CN116609755B - Vital sign detection method and system based on ultra-wideband radar - Google Patents
Vital sign detection method and system based on ultra-wideband radar Download PDFInfo
- Publication number
- CN116609755B CN116609755B CN202310883831.7A CN202310883831A CN116609755B CN 116609755 B CN116609755 B CN 116609755B CN 202310883831 A CN202310883831 A CN 202310883831A CN 116609755 B CN116609755 B CN 116609755B
- Authority
- CN
- China
- Prior art keywords
- signal
- signals
- heartbeat
- algorithm
- micro
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 31
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 95
- 238000012545 processing Methods 0.000 claims abstract description 73
- 230000000241 respiratory effect Effects 0.000 claims abstract description 67
- 238000001914 filtration Methods 0.000 claims abstract description 48
- 238000000034 method Methods 0.000 claims abstract description 17
- 239000011159 matrix material Substances 0.000 claims description 33
- 238000005070 sampling Methods 0.000 claims description 29
- 230000029058 respiratory gaseous exchange Effects 0.000 claims description 27
- 239000013598 vector Substances 0.000 claims description 9
- 230000001121 heart beat frequency Effects 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 7
- 230000003044 adaptive effect Effects 0.000 claims description 6
- 230000007246 mechanism Effects 0.000 claims description 6
- 238000001228 spectrum Methods 0.000 claims description 5
- 230000003068 static effect Effects 0.000 claims description 5
- 230000033001 locomotion Effects 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000000926 separation method Methods 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 4
- 238000009528 vital sign measurement Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000002592 echocardiography Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 229910052755 nonmetal Inorganic materials 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000036391 respiratory frequency Effects 0.000 description 1
Classifications
-
- 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
- G01S7/414—Discriminating targets with respect to background clutter
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1101—Detecting tremor
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
-
- 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
Abstract
The invention discloses a vital sign detection method and a vital sign detection system based on ultra-wideband radar, wherein the method comprises the following steps: the method comprises the steps of measuring vital signs through an ultra-wideband radar to obtain echo signals, wherein the echo signals comprise vital sign signals and clutter signals of stationary objects; clutter filtering processing is carried out on the echo signals to obtain body surface micro-motion signals; after the body surface micro-motion signal is compensated and undersampled, performing modal decomposition by using an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm, and reconstructing to obtain a heartbeat respiratory micro-motion signal; and classifying and processing the heartbeat respiratory micro-motion signals through a multiple signal classification MUSIC algorithm to obtain respiratory signals and heartbeat signals. The problem of modal aliasing caused by modal decomposition of the existing EMD algorithm is avoided, and the problem of interference of the respiratory signal to the heartbeat signal during separation of the heartbeat respiratory signal in the prior art is solved.
Description
Technical Field
The invention belongs to the field of signal processing, and particularly relates to a vital sign detection method and system based on ultra-wideband radar.
Background
The vital sign monitoring is a non-contact detection technology for reconstructing vital sign waveforms by transmitting electromagnetic wave signals to penetrate through non-metal barriers, extracting vital sign information related to respiration and heartbeat in electromagnetic echoes, and estimating related parameters. When the ultra-wideband radar detects the sign signals, the echo signals not only contain life information of fluctuation of the chest of a human body, but also contain noise information generated by movement of the background and other objects, and respiratory harmonic wave is used as one of the noise signals, so that the extraction and analysis of the heart beat frequency are seriously influenced.
The echo signal processing algorithm firstly removes echo signal noise, a common denoising algorithm such as empirical mode decomposition (Empirical Mode Decomposition, EMD) performs signal decomposition according to the time scale characteristics of data, when processing non-stationary radar echo data, the echo signal containing noise can be decomposed into a plurality of inherent mode components (Intrinsic mode Functions, IMF), and then different frequency components are primarily separated to achieve the purpose of denoising. However, when there is an intermittent phenomenon caused by an abnormal event in the signal, a problem of modal aliasing occurs in the decomposition result of the EMD; the secondary separation of the heartbeat and respiratory signals is carried out, such as a band-pass filter, and the electromagnetic echo of the heartbeat and respiratory frequency band is directly subjected to signal separation, so that the problem of interference of respiratory harmonics on the heartbeat signals still cannot be solved.
Disclosure of Invention
In order to make up the defects of the prior art, the invention provides a vital sign detection method and system based on ultra-wideband radar.
In order to solve the technical problems, the invention adopts the following technical scheme:
in a first aspect, a vital sign detection method based on ultra wideband radar is provided, including:
the method comprises the steps of measuring vital signs through an ultra-wideband radar to obtain echo signals, wherein the echo signals comprise vital sign signals and clutter signals of stationary objects;
clutter filtering processing is carried out on the echo signals to obtain body surface micro-motion signals;
after the body surface micro-motion signal is compensated and undersampled, performing modal decomposition by using an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm, and reconstructing to obtain a heartbeat respiratory micro-motion signal;
and classifying and processing the heartbeat respiratory micro-motion signals through a multiple signal classification MUSIC algorithm to obtain respiratory signals and heartbeat signals.
Further, the method for measuring vital signs by ultra-wideband radar to obtain echo signals comprises the following steps:
transmitting a Gaussian pulse signal to a direction area of a person to be measured through an ultra-wideband radar, wherein the direction area of the person to be measured comprises the person to be measured and a static object;
and receiving echo signals reflected by the direction area of the testee, wherein the echo signals comprise vital sign signals of the Gaussian pulse signals reflected by the testee and clutter signals of the Gaussian pulse signals reflected by the stationary object.
