frequency modulation continuous wave radar - Google Patents
Signal extraction method and system based on frequency modulation continuous wave radar Download PDFInfo
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- 238000000605 extraction Methods 0.000 title claims abstract description 25
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- 210000000779 thoracic wall Anatomy 0.000 claims abstract description 11
- 238000007781 pre-processing Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 22
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- G—PHYSICS
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- 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
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
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- A—HUMAN NECESSITIES
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- 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
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- G01S7/35—Details of non-pulse systems
- G01S7/352—Receivers
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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
- G01S7/35—Details of non-pulse systems
- G01S7/352—Receivers
- G01S7/354—Extracting wanted echo-signals
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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
- G01S7/411—Identification of targets based on measurements of radar reflectivity
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- 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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Abstract
The invention relates to the technical field of signal processing, in particular to a signal extraction method and a system based on a frequency modulation continuous wave radar, which are used for preprocessing chest wall mechanical motion signals of a human body to be detected, received by the frequency modulation continuous wave radar to obtain echo signals, wherein the echo signals comprise the following components: a respiration signal, a heartbeat signal, a higher harmonic component of the respiration signal, a higher harmonic component of the heartbeat signal, and a noise signal; filtering the signal which does not contain noise in the echo signal to obtain a filtering signal, determining the frequency-power spectrum of the filtering signal in an estimated frequency interval, and separating according to the spectrogram of the frequency-power spectrum peak value to obtain a heartbeat signal and a respiratory signal; the method for estimating the signal frequency by matching with the power spectrum can achieve the aim of retaining the respiratory signal and the heartbeat signal components and filtering other interference components, thereby extracting the heartbeat signal and the respiratory signal in a complex noise environment.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a signal extraction method and system based on a frequency modulation continuous wave radar.
Background
The millimeter wave frequency modulation continuous wave radar (Frequency Modulated Continuous Wave Radar, FMCW) is a non-contact detection method, can realize all-weather and all-space detection because the radar is not interfered by weather and detection environment shielding objects, has high working frequency band, can capture weak breathing and heartbeat signals, and has high detection precision. When the millimeter wave frequency modulation continuous wave radar is used for monitoring the sign signals, the most important link is how to separate and extract the breathing information and the heartbeat information from the echo signals received by the millimeter wave frequency modulation continuous wave radar.
The amplitude of the thoracic motion caused by the respiratory motion of the human body in a static state is larger, the respiratory signal energy is stronger, and the amplitude of the thoracic micro motion caused by the heartbeat motion is relatively smaller, and the energy is also relatively weaker. And frequency analysis of the echo signals shows that: the higher harmonic component of the respiratory signal and the heartbeat signal have overlapping portions in the frequency spectrum. Under the condition that the frequency spectrums of the two signals are close and the energy difference distance is large, the weak heartbeat signal is easily covered by the breathing signal; in addition, the millimeter wave frequency modulation continuous wave radar has a signal wavelength between the microwave and the far infrared wave, and the displacement of a fraction of a millimeter can be detected, which also causes a plurality of tiny disturbances in the measuring process to be detected. Weak heartbeat and respiration signals are interspersed in a complex noise environment, which makes it difficult to extract heartbeat and respiration signals from echo signals.
Disclosure of Invention
The invention provides a signal extraction method and a system based on a frequency modulation continuous wave radar, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
In order to achieve the above object, the present invention provides the following technical solutions:
a signal extraction method based on a frequency modulated continuous wave radar, the method comprising the steps of:
step 100, preprocessing a chest wall mechanical motion signal of a human body to be detected, which is received by a frequency modulation continuous wave radar, to obtain an echo signal, wherein the echo signal comprises: a respiration signal, a heartbeat signal, a higher harmonic component of the respiration signal, a higher harmonic component of the heartbeat signal, and a noise signal;
step 200, filtering the signal without noise in the echo signal to obtain a filtered signal, determining a frequency-power spectrum of the filtered signal in an estimated frequency interval, and separating according to the spectrogram of the frequency-power spectrum peak value to obtain a heartbeat signal and a respiratory signal.