Further, performing clutter filtering processing on the echo signal to obtain a body surface micro-motion signal, including:
sampling the echo signals at preset time intervals to obtain a plurality of sampling signals;
analyzing each sampling signal to obtain distance information and time information;
according to the distance information and time information of all the sampling signals, constructing an echo data matrix taking the distance dimension and the time dimension as coordinates;
carrying out average preset frequency filtering on the sampling signals of each distance dimension in the echo data matrix along the time dimension by using a moving target display MTI algorithm to obtain a filtering processing signal;
processing the filtering processing signal by using a range gate selection algorithm to obtain the spatial position of the person to be tested;
and extracting the body surface micro-motion signals from the echo data matrix according to the space position.
Further, after the body surface micro-motion signal is compensated and undersampled, the improved adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm is utilized to perform mode decomposition, and the heartbeat breath micro-motion signal is obtained through reconstruction, which comprises the following steps:
performing compensation and undersampling processing on the body surface micro-motion signal according to a preset signal compensation undersampling processing mechanism to obtain a one-dimensional echo signal;
and carrying out modal decomposition on the one-dimensional echo signals by using an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm to obtain target intrinsic mode components IMF, and constructing and obtaining heartbeat respiratory micro-motion signals according to the target intrinsic mode components IMF.
Further, performing modal decomposition on the one-dimensional echo signal by using an improved adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm to obtain a target intrinsic mode component IMF, and constructing and obtaining a heartbeat respiratory micro-motion signal according to the target intrinsic mode component IMF, wherein the method comprises the following steps:
s1, carrying out modal decomposition on Gaussian white noise through an Empirical Mode Decomposition (EMD) algorithm to obtain white noise inherent modal components IMF of K stages, wherein K is an integer greater than or equal to 0;
s2, adding a white noise inherent mode component IMF of a first stage to the one-dimensional echo signal, constructing to obtain a first sequence, carrying out mode decomposition on the first sequence through an Empirical Mode Decomposition (EMD) algorithm to obtain a first order residual margin, and obtaining a first order inherent mode component IMF according to the difference between the one-dimensional echo signal and the first order residual margin;
s3, adding a white noise inherent mode component IMF of a second stage to the first order residual margin, constructing to obtain a second sequence, carrying out mode decomposition on the second sequence through an Empirical Mode Decomposition (EMD) algorithm to obtain a second order residual margin, and obtaining a second order inherent mode component IMF according to the difference between the first order residual margin and the second order residual margin;
s4, performing iteration in a circulating way until the j-th order residual error allowance meets a preset stopping condition, wherein j is an integer which is more than or equal to 2 and less than or equal to K;
s5, taking the first-order intrinsic mode component IMF to the j-th-order intrinsic mode component IMF as a target intrinsic mode component IMF, and constructing and obtaining a heartbeat respiratory micro-motion signal according to the target intrinsic mode component IMF.
In a second aspect, there is provided an ultra wideband radar-based vital sign detection system, comprising:
the detection module is used for measuring vital signs through the ultra-wideband radar to obtain echo signals, wherein the echo signals comprise vital sign signals and clutter signals of stationary objects;
the filtering processing module is used for performing clutter filtering processing on the echo signals to obtain body surface micro-motion signals;
the signal reconstruction module is used for carrying out modal decomposition by utilizing an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm after carrying out compensation and undersampling treatment on the body surface micro-motion signals, and obtaining heartbeat respiration micro-motion signals through reconstruction;
and the signal classification module is used for classifying and processing the heartbeat respiration micro-motion signals through a multiple signal classification MUSIC algorithm to obtain respiration signals and heartbeat signals.
Further, the detection module is specifically configured to transmit a gaussian pulse signal to a direction area of the person to be detected through an ultra wideband radar, where the direction area of the person to be detected includes the person to be detected and a stationary object; and receiving echo signals reflected by the direction area of the testee, wherein the echo signals comprise vital sign signals of the Gaussian pulse signals reflected by the testee and clutter signals of the Gaussian pulse signals reflected by the stationary object.
Further, the filtering processing module is specifically configured to sample the echo signal at a preset time interval to obtain a plurality of sampling signals; analyzing each sampling signal to obtain distance information and time information; according to the distance information and time information of all the sampling signals, constructing an echo data matrix taking the distance dimension and the time dimension as coordinates; carrying out average preset frequency filtering on the sampling signals of each distance dimension in the echo data matrix along the time dimension by using a moving target display MTI algorithm to obtain a filtering processing signal; processing the filtering processing signal by using a range gate selection algorithm to obtain the spatial position of the person to be tested; and extracting the body surface micro-motion signals from the echo data matrix according to the space position.
Further, the signal reconstruction module includes:
the signal preprocessing unit is used for carrying out compensation and undersampling processing on the body surface micro-motion signal according to a preset signal compensation undersampling processing mechanism to obtain a one-dimensional echo signal;
the signal reconstruction unit is used for carrying out modal decomposition on the one-dimensional echo signals by utilizing an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm to obtain a target intrinsic mode component IMF, and constructing and obtaining a heartbeat respiratory micro-motion signal according to the target intrinsic mode component IMF.
Further, the signal reconstruction unit is specifically configured to perform the following steps:
s1, carrying out modal decomposition on Gaussian white noise through an Empirical Mode Decomposition (EMD) algorithm to obtain white noise inherent modal components IMF of K stages, wherein K is an integer greater than or equal to 0;
s2, adding a white noise inherent mode component IMF of a first stage to the one-dimensional echo signal, constructing to obtain a first sequence, carrying out mode decomposition on the first sequence through an Empirical Mode Decomposition (EMD) algorithm to obtain a first order residual margin, and obtaining a first order inherent mode component IMF according to the difference between the one-dimensional echo signal and the first order residual margin;
s3, adding a white noise inherent mode component IMF of a second stage to the first order residual margin, constructing to obtain a second sequence, carrying out mode decomposition on the second sequence through an Empirical Mode Decomposition (EMD) algorithm to obtain a second order residual margin, and obtaining a second order inherent mode component IMF according to the difference between the first order residual margin and the second order residual margin;
s4, performing iteration in a circulating way until the j-th order residual error allowance meets a preset stopping condition, wherein j is an integer which is more than or equal to 2 and less than or equal to K;
s5, taking the first-order intrinsic mode component IMF to the j-th-order intrinsic mode component IMF as a target intrinsic mode component IMF, and constructing and obtaining a heartbeat respiratory micro-motion signal according to the target intrinsic mode component IMF.
The invention has the beneficial effects that:
the method comprises the steps of measuring vital signs through an ultra-wideband radar to obtain echo signals, performing clutter filtering processing on the echo signals to obtain body surface micro signals, performing modal decomposition by using an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm after compensating and undersampling processing on the body surface micro signals, reconstructing to obtain heartbeat respiration micro signals, and performing classification processing on the heartbeat respiration micro signals through a multiple signal classification MUSIC algorithm to obtain respiration signals and heartbeat signals. The heartbeat respiratory micro-motion signal obtained by the mode decomposition and reconstruction of the ICEEMDAN algorithm is utilized, so that the mode aliasing problem caused by the mode decomposition of the existing EMD algorithm is avoided; and after denoising by using an ICEEMDAN algorithm, the respiratory signal and the heartbeat signal are separated by using a MUSIC algorithm signal, so that the problem of interference of the respiratory signal on the heartbeat signal during the separation of the heartbeat respiratory signal in the prior art is solved.
Drawings
FIG. 1 is a flow chart of a vital sign detection method based on ultra wideband radar of the present invention;
FIG. 2 is a flow chart of the clutter filtering process of the present invention;
FIG. 3 is a flow chart of the reconstruction of the heart beat respiratory micro motion signal of the present invention;
FIG. 4 is a first block diagram of an ultra wideband radar-based vital sign detection system of the present invention;
fig. 5 is a second block diagram of the ultra wideband radar-based vital sign detection system of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a vital sign detection method based on ultra wideband radar, including:
101, measuring vital signs through an ultra-wideband radar to obtain an echo signal;
in practical application of the embodiment, a key device in Ultra Wide Band (UWB) radar is a UWB radar development kit X4M03 module manufactured by Novelda corporation, and the module is composed of a pair of directional patch antennas with integrated Wi-Fi filters, an X4 radar SOC chip, and an SAMS70 microcontroller manufactured by Atmel corporation for controlling X4 and communicating with external devices. The UWB radar transmits pulse signals with the bandwidth of 1.5 GHz and the center frequency of 7.29 GHz, and the beam width of-3 dB is 65 degrees. The UWB radar receiver samples the reflected signal at 23.328 GHz, each fast time distance unit length for the baseband signal is 0.0514 m, and the equivalent detection distance is 9.9 m;
in the case of accidents such as collapse, people can be buried under a building or earth, in order to detect vital signs of the buried people, a Gaussian pulse signal is sent to a direction area of a person to be detected through a UWB radar, the direction area of the person to be detected contains the person to be detected and a static object, an echo signal reflected by the direction area of the person to be detected is received, the echo signal contains vital signs of the Gaussian pulse signal reflected by the person to be detected and clutter of the Gaussian pulse signal reflected by the static object, the vital signs describe vital information of fluctuation of a chest of a human body, and the fluctuation of the chest of the human body is caused by respiration and heartbeat.
102, performing clutter filtering processing on the echo signals to obtain body surface micro-motion signals;
since the echo signal contains other useless clutter signals in addition to the vital sign signal, clutter filtering processing needs to be performed first, and a specific clutter filtering processing process is shown in fig. 2 as follows:
201, sampling the echo signals at preset time intervals to obtain a plurality of sampling signals;
the echo signals are continuous, and in order to facilitate data processing, the echo signals need to be sampled at preset time intervals so as to obtain a plurality of sampling signals;
202, analyzing each sampling signal to obtain distance information and time information;
because the echo signal is a Gaussian pulse signal transmitted by the reflected UWB, the distance can be deduced according to the time consumption of the received signal and the reflected signal in the transmission process at different positions of a human body or a static object, so that the distance information and the time information of each sampling signal can be obtained, and the distance information is relative to the UWB radar;
203, constructing an echo data matrix taking the distance dimension and the time dimension as coordinates according to the distance information and the time information of all the sampling signals;
204, carrying out average preset frequency filtering on the sampling signals of each distance dimension in the echo data matrix along the time dimension by using a moving target display MTI algorithm to obtain a filtering processing signal;
carrying out average preset frequency filtering on sampling signals of each distance dimension in an echo data matrix along a time dimension by using a known moving target display (Moving Target Indicator, MTI) algorithm, so that interference of other useless clutter on the echo signals is eliminated, and a filtering processing signal is obtained;
205, processing the filtering processing signal by using a range gate selection algorithm to obtain the spatial position of the person to be detected;
and 206, extracting body surface micro-motion signals from the echo data matrix according to the space position.