Further, the step S200 includes:
step S210, determining an estimated frequency interval of the echo signal, and setting n frequency points at equal intervals in the estimated frequency interval;
step S220, selecting a frequency point from the estimated frequency interval as an expected frequency point;
step S230, determining a frequency vector matrix of a desired frequency point, and incorporating the frequency vector matrix into a desired subspace;
step S240, determining a difference set between the estimated frequency interval and the selected expected frequency point, and taking a frequency vector matrix of the difference set as an interference subspace;
step S250, constructing an oblique projection operator of the echo signal, and carrying out oblique projection filtering on a signal which does not contain noise in the echo signal according to the oblique projection operator to obtain a filtered signal;
step S260, calculating the power of the filtered signal, and weighting the power P by adopting an adaptive weight vector to obtain the weighted power of the filtered signal;
step S270, determining whether to traverse the estimated frequency interval, if not, selecting the rest frequency points from the estimated frequency interval as expected frequency points, and jumping to step S230; if yes, go to step S280;
step S280, determining a frequency-power spectrum of the estimated frequency interval, and separating to obtain a heartbeat signal and a respiratory signal according to a spectrogram of the frequency-power spectrum peak value.
Further, the range of the estimated frequency range is [0.10hz,3.00hz ], and n=290.
Further, the calculation formula of the oblique projection operator is as follows: e (E) A(θ)|A(V) ＝A(θ)/[A(θ) H *A(V)*A(θ)]*A(θ) H * A (θ), where θ is a desired frequency point, a (θ) is a frequency vector matrix of the desired frequency point, V is a difference set, and a (V) is a frequency vector matrix of the difference set;
the calculation formula of the filtering signal is as follows: y' =e A(θ)|A(V) *Y F F is the estimated frequency interval, Y F Noise-free echo signals for the oblique projection operatorThe signal, Y', is the filtered signal.
Further, the power P of the filtered signal Y' is calculated by the following formula:
wherein M is a signal Y without noise in the echo signal F Data length of->Signal Y representing no noise in echo signal F P is the power of the filtered signal.
Further, the adaptive weight vector calculation formula is: u=1/trace (E A(θ)|A(V) H *E A(θ)|A(V) ) U is the self-adaptive weight vector; the self-adaptive weight vector calculation formula is as follows: p '=u×p, P' is the weighted power of the filtered signal.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a method of frequency modulated continuous wave radar based signal extraction as claimed in any one of the preceding claims.
A frequency modulated continuous wave radar based signal extraction system comprising:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement a frequency modulated continuous wave radar-based signal extraction method as defined in any one of the preceding claims.
The beneficial effects of the invention are as follows: the invention discloses a signal extraction method and a system based on a frequency modulation continuous wave radar.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a signal extraction method based on a frequency modulated continuous wave radar in an embodiment of the invention;
FIG. 2 is a flowchart of step S200 in an embodiment of the present invention;
fig. 3 is a schematic diagram of simulation of echo signals in an embodiment of the present invention.
Detailed Description
The conception, specific structure, and technical effects produced by the present disclosure will be clearly and completely described below in connection with the embodiments and the drawings to fully understand the objects, aspects, and effects of the present disclosure. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
The following describes the principle of radar measurement of human body sign data (respiration rate, heart rate, variability of respiration rate, and variability of heart rate):
the respiration and heartbeat of the human body can cause the tiny displacement of the chest wall, wherein the displacement amplitude caused by the respiration is 1mm to 12mm, and the displacement amplitude caused by the heartbeat is 0.01mm to 0.5mm. Both respiratory-induced displacement and heartbeat-induced displacement are quasi-periodic mechanical movements, with respiratory frequencies typically ranging from 0.1Hz to 0.6Hz and heartbeat frequencies ranging from 0.8Hz to 2.5Hz. The small displacement of the chest wall can generate modulation action on the radar signal, and the echo signal generated by modulation is received by the radar and is processed. The frequency of respiratory and cardiac movements can be measured in a non-contact manner.
The following is a specific technical scheme provided by the invention:
referring to fig. 1, as shown in fig. 1, a signal extraction method based on a frequency modulation continuous wave radar includes the following steps:
step 100, preprocessing a chest wall mechanical motion signal of a human body to be detected, which is received by a frequency modulation continuous wave radar, to obtain an echo signal, wherein the echo signal comprises: a respiration signal, a heartbeat signal, a higher harmonic component of the respiration signal, a higher harmonic component of the heartbeat signal, and a noise signal;
specifically, a frequency modulation continuous wave radar is adopted to emit electromagnetic wave signals to a human body to be detected, and a chest wall mechanical motion signal returned by the human body to be detected is received and detected; then, signal sampling and clutter filtering are carried out on the chest wall mechanical motion signals, and preprocessing operations such as extracting distance units of the chest wall are carried out;
the pretreatment comprises the following steps: sampling chest wall mechanical motion signals, processing the sampled echo signals by using a multiple signal classification algorithm to obtain a high-resolution range profile, and performing chest wall position estimation, pre-whitening treatment, I/Q channel imbalance compensation and phase unwrapping on the high-resolution range profile.