103, after the body surface micro-motion signal is compensated and undersampled, performing modal decomposition by using an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm, and reconstructing to obtain a heartbeat respiration micro-motion signal;
the body surface micro-motion signal is subjected to compensation and undersampling processing according to a preset signal compensation undersampling processing mechanism, and a one-dimensional echo signal is obtained; after the body surface micro-motion signal is compensated and undersampled, performing modal decomposition by using an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm, and reconstructing to obtain a heartbeat respiratory micro-motion signal;
the ICEEMDAN is utilized to carry out modal decomposition,represented as the first signal to be decomposed obtained by EMD decompositionThe number of modal components,represented as a local mean of the solution signal,is Gaussian white noise with the average value of 0,residual margin for the kth stage; coefficients ofFor the signal-to-noise ratio of the kth stage,satisfies the following formula:
；
wherein;setting a good amplitude value in advance;is a mathematical expectation operator;
the flow chart for reconstructing the heartbeat breath micro-motion signal is shown in fig. 3:
s1, carrying out modal decomposition on Gaussian white noise through an Empirical Mode Decomposition (EMD) algorithm to obtain white noise inherent mode components IMF of K stages;
s2, adding a white noise inherent mode component IMF of a first stage to the one-dimensional echo signal, constructing to obtain a first sequence, carrying out mode decomposition on the first sequence through an Empirical Mode Decomposition (EMD) algorithm to obtain a first order residual margin, and obtaining a first order inherent mode component IMF according to the difference between the one-dimensional echo signal and the first order residual margin;
the one-dimensional echo signal isThe white noise intrinsic mode component IMF of the first stage isAt the time of addingThe signal-to-noise ratio of the corresponding stage is considered, and the expression of the first sequence isPerforming modal decomposition on the first sequence through an Empirical Mode Decomposition (EMD) algorithm to obtain a first-order residual marginThe expression of the first-order intrinsic mode component IMF is；
S3, adding a white noise inherent mode component IMF of a second stage to the first order residual margin, constructing to obtain a second sequence, carrying out mode decomposition on the second sequence through an Empirical Mode Decomposition (EMD) algorithm to obtain a second order residual margin, and obtaining a second order inherent mode component IMF according to the difference between the first order residual margin and the second order residual margin;
similar to the step S2, adding a white noise inherent mode component IMF structure of a second stage to the residual margin of the first order to obtain a second sequence, and carrying out mode decomposition on the second sequence through an Empirical Mode Decomposition (EMD) algorithm to obtain the residual margin of the second orderThe expression of the second-order intrinsic mode component IMF is；
S4, performing iteration in a circulating way until the j-th order residual allowance meets a preset stopping condition;
the setting of the preset stopping condition is that the residual margin of the jth decomposition is assumed to be a monotone signal, the subsequent EMD modal decomposition is stopped, and j is an integer which is more than or equal to 2 and less than or equal to K;
s5, taking the first-order intrinsic mode component IMF to the j-th-order intrinsic mode component IMF as a target intrinsic mode component IMF, and constructing and obtaining a heartbeat respiratory micro-motion signal according to the target intrinsic mode component IMF.
The heartbeat respiratory jog signal at this time is comprised of both respiration and heartbeat and has been excluded from noise interference.
104, classifying the heartbeat respiratory micro-motion signals through a multiple signal classification MUSIC algorithm to obtain respiratory signals and heartbeat signals.
The basic principle of the MUSIC algorithm is that firstly, a covariance matrix is obtained by counting heartbeat respiratory micro signals, then algebraic operation processing is carried out on the covariance matrix to obtain a signal subspace and a noise subspace, and finally, a spectrogram is obtained by utilizing orthogonality of the signal subspace and the noise subspace.
When only two respiratory signals and heartbeat signals are used, and parameters are set to be 2, the frequency spectrum displays the strongest signal component, the main signal in the heartbeat respiratory micro-motion signals is the respiratory signal, and the frequency spectrum of the strongest signal (respiratory signal) is obtained by MUSIC algorithm analysis through setting the number of the signals to be 2, so that the frequency of the respiratory signal is detected;
after the heartbeat respiratory micro-motion signal is subjected to band-pass filtering, the main component of the signal component is a heartbeat signal, so that the frequency spectrum of the heartbeat signal can be obtained through MUSIC algorithm analysis by setting the number parameter of the signal, and the heartbeat frequency is detected;
considering that the signals after band-pass filtering are random stationary signals, counting to obtain a covariance matrix, performing algebraic operation on the covariance matrix to decompose the characteristics of the covariance matrix to obtain matrix characteristic values and characteristic vector matrixes, constructing a scanning function, changing circle frequency, and if the signal vectors are not corresponding to the noise space, searching the signal vectors which are not orthogonal to the noise space, so that a corresponding spectrogram can be obtained to analyze the aliasing degree of respiratory harmonic frequency and heartbeat frequency, and respiratory signals and heartbeat signals can be obtained.
The embodiment principle of the embodiment of the invention is as follows:
the method comprises the steps of measuring vital signs through an ultra-wideband radar to obtain echo signals, performing clutter filtering processing on the echo signals to obtain body surface micro signals, performing modal decomposition by using an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm after compensating and undersampling processing on the body surface micro signals, reconstructing to obtain heartbeat respiration micro signals, and performing classification processing on the heartbeat respiration micro signals through a multiple signal classification MUSIC algorithm to obtain respiration signals and heartbeat signals. The heartbeat respiratory micro-motion signal obtained by the mode decomposition and reconstruction of the ICEEMDAN algorithm is utilized, so that the mode aliasing problem caused by the mode decomposition of the existing EMD algorithm is avoided; and after denoising by using an ICEEMDAN algorithm, the respiratory signal and the heartbeat signal are separated by using a MUSIC algorithm signal, so that the problem of interference of the respiratory signal on the heartbeat signal during the separation of the heartbeat respiratory signal in the prior art is solved.
The implementation process and principle of the vital sign detection method based on the ultra wideband radar are described in detail in the above embodiments, and the vital sign detection system based on the ultra wideband radar is described below by way of embodiments, as shown in fig. 4, including:
the detection module 401 is configured to perform vital sign measurement by using an ultra-wideband radar to obtain an echo signal, where the echo signal includes a vital sign signal and a clutter signal of a stationary object;
the filtering processing module 402 is configured to perform clutter filtering processing on the echo signal to obtain a body surface micro-motion signal;
the signal reconstruction module 403 is configured to perform modal decomposition by using an improved adaptive noise complete set empirical mode decomposition icemdan algorithm after performing compensation and undersampling processing on the body surface micro-motion signal, and reconstruct to obtain a heartbeat respiratory micro-motion signal;
the signal classification module 404 is configured to perform classification processing on the heartbeat respiratory micro-motion signal through a multiple signal classification MUSIC algorithm, so as to obtain a respiratory signal and a heartbeat signal.
The embodiment principle of the embodiment of the invention is as follows:
the detection module 401 performs vital sign measurement through an ultra-wideband radar to obtain an echo signal, the filtering processing module 402 performs clutter filtering processing on the echo signal to obtain a body surface micro-motion signal, the signal reconstruction module 403 performs modal decomposition by using an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm after performing compensation and undersampling processing on the body surface micro-motion signal, the heart beat respiratory micro-motion signal is obtained through reconstruction, and the signal classification module 404 performs classification processing on the heart beat respiratory micro-motion signal through a multiple signal classification MUSIC algorithm to obtain a respiratory signal and a heart beat signal. The heartbeat respiratory micro-motion signal obtained by the mode decomposition and reconstruction of the ICEEMDAN algorithm is utilized, so that the mode aliasing problem caused by the mode decomposition of the existing EMD algorithm is avoided; and after denoising by using an ICEEMDAN algorithm, the respiratory signal and the heartbeat signal are separated by using a MUSIC algorithm signal, so that the problem of interference of the respiratory signal on the heartbeat signal during the separation of the heartbeat respiratory signal in the prior art is solved.
Based on the embodiment shown in fig. 4 above, it is preferred that, in some embodiments of the invention,
the detection module 401 is specifically configured to transmit a gaussian pulse signal to a direction area of a person to be detected through an ultra wideband radar, where the direction area of the person to be detected includes the person to be detected and a stationary object; and receiving echo signals reflected by the direction area of the testee, wherein the echo signals comprise vital sign signals of the Gaussian pulse signals reflected by the testee and clutter signals of the Gaussian pulse signals reflected by the stationary object.
Based on the embodiment shown in fig. 4 above, it is preferred that, in some embodiments of the invention,
the filtering processing module 402 is specifically configured to sample the echo signal at a preset time interval to obtain a plurality of sampled signals; analyzing each sampling signal to obtain distance information and time information; according to the distance information and time information of all the sampling signals, constructing an echo data matrix taking the distance dimension and the time dimension as coordinates; carrying out average preset frequency filtering on the sampling signals of each distance dimension in the echo data matrix along the time dimension by using a moving target display MTI algorithm to obtain a filtering processing signal; processing the filtering processing signal by using a range gate selection algorithm to obtain the spatial position of the person to be tested; and extracting the body surface micro-motion signals from the echo data matrix according to the space position.
Based on the embodiment shown in fig. 4 above, it is preferred that, as shown in fig. 5, in some embodiments of the invention,
the signal reconstruction module 403 includes:
the signal preprocessing unit 501 is configured to perform compensation and undersampling processing on the body surface micro-motion signal according to a preset signal compensation undersampling processing mechanism, so as to obtain a one-dimensional echo signal;
the signal reconstruction unit 502 is configured to perform modal decomposition on the one-dimensional echo signal by using an improved adaptive noise complete set empirical mode decomposition icemdan algorithm to obtain a target intrinsic mode component IMF, and construct a heartbeat respiratory micro-motion signal according to the target intrinsic mode component IMF.
Based on the embodiment shown in fig. 5 above, it is preferred that, in some embodiments of the invention,
the signal reconstruction unit is specifically configured to perform the following steps:
s1, carrying out modal decomposition on Gaussian white noise through an Empirical Mode Decomposition (EMD) algorithm to obtain white noise inherent modal components IMF of K stages, wherein K is an integer greater than or equal to 0;
s2, adding a white noise inherent mode component IMF of a first stage to the one-dimensional echo signal, constructing to obtain a first sequence, carrying out mode decomposition on the first sequence through an Empirical Mode Decomposition (EMD) algorithm to obtain a first order residual margin, and obtaining a first order inherent mode component IMF according to the difference between the one-dimensional echo signal and the first order residual margin;
s3, adding a white noise inherent mode component IMF of a second stage to the first order residual margin, constructing to obtain a second sequence, carrying out mode decomposition on the second sequence through an Empirical Mode Decomposition (EMD) algorithm to obtain a second order residual margin, and obtaining a second order inherent mode component IMF according to the difference between the first order residual margin and the second order residual margin;
s4, performing iteration in a circulating way until the j-th order residual error allowance meets a preset stopping condition, wherein j is an integer which is more than or equal to 2 and less than or equal to K;
s5, taking the first-order intrinsic mode component IMF to the j-th-order intrinsic mode component IMF as a target intrinsic mode component IMF, and constructing and obtaining a heartbeat respiratory micro-motion signal according to the target intrinsic mode component IMF.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather as providing for the use of additional embodiments and advantages of all such modifications, equivalents, improvements and similar to the present invention are intended to be included within the scope of the present invention as defined by the appended claims.