Step 200, filtering the signal without noise in the echo signal to obtain a filtered signal, determining a frequency-power spectrum of the filtered signal in an estimated frequency interval, and separating according to the spectrogram of the frequency-power spectrum peak value to obtain a heartbeat signal and a respiratory signal.
In the prior art, one difficulty in vital sign detection by using a biological radar is that a heartbeat signal is separated from an echo signal, and the method for signal frequency estimation by matching with a power spectrum can achieve the purpose of retaining the components of expected signals (respiratory signals and heartbeat signals) and filtering other interference components, so that the heartbeat signals and respiratory signals are extracted in a complex noise environment.
Referring to fig. 2, in a modified embodiment, the step S200 includes:
step S210, determining an estimated frequency interval of the echo signal, and setting n frequency points at equal intervals in the estimated frequency interval;
in a specific embodiment, the range of the estimated frequency range is [0.10Hz,3.00Hz ]; the estimated frequency interval F is equally spaced to set 290 frequency points, the distance between every two adjacent frequency points is 0.01Hz, the estimated frequency interval is equally spaced to be divided into 290 parts (the more the frequency interval points are set, the higher the frequency resolution is), namely the dividing interval of the estimated frequency interval is 0.01Hz, and F= [0.10,0.11,0.12, …,2.99,3.00].
In one embodiment, the mathematical model of the echo signal is expressed as: y=y F +N＝A*S+N；
Wherein Y represents an echo signal, Y F A signal indicating that the echo signal does not contain noise, and N indicates a noise signal;
Y F ＝A*S，is a normalized frequency vector matrix of the estimated frequency interval F, S represents a complex envelope matrix of the echo signal, a represents a Fourier basis vector in the frequency domain, and F= [ F ] 1 ，f 2 ，f 3 …，f n ]，f 1 ，f 2 ，f 3 …，f n N frequency points which are arranged at equal intervals are represented;
in this embodiment, it is assumed that the echo signal is composed of two parts, namely noise and other signals, and the noise signal is a stationary gaussian white noise signal with zero mean value. Wherein Y ε C, C represents the complex domain.
Step S220, selecting a frequency point from the estimated frequency interval as an expected frequency point;
step S230, determining a frequency vector matrix of a desired frequency point, and incorporating the frequency vector matrix into a desired subspace;
step S240, determining a difference set between the estimated frequency interval and the selected expected frequency point, and taking a frequency vector matrix of the difference set as an interference subspace (zero space);
step S250, constructing an oblique projection operator of the echo signal, and carrying out oblique projection filtering on a signal which does not contain noise in the echo signal according to the oblique projection operator to obtain a filtered signal;
in one embodiment, the calculation of the oblique projection operatorThe calculation formula is as follows: e (E) A(θ)|A(V) ＝A(θ)/[A(θ) H *A(V)*A(θ)]*A(θ) H * A (θ), where θ is a desired frequency point, A (θ) is a frequency vector matrix of the desired frequency point, V is a difference set, A (V) is a frequency vector matrix of the difference set, (. Cndot.) is a frequency vector matrix of the difference set H Representing the conjugate transpose operation, A (θ) H A conjugate transpose matrix representing a (θ);
the calculation formula of the filtering signal is as follows: y' =e A(θ)|A(v) *Y F F is the estimated frequency interval, Y F And Y' is a filtering signal for the signals without noise in the echo signals.
Step S260, calculating the power of the filtered signal, and weighting the power P by adopting an adaptive weight vector to obtain the weighted power of the filtered signal;
in one embodiment, the power P of the filtered signal Y' is calculated by the following formula:
wherein M is a signal Y without noise in the echo signal F Data length of->Signal Y representing no noise in echo signal F P is the power of the filtered signal. E (E) A(θ)|A(V) H Representation E A(θ)|A(V) Is a complex matrix of the matrix.
In one embodiment, the adaptive weight vector calculation formula is: u=1/trace (E A(θ)|A(V) H *E A(θ)|A(V) ) U is the self-adaptive weight vector; the self-adaptive weight vector calculation formula is as follows: p '=u×p, P' is the weighted power of the filtered signal.
In the step, the adaptive weight vector is adopted to adjust the oblique projection operator, so that the power values of two adjacent signals on the frequency can be balanced, and the problem that the power peaks of the heartbeat signal and the respiratory signal on the power spectrum are overlapped and are difficult to separate due to the fact that the weak signal power is covered by the strong signal is avoided.