Claims (10)
1. The vital sign detection method based on the ultra-wideband radar is characterized by comprising the following steps of:
the method comprises the steps of measuring vital signs through an ultra-wideband radar to obtain echo signals, wherein the echo signals comprise vital sign signals and clutter signals of stationary objects;
performing clutter filtering processing on the echo signals to obtain body surface micro-motion signals;
after the body surface micro-motion signal is compensated and undersampled, performing modal decomposition by using an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm, and reconstructing to obtain a heartbeat respiration micro-motion signal;
classifying the heartbeat respiratory micro-motion signals through a multiple signal classification MUSIC algorithm to obtain respiratory signals and heartbeat signals;
the step of classifying the heartbeat respiratory micro-motion signal through a multiple signal classification MUSIC algorithm to obtain a respiratory signal and a heartbeat signal comprises the following steps:
the main signal in the heartbeat respiration micro-motion signal is a respiration signal, the secondary signal is a heartbeat signal, the MUSIC algorithm analysis is carried out by setting the number parameter of the signals to be 2, a spectrogram of the respiration signal is obtained, and the respiration harmonic frequency of the respiration signal is detected;
after the heartbeat respiratory micro-motion signal is subjected to band-pass filtering, the main component of the signal component is the heartbeat signal, and the frequency spectrum of the heartbeat signal is obtained by MUSIC algorithm analysis through setting the number parameter of the signal, so that the heartbeat frequency is detected;
considering that the signals after band-pass filtering are random stationary signals, counting to obtain a covariance matrix, performing algebraic operation on the covariance matrix to decompose the characteristics of the covariance matrix to obtain matrix characteristic values and characteristic vector matrixes, constructing a scanning function, changing circle frequency, and if the signal vectors are not corresponding to the noise space, searching the signal vectors which are not orthogonal to the noise space, so that a corresponding spectrogram can be obtained to analyze the aliasing degree of respiratory harmonic frequency and heartbeat frequency, and respiratory signals and heartbeat signals can be obtained.
2. The method for detecting vital signs based on the ultra-wideband radar according to claim 1, wherein the step of obtaining the echo signal by measuring the vital signs by the ultra-wideband radar comprises the steps of:
transmitting a Gaussian pulse signal to a direction area of a to-be-detected person through an ultra-wideband radar, wherein the direction area of the to-be-detected person comprises the to-be-detected person and a static object;
and receiving echo signals reflected by the direction area of the to-be-detected person, wherein the echo signals comprise vital sign signals of the Gaussian pulse signals reflected by the to-be-detected person and clutter signals of the Gaussian pulse signals reflected by the stationary object.
3. The vital sign detection method based on ultra-wideband radar according to claim 1, wherein the performing clutter filtering processing on the echo signal to obtain a body surface micro-motion signal includes:
sampling the echo signals at preset time intervals to obtain a plurality of sampling signals;
analyzing each sampling signal to obtain distance information and time information;
according to the distance information and time information of all the sampling signals, constructing an echo data matrix taking the distance dimension and the time dimension as coordinates;
carrying out average preset frequency filtering on the sampling signals of each distance dimension in the echo data matrix along the time dimension by using a moving target display MTI algorithm to obtain a filtering processing signal;
processing the filtering processing signal by using a range gate selection algorithm to obtain the spatial position of the person to be detected;
and extracting the body surface micro-motion signals from the echo data matrix according to the space position.
4. The vital sign detection method based on ultra wideband radar according to claim 1, wherein after the body surface micro motion signal is compensated and undersampled, performing modal decomposition by using an improved adaptive noise complete set empirical mode decomposition icemdan algorithm, and reconstructing to obtain a heartbeat respiratory micro motion signal, the method comprises:
performing compensation and undersampling processing on the body surface micro-motion signal according to a preset signal compensation undersampling processing mechanism to obtain a one-dimensional echo signal;
and carrying out modal decomposition on the one-dimensional echo signals by using an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm to obtain target intrinsic mode components IMF, and constructing and obtaining heartbeat respiratory micro-motion signals according to the target intrinsic mode components IMF.
5. The method for detecting vital signs based on ultra wideband radar according to claim 4, wherein performing modal decomposition on the one-dimensional echo signal by using an improved adaptive noise perfect set empirical mode decomposition icemdan algorithm to obtain a target intrinsic mode component IMF, and constructing a heartbeat respiratory micro-motion signal according to the target intrinsic mode component IMF, comprises:
s1, carrying out modal decomposition on Gaussian white noise through an Empirical Mode Decomposition (EMD) algorithm to obtain white noise inherent mode components IMF of K stages, wherein K is an integer greater than or equal to 0;
s2, adding a white noise inherent mode component IMF of a first stage to the one-dimensional echo signal, constructing a first sequence, carrying out mode decomposition on the first sequence through the empirical mode decomposition EMD algorithm to obtain a first-order residual margin, and obtaining a first-order inherent mode component IMF according to the difference between the one-dimensional echo signal and the first-order residual margin;
s3, adding a white noise intrinsic mode component IMF of a second stage to the first order residual margin, constructing a second sequence, carrying out modal decomposition on the second sequence through the empirical mode decomposition EMD algorithm to obtain a second order residual margin, and obtaining a second order intrinsic mode component IMF according to the difference between the first order residual margin and the second order residual margin;
s4, performing iteration in a circulating way until the j-th order residual error allowance meets a preset stopping condition, wherein j is an integer which is more than or equal to 2 and less than or equal to K;
s5, taking the first-order intrinsic mode component IMF to the j-th-order intrinsic mode component IMF as a target intrinsic mode component IMF, and constructing and obtaining a heartbeat respiratory micro-motion signal according to the target intrinsic mode component IMF.