Step S270, determining whether to traverse the estimated frequency interval, if not, selecting the rest frequency points from the estimated frequency interval as expected frequency points, and jumping to step S230; if yes, go to step S280;
step S280, determining a frequency-power spectrum of the estimated frequency interval, and separating to obtain a heartbeat signal and a respiratory signal according to a spectrogram of the frequency-power spectrum peak value.
In this embodiment, on the premise of knowing the frequency ranges of the respiratory signal and the heartbeat signal, the interference signal (the higher harmonic component of the respiratory signal and the higher harmonic component of the heartbeat signal) in the frequency range is removed, the heartbeat signal and the respiratory signal are separated from the spectrogram of the frequency-power spectrum peak value, the frequencies of the two are obtained, and the unit is converted into: times/min.
Referring to fig. 3, to verify the feasibility of extracting respiratory and heartbeat signals from echo signals, applicant designed and performed simulation experiments on MATLAB software platform.
Firstly, an echo signal received by a frequency modulation continuous wave radar is collected, wherein the echo signal comprises a respiration signal, a heartbeat signal, a fifth-order higher harmonic component of the respiration signal and a Gaussian white noise signal. The fundamental frequencies of the respiration signal and the heartbeat signal are respectively 0.3Hz and 1.3Hz, the corresponding respiration frequency is 18 times/min, and the heartbeat frequency is 78 times/min. Then, software simulation is carried out by adopting the method provided by the application, finally, the respiratory signal and the heartbeat signal are successfully extracted, the simulation experiment result accords with the expected design, and the fact that the research scheme to be adopted by the patent is feasible in the simulation link is proved.
Corresponding to the method of fig. 1, an embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, implements the steps of the signal extraction method based on a fm continuous wave radar according to any of the embodiments above.
Corresponding to the method of fig. 1, the embodiment of the invention further provides a signal extraction system based on the frequency modulation continuous wave radar, which comprises:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement a frequency modulated continuous wave radar based signal extraction method as described in any one of the embodiments above.
The content in the method embodiment is applicable to the system embodiment, the functions specifically realized by the system embodiment are the same as those of the method embodiment, and the achieved beneficial effects are the same as those of the method embodiment.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, digital-Signal-Processor (DSP), application-Specific-Integrated-Circuit (ASIC), field-Programmable-Gate Array (FPGA), or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the signal extraction system based on the fm continuous wave radar, and various interfaces and lines are used to connect various parts of the whole operational device of the signal extraction system based on the fm continuous wave radar.
The memory may be used to store the computer program and/or the module, and the processor may implement various functions of the frequency modulated continuous wave radar based signal extraction system by running or executing the computer program and/or the module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart-Media-Card (SMC), secure-Digital (SD) Card, flash Card (Flash-Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
While the present disclosure has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiments or any particular embodiment, but is to be considered as providing a broad interpretation of such claims by reference to the appended claims in light of the prior art and thus effectively covering the intended scope of the disclosure. Furthermore, the foregoing description of the present disclosure has been presented in terms of embodiments foreseen by the inventor for the purpose of providing a enabling description for enabling the enabling description to be available, notwithstanding that insubstantial changes in the disclosure, not presently foreseen, may nonetheless represent equivalents thereto.
Claims (7)
1. A signal extraction method based on a frequency modulated continuous wave radar, the method comprising the steps of:
step 100, preprocessing a chest wall mechanical motion signal of a human body to be detected, which is received by a frequency modulation continuous wave radar, to obtain an echo signal, wherein the echo signal comprises: a respiration signal, a heartbeat signal, a higher harmonic component of the respiration signal, a higher harmonic component of the heartbeat signal, and a noise signal;
step 200, filtering the signal without noise in the echo signal to obtain a filtered signal, determining a frequency-power spectrum of the filtered signal in an estimated frequency interval, and separating according to a spectrogram of a peak value of the frequency-power spectrum to obtain a heartbeat signal and a respiratory signal;
the step S200 includes:
step S210, determining the estimated frequency interval of the echo signal, and setting the estimated frequency interval at equal intervalsFrequency of personalA rate point;
step S220, selecting a frequency point from the estimated frequency interval as an expected frequency point;
step S230, determining a frequency vector matrix of a desired frequency point, and incorporating the frequency vector matrix into a desired subspace;
step S240, determining a difference set between the estimated frequency interval and the selected expected frequency point, and taking a frequency vector matrix of the difference set as an interference subspace;
step S250, constructing an oblique projection operator of the echo signal, and carrying out oblique projection filtering on a signal which does not contain noise in the echo signal according to the oblique projection operator to obtain a filtered signal;
step S260, calculating the power of the filtered signal, and adopting the self-adaptive weight vector to the powerWeighting is carried out to obtain the weighted power of the filtered signal;
step S270, determining whether to traverse the estimated frequency interval, if not, selecting the rest frequency points from the estimated frequency interval as expected frequency points, and jumping to step S230; if yes, go to step S280;
step S280, determining a frequency-power spectrum of the estimated frequency interval, and separating to obtain a heartbeat signal and a respiratory signal according to a spectrogram of the frequency-power spectrum peak value.