6. A vital sign detection system based on ultra wideband radar, comprising:
the detection module is used for measuring vital signs through the ultra-wideband radar to obtain echo signals, wherein the echo signals comprise vital sign signals and clutter signals of stationary objects;
the filtering processing module is used for performing clutter filtering processing on the echo signals to obtain body surface micro-motion signals;
the signal reconstruction module is used for carrying out modal decomposition by utilizing an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm after carrying out compensation and undersampling processing on the body surface micro-motion signals, and reconstructing to obtain heartbeat respiration micro-motion signals;
the signal classification module is used for classifying the heartbeat respiration micro-motion signals through a multiple signal classification MUSIC algorithm to obtain respiratory signals and heartbeat signals;
the signal classification module is specifically used for carrying out MUSIC algorithm analysis by setting the number parameter of the signals as 2, wherein the main signal in the heartbeat respiration micro-motion signal is a respiration signal, the secondary signal is a heartbeat signal, a spectrogram of the respiration signal is obtained, and the respiration harmonic frequency of the respiration signal is detected; after the heartbeat respiratory micro-motion signal is subjected to band-pass filtering, the main component of the signal component is the heartbeat signal, and the frequency spectrum of the heartbeat signal is obtained by MUSIC algorithm analysis through setting the number parameter of the signal, so that the heartbeat frequency is detected; considering that the signals after band-pass filtering are random stationary signals, counting to obtain a covariance matrix, performing algebraic operation on the covariance matrix to decompose the characteristics of the covariance matrix to obtain matrix characteristic values and characteristic vector matrixes, constructing a scanning function, changing circle frequency, and if the signal vectors are not corresponding to the noise space, searching the signal vectors which are not orthogonal to the noise space, so that a corresponding spectrogram can be obtained to analyze the aliasing degree of respiratory harmonic frequency and heartbeat frequency, and respiratory signals and heartbeat signals can be obtained.
7. The ultra-wideband radar-based vital sign detection system of claim 6, wherein,
the detection module is specifically configured to transmit a gaussian pulse signal to a direction region of a to-be-detected person through an ultra-wideband radar, where the direction region of the to-be-detected person includes the to-be-detected person and a stationary object; and receiving echo signals reflected by the direction area of the to-be-detected person, wherein the echo signals comprise vital sign signals of the Gaussian pulse signals reflected by the to-be-detected person and clutter signals of the Gaussian pulse signals reflected by the stationary object.
8. The ultra-wideband radar-based vital sign detection system of claim 6, wherein,
the filtering processing module is specifically configured to sample the echo signal at a preset time interval to obtain a plurality of sampling signals; analyzing each sampling signal to obtain distance information and time information; according to the distance information and time information of all the sampling signals, constructing an echo data matrix taking the distance dimension and the time dimension as coordinates; carrying out average preset frequency filtering on the sampling signals of each distance dimension in the echo data matrix along the time dimension by using a moving target display MTI algorithm to obtain a filtering processing signal; processing the filtering processing signal by using a range gate selection algorithm to obtain the spatial position of the person to be detected; and extracting the body surface micro-motion signals from the echo data matrix according to the space position.
9. The ultra-wideband radar-based vital sign detection system of claim 6, wherein the signal reconstruction module includes:
the signal preprocessing unit is used for carrying out compensation and undersampling processing on the body surface micro-motion signal according to a preset signal compensation undersampling processing mechanism to obtain a one-dimensional echo signal;
the signal reconstruction unit is used for carrying out modal decomposition on the one-dimensional echo signals by utilizing an improved self-adaptive noise complete set empirical mode decomposition ICEEMDAN algorithm to obtain a target intrinsic mode component IMF, and constructing and obtaining a heartbeat respiration micro-motion signal according to the target intrinsic mode component IMF.