2. The signal extraction method based on the frequency modulation continuous wave radar according to claim 1, wherein the estimated frequency range is [0.10hz,3.00hz ], and n=290.
3. The signal extraction method based on the frequency modulation continuous wave radar according to claim 1, wherein the calculation formula of the oblique projection operator is:wherein->For the desired frequency point, < >>A frequency vector matrix for the desired frequency point, < +.>For difference set, ->A frequency vector matrix that is a difference set;
the calculation formula of the filtering signal is as follows:，/>for estimating the frequency range, the frequency is estimated>For a signal that does not contain noise in the echo signal, and (2)>For filtering the signal.
4. A method for extracting signals based on a frequency modulated continuous wave radar as defined in claim 3, wherein said filtered signalsThe power P of (2) is calculated by the following formula:
wherein->Is a noise-free signal in the echo signal +.>Data length of->Signal representing noise-free echo signals +.>Covariance matrix of>For filtering the power of the signal.
5. The signal extraction method based on a frequency modulated continuous wave radar according to claim 4, wherein the adaptive weight vector calculation formula is:，/>namely, the self-adaptive weight vector; the calculation formula of the weighted power of the filtering signal is as follows: />，/>Is the weighted power of the filtered signal.
6. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the frequency modulated continuous wave radar based signal extraction method according to any one of claims 1 to 5.
7. A signal extraction system based on a frequency modulated continuous wave radar, comprising:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the frequency modulated continuous wave radar based signal extraction method as claimed in any one of claims 1 to 5.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19731895A1 (en) * | 1996-09-27 | 1998-04-09 | Hewlett Packard Co | Intra-myocardial activity detector |
| CN105137399A (en) * | 2015-07-24 | 2015-12-09 | 西安电子科技大学 | Radar adaptive beam forming method based on oblique projection filtration |
| CN111157960A (en) * | 2019-12-03 | 2020-05-15 | 南京汇君半导体科技有限公司 | Vital sign signal enhancement method and equipment, and extraction method and equipment based on millimeter wave radar |
| CN111568399A (en) * | 2020-05-15 | 2020-08-25 | 中国人民解放军陆军军医大学 | Radar-based respiration and heartbeat signal detection method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11311202B2 (en) * | 2017-11-14 | 2022-04-26 | Arizona Board Of Regents On Behalf Of Arizona State University | Robust real-time heart rate monitoring method based on heartbeat harmonics using small-scale radar |
-
2021
- 2021-02-22 CN CN202110196580.6A patent/CN113009476B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19731895A1 (en) * | 1996-09-27 | 1998-04-09 | Hewlett Packard Co | Intra-myocardial activity detector |
| CN105137399A (en) * | 2015-07-24 | 2015-12-09 | 西安电子科技大学 | Radar adaptive beam forming method based on oblique projection filtration |
| CN111157960A (en) * | 2019-12-03 | 2020-05-15 | 南京汇君半导体科技有限公司 | Vital sign signal enhancement method and equipment, and extraction method and equipment based on millimeter wave radar |
| CN111568399A (en) * | 2020-05-15 | 2020-08-25 | 中国人民解放军陆军军医大学 | Radar-based respiration and heartbeat signal detection method and system |
Non-Patent Citations (3)
| Title |
|---|
| Breast Tumor Detection in the Microwave Imaging With Oblique Projection and Rao Detectors;Fang L Der, et al;《Proc. 4th IEEE Int. Conf. Appl. Syst. Innov. 》;第239-242页 * |
| 二次谐波加权重构的 ７７ＧＨｚ ＦＭＣＷ 雷达心率监测方法;郑春弟等;《西安电子科技大学学报》;第173-179页 * |
| 基于N次峰值捕捉的超宽带雷达生命体征检测;杨国成等;《电子测量与仪器学报》;第205-208页 * |
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