10. The ultra wideband radar based vital sign detection system of claim 9, wherein the signal reconstruction unit is specifically configured to perform the steps of:
s1, carrying out modal decomposition on Gaussian white noise through an Empirical Mode Decomposition (EMD) algorithm to obtain white noise inherent mode components IMF of K stages, wherein K is an integer greater than or equal to 0;
s2, adding a white noise inherent mode component IMF of a first stage to the one-dimensional echo signal, constructing a first sequence, carrying out mode decomposition on the first sequence through the empirical mode decomposition EMD algorithm to obtain a first-order residual margin, and obtaining a first-order inherent mode component IMF according to the difference between the one-dimensional echo signal and the first-order residual margin;
s3, adding a white noise intrinsic mode component IMF of a second stage to the first order residual margin, constructing a second sequence, carrying out modal decomposition on the second sequence through the empirical mode decomposition EMD algorithm to obtain a second order residual margin, and obtaining a second order intrinsic mode component IMF according to the difference between the first order residual margin and the second order residual margin;
s4, performing iteration in a circulating way until the j-th order residual error allowance meets a preset stopping condition, wherein j is an integer which is more than or equal to 2 and less than or equal to K;
s5, taking the first-order intrinsic mode component IMF to the j-th intrinsic mode component IMF as a target intrinsic mode component IMF, and constructing and obtaining a heartbeat respiratory micro-motion signal according to the target intrinsic mode component IMF.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310883831.7A CN116609755B (en) | 2023-07-19 | 2023-07-19 | Vital sign detection method and system based on ultra-wideband radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310883831.7A CN116609755B (en) | 2023-07-19 | 2023-07-19 | Vital sign detection method and system based on ultra-wideband radar |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116609755A CN116609755A (en) | 2023-08-18 |
| CN116609755B true CN116609755B (en) | 2023-10-20 |
Family
ID=87676807
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310883831.7A Active CN116609755B (en) | 2023-07-19 | 2023-07-19 | Vital sign detection method and system based on ultra-wideband radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116609755B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112754441A (en) * | 2021-01-08 | 2021-05-07 | 杭州环木信息科技有限责任公司 | Millimeter wave-based non-contact heartbeat detection method |
| CN114897011A (en) * | 2022-04-29 | 2022-08-12 | 西安交通大学 | Signal separation and denoising method and system for Doppler radar physiological signal detection |
| CN115067916A (en) * | 2022-06-15 | 2022-09-20 | 南京邮电大学 | Vital sign monitoring method based on millimeter wave radar |
| CN115708675A (en) * | 2022-11-21 | 2023-02-24 | 南京邮电大学 | Heart rate estimation method based on millimeter wave radar |
| CN116269259A (en) * | 2023-02-27 | 2023-06-23 | 重庆邮电大学 | Human body breathing and heartbeat detection method based on improved BP neural network |
| CN116407104A (en) * | 2023-03-15 | 2023-07-11 | 江苏科技大学 | Fetal heart rate estimation method based on empirical mode decomposition and multiple signal classification |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220373646A1 (en) * | 2021-05-21 | 2022-11-24 | Samsung Electronics Co., Ltd. | Joint estimation of respiratory and heart rates using ultra-wideband radar |
-
2023
- 2023-07-19 CN CN202310883831.7A patent/CN116609755B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112754441A (en) * | 2021-01-08 | 2021-05-07 | 杭州环木信息科技有限责任公司 | Millimeter wave-based non-contact heartbeat detection method |
| CN114897011A (en) * | 2022-04-29 | 2022-08-12 | 西安交通大学 | Signal separation and denoising method and system for Doppler radar physiological signal detection |
| CN115067916A (en) * | 2022-06-15 | 2022-09-20 | 南京邮电大学 | Vital sign monitoring method based on millimeter wave radar |
| CN115708675A (en) * | 2022-11-21 | 2023-02-24 | 南京邮电大学 | Heart rate estimation method based on millimeter wave radar |
| CN116269259A (en) * | 2023-02-27 | 2023-06-23 | 重庆邮电大学 | Human body breathing and heartbeat detection method based on improved BP neural network |
| CN116407104A (en) * | 2023-03-15 | 2023-07-11 | 江苏科技大学 | Fetal heart rate estimation method based on empirical mode decomposition and multiple signal classification |
Non-Patent Citations (3)
| Title |
|---|
| SSA-VMD for UWB Radar Sensor Vital Sign Extraction;Huimin Yu 等;Sensors;第23卷(第2期);全文 * |
| 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34卷(第11期);全文 * |
| 基于改进变分模态分解的生命体征检测;韩宇 等;南京大学学报（自然科学）;第58卷(第4期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116609755A (en) | 2023-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10429488B1 (en) | System and method for geo-locating and detecting source of electromagnetic emissions | |
| CN112754441B (en) | Millimeter wave-based non-contact heartbeat detection method | |
| KR102221839B1 (en) | Real-time cardiac rate detection apparatus in noisy environment and method thereof | |
| CN112220464A (en) | Human body respiration and heartbeat signal detection method and system based on UWB radar | |
| CN105158749A (en) | High-frequency radar sea-clutter amplitude statistical distribution test method | |
| CN114002656A (en) | Method and system for suppressing clutter of ultra-wideband continuous wave biological radar data | |
| CN110398722A (en) | Extension target echo detection method based on the limited spectrum of random matrix | |
| CN116942125A (en) | UWB vital sign signal detection method based on NGO-VMD algorithm | |
| CN109085547B (en) | Denoising method and related device for surface penetrating radar echo signal | |
| CN115755015A (en) | Method, device, equipment and medium for detecting living body in cabin | |
| Chen et al. | Ultrawideband synthetic aperture radar for respiratory motion detection | |
| Lv et al. | A two-step approach for pulse RFI detection in SAR data | |
| Rane et al. | Clutter suppression techniques to detect behind the wall static human using UWB radar | |
| CN116609755B (en) | Vital sign detection method and system based on ultra-wideband radar | |
| Ma et al. | Radar vital signs detection method based on variational mode decomposition and wavelet transform | |
| KR101916591B1 (en) | A bio-information determination apparatus and method using principal component analysis of radar signal | |
| CN115345216A (en) | FMCW radar interference elimination method fusing prior information | |
| CN114903469A (en) | Vital sign monitoring method and device | |
| Wang et al. | Automated weak signal frequency estimation method based on Duffing oscillator and particle swarm optimization | |
| CN114355329A (en) | Method for detecting vital sign distance of frequency modulated continuous wave radar | |
| Faisal et al. | Human Activity Recognition From FMCW Radar Signals Utilizing Cross-Terms Free WVD | |
| CN113281740A (en) | Ultra-wideband Doppler radar life detection system | |
| Li et al. | Deep learning for interference mitigation in time-frequency maps of fmcw radars | |
| Wu et al. | Vital Signs Detection Based on Scale-Space Representation and Empirical Wavelet Transform for FMCW Radar | |
| Sekak et al. | Study and Evaluation of the Vital Signs Detection Based on the Third Order Cyclic Temporal Moment and Cumulant |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |