CN114355329A - Method for detecting vital sign distance of frequency modulated continuous wave radar - Google Patents
Method for detecting vital sign distance of frequency modulated continuous wave radar Download PDFInfo
- Publication number
- CN114355329A CN114355329A CN202111652208.8A CN202111652208A CN114355329A CN 114355329 A CN114355329 A CN 114355329A CN 202111652208 A CN202111652208 A CN 202111652208A CN 114355329 A CN114355329 A CN 114355329A
- Authority
- CN
- China
- Prior art keywords
- distance
- vital sign
- signal
- signals
- target
- 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.)
- Pending
Links
Images
Landscapes
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention relates to a vital sign detection method of a frequency modulation continuous wave radar, which can accurately detect a distance unit containing a vital sign signal in a complex environment with more interference sources, thereby finding out a correct distance dimension, needing no presetting of a target detection threshold value, reducing false alarm and false alarm probability of real human target detection, and ensuring that target vital sign signals in different distance units are successfully extracted. The vital sign signal extraction depends on the correct selection of the distance dimension to a great extent, the distance unit where the measured target is located is found, and a very important step is taken for solving the accurate vital sign signal.
Description
Technical Field
The invention relates to the technical field of radars, in particular to a method for detecting the distance of a frequency-modulated continuous wave radar life body.
Background
In recent years, the application of radar technology to contactless vital sign detection has been a major breakthrough in healthcare. Respiration and heartbeat are important vital sign signals of the human body. Compared with the traditional contact type measurement mode, the radar-based non-contact detection technology gradually receives wide attention, and has a wide application prospect in the aspects of home monitoring, earthquake relief, epidemic prevention control and the like. The current doppler radar measurement methods mainly include two measurement methods: continuous Wave (CW) radar and Frequency Modulated Continuous Wave (FMCW) radar. One of the advantages of the radar of the FMCW system compared with the radar of the CW system is that the echo signal contains the distance information of the target, so that when the FMCW radar is used for detection, the distance information and the vital sign information of the target can be obtained at the same time. Compared with the CW radar, the FMCW radar has the characteristics of low system power consumption, simple structure, strong anti-interference capability, high resolution and the like, thereby playing an important role in the field of vital sign detection.
Frequency Modulated Continuous Wave (FMCW) radar transmits Frequency Modulated Continuous waves to a human body in a non-contact manner, and heart beat and respiration of the human body are monitored according to echo signals reflected back. Under an ideal test condition, an FMCW radar is placed in front of the chest of a human body to collect signals and monitor vital signs, the distance of a target to be detected and phase changes of different time under corresponding distance units are easily determined, but under an actual condition, especially in some complex test environments such as indoor and in a vehicle, a plurality of strong reflectors exist in the range of radar antenna beams, generated strong clutter can cover the target signals, a 'shielding effect' is generated, the detection precision of the distance of a real target is reduced, and therefore the estimation performance of heartbeat and respiration rate is reduced. The correct selection of range units is an important prerequisite for extracting the respiratory frequency and the heartbeat frequency, and particularly in a multi-target scene, the range units with targets need to be selected because signals among the targets have mutual interference and are interfered by other obstacles in the environment.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a method for detecting the distance of a frequency modulation continuous wave radar living body, which can accurately detect a distance unit containing a vital sign signal under a complex environment with more interference sources, so that a correct distance dimension can be found, a threshold value of target detection is not required to be preset, the false alarm and false alarm probability of real human target detection is reduced, and the vital sign signals of multiple targets in different distance units are ensured to be successfully extracted.
In order to achieve the purpose, the invention adopts the technical scheme that:
a vital sign detection method of a frequency modulated continuous wave radar comprises the following steps:
step 1, radar transmits a linear frequency modulation signal s (t), after the transmission signal is reflected by a human body target, an Intermediate Frequency (IF) signal y (t), namely an echo signal, is obtained by applying low-pass filtering after the transmission signal and the receiving signal are mixed;
step 2, sampling M actually measured echo signals y (t) and carrying out Fourier transform processing to obtain a one-dimensional distance spectrogram containing vital sign signals, wherein the number of sampling points in each period is N;
step 3, performing FFT operation on the M actually measured echo signals Y (t) on the fast time dimension and the slow time dimension respectively to obtain a two-dimensional frequency spectrum matrix image Y2dfft(M, N), wherein M is 0-M, and N is 0-N;
step 4, after removing static obstacles and low-frequency data in the two-dimensional spectrogram, carrying out autocorrelation processing on the vital sign signals; the method comprises the following specific steps:
step 4.1, extracting a two-dimensional frequency spectrum matrix diagram Y2dfftThe signals of each column of (m, n), i.e. the signals Y in each distance dimension after being processed are extractedn(t)＝Y2dfft(:,n)；
Step 4.2, remove signal YnThe low-dimensional interference component in (t) yields Yn(t)＝Yn(b is M), and the value of b is 5-10;
step 4.3, performing autocorrelation processing on the result of step 4.2 to obtain an autocorrelation two-dimensional spectrogram, namely
Wherein l represents each element on the sequence;
step 4.4, one matrix coefficient can be obtained for each distance unit, and the maximum matrix coefficient is obtained, that is, r is the distance unit where the target is located, that is, r is argmaxRn(t), the value of r is 0,1.
Step 5, selecting correct distance unitThen, the distance spectrogram obtained in the step 2 is selected according to the distance r, and the signals corresponding to the vital signs on the distance unit, which comprise the vital sign signals r (t) of the measured target, are selected, and the signals r (t) r (r) are composed of respiration signals and heartbeat signalsr(t)+rh(t)＝arcos(2πfrt)+ahcos(2πfht) in which ar、ah、fr、fhRepresenting the amplitude of the thorax due to breathing and heartbeat, respectively, and their frequency.
Step 2 is performed after step 3 or after step 4.
By adopting the scheme, the distance unit containing the vital sign signals can be accurately detected in a complex environment with more interference sources, so that the correct distance dimension can be found, the threshold value of target detection is not required to be preset, the false alarm and false alarm probability of real human target detection is reduced, and the target vital sign signals in different distance units can be successfully extracted. The vital sign signal extraction depends on the correct selection of the distance dimension to a great extent, the distance unit where the measured target is located is found, and a very important step is taken for solving the accurate vital sign signal.
Drawings
FIG. 1 is a flow chart of the operation of the present invention;
FIG. 2 is a diagram showing the comparison of simulation results of the method of the present invention and the conventional method.
Detailed Description
As shown in fig. 1, the present invention discloses a vital sign detection method for a frequency modulated continuous wave radar, which specifically comprises the following steps:
step 1, radar transmits a linear frequency modulation signal s (t), after the transmission signal is reflected by a human body target, an Intermediate Frequency (IF) signal y (t), namely an echo signal, is obtained by applying low-pass filtering after the transmission signal and the receiving signal are mixed;
step 2, sampling M actually measured echo signals y (t) and carrying out Fourier transform processing to obtain a one-dimensional distance spectrogram containing vital sign signals; the number of sampling points in each period is N, the one-dimensional distance spectrogram shows the target condition at different distances, and the signals related to the target vital signs are on the corresponding distance dimension.
Step 3, performing FFT operation on the M actually measured echo signals Y (t) on the fast time dimension and the slow time dimension respectively to obtain a two-dimensional frequency spectrum matrix image Y2dfft(M, N), wherein M is 0-M, and N is 0-N.
And 4, removing static obstacles and low-frequency data in the two-dimensional spectrogram, and then carrying out autocorrelation processing on the vital sign signals. The method comprises the following specific steps:
step 4.1, extracting a two-dimensional frequency spectrum matrix diagram Y2dfftThe signals of each column of (m, n), i.e. the signals Y in each distance dimension after being processed are extractedn(t)＝Y2dfft(:,n)；
Step 4.2, remove signal YnThe low-dimensional interference component in (t) yields Yn(t)＝Yn(b is M), and the value of b is 5-10; the data in the dimension 0 of the spectrogram are stationary obstacles, and 1-b belong to low-frequency data.
Step 4.3, performing autocorrelation processing on the result of step 4.2 to obtain an autocorrelation two-dimensional spectrogram, namely
Wherein l represents each element on the sequence;
step 4.4, one matrix coefficient can be obtained for each distance unit, and the maximum matrix coefficient is obtained, that is, r is the distance unit where the target is located, that is, r is arg maxRn(t), the value of r is 0,1.
Step 5, after selecting the correct distance unit, selecting the distance of the distance spectrogram obtained in step 2 according to the distance r, and selecting the signal corresponding to the vital sign on the distance unit, wherein the signal comprises the vital sign signal r (t) of the target to be measured, and r (t) r (r) is composed of a respiration signal and a heartbeat signalr(t)+rh(t)＝arcos(2πfrt)+ahcos(2πfht) in which ar、ah、fr、fhRepresenting the amplitude of the thorax due to respiration and heartbeat, respectivelyAnd their frequencies. By this, a range unit with vital sign signals is found.
After one-dimensional FFT, the data expression (one-dimensional distance spectrum) for each column of data (i.e. each distance dimension) can be expressed as:
when R ═ arg max R is found in step 4n(t), equivalent to finding a suitable R ═ R0Substituting formula (2) and then substituting formula (1) with formula (2) to obtain the data of the column
R is equal to R0R (t) can be obtained by substituting the formula (3).
Step 2 above may be performed after step 3 or step 4, as long as it is ensured that it is performed after step 1 and before step 5.
To elaborate the technical scheme of the invention, an embodiment will be illustrated below.
Detecting two targets by using a frequency modulation continuous wave radar with the center frequency of 77G, the bandwidth of 4GHz and the sampling frequency of 5MHz, carrying out one-dimensional Fourier transform on the intermediate frequency signal y (t) to obtain a distance spectrogram, and carrying out distance detection for finding out a vital sign signal corresponding to the target in the one-dimensional spectrogram.
Performing Fourier transform on the one-dimensional distance spectrogram again to obtain a two-dimensional frequency spectrum matrix Y2dfft(m, n), extracting Y from each row of data of the matrixn(t)＝Y2dfft(n) removing DC components and low frequency interference Y in some environmentsn(t)＝Yn(5: M), each range bin signal is subjected to an autocorrelation process
And obtaining an autocorrelation two-dimensional spectrogram, wherein each distance unit has a corresponding matrix coefficient, and the maximum matrix coefficient is selected to be the distance unit where the target is located.
By extracting the signals on the corresponding distance units, a signal r (t) containing the respiratory information and the heartbeat information of the detected object can be obtained.
Under the condition that the simulation target has a known distance, a plurality of groups of data are collected, Gaussian white noise with different dB is added, the distance of the target is solved, and the method (MCSM in a corresponding diagram) of the invention is compared with the existing method (1 ] [2] [3] in the corresponding diagram), and the result is shown in figure 2. In FIG. 2, the curves of method [2] and method [3] are substantially identical.
The method [1] adopts a parameter based on the product of the amplitude and the phase of a signal to detect the range of the target distance, the method [2] considers the reflected power from the target and adopts the maximum average amplitude to detect the distance of the real target, and the method [3] adopts the distance detection algorithm of the maximum average power. From the simulation results, it can be seen that, as the gaussian white noise increases, the distance algorithm proposed herein depends on the signal period of the signal itself, rather than being determined only by amplitude or phase, and thus shows a high accuracy in distance extraction, and can also show a high accuracy in a complex environment with relatively large noise.
The above description is only exemplary of the present invention and is not intended to limit the technical scope of the present invention, so that any minor modifications, equivalent changes and modifications made to the above exemplary embodiments according to the technical spirit of the present invention are within the technical scope of the present invention.
Claims (2)
1. A vital sign detection method of a frequency modulation continuous wave radar is characterized by comprising the following steps: the method comprises the following steps:
step 1, radar transmits a linear frequency modulation signal s (t), after the transmission signal is reflected by a human body target, an Intermediate Frequency (IF) signal y (t), namely an echo signal, is obtained by applying low-pass filtering after the transmission signal and the receiving signal are mixed;
step 2, sampling M actually measured echo signals y (t) and carrying out Fourier transform processing to obtain a one-dimensional distance spectrogram containing vital sign signals, wherein the number of sampling points in each period is N;
step 3, performing FFT operation on the M actually measured echo signals Y (t) on the fast time dimension and the slow time dimension respectively to obtain a two-dimensional frequency spectrum matrix image Y2dfft(M, N), wherein M is 0-M, and N is 0-N;
step 4, after removing static obstacles and low-frequency data in the two-dimensional spectrogram, carrying out autocorrelation processing on the vital sign signals; the method comprises the following specific steps:
step 4.1, extracting a two-dimensional frequency spectrum matrix diagram Y2dfftThe signals of each column of (m, n), i.e. the signals Y in each distance dimension after being processed are extractedn(t)＝Y2dfft(:,n)；
Step 4.2, remove signal YnThe low-dimensional interference component in (t) yields Yn(t)＝Yn(b is M), and the value of b is 5-10;
step 4.3, performing autocorrelation processing on the result of step 4.2 to obtain an autocorrelation two-dimensional spectrogram, namely
Wherein l represents each element on the sequence;
step 4.4, one matrix coefficient can be obtained for each distance unit, and the maximum matrix coefficient is obtained, namely, R is the distance unit where the target is located, namely, R is arg max Rn(t), the value of r is 0,1.
Step 5, after selecting the correct distance unit, selecting the distance of the distance spectrogram obtained in step 2 according to the distance r, and selecting the signal corresponding to the vital sign on the distance unit, wherein the signal comprises the vital sign signal r (t) of the target to be measured, and r (t) r (r) is composed of a respiration signal and a heartbeat signalr(t)+rh(t)＝arcos(2πfrt)+ahcos(2πfht) in which ar、ah、fr、fhRepresenting the amplitude of the thorax due to breathing and heartbeat, respectively, and their frequency.
2. A method of vital sign monitoring of frequency modulated continuous wave radar as claimed in claim 1, wherein: step 2 is performed after step 3 or after step 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111652208.8A CN114355329A (en) | 2021-12-30 | 2021-12-30 | Method for detecting vital sign distance of frequency modulated continuous wave radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111652208.8A CN114355329A (en) | 2021-12-30 | 2021-12-30 | Method for detecting vital sign distance of frequency modulated continuous wave radar |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114355329A true CN114355329A (en) | 2022-04-15 |
Family
ID=81103809
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111652208.8A Pending CN114355329A (en) | 2021-12-30 | 2021-12-30 | Method for detecting vital sign distance of frequency modulated continuous wave radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114355329A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117031464A (en) * | 2023-07-25 | 2023-11-10 | 南京航空航天大学 | Method and device for distinguishing interference of moving living body and moving target in cabin |
-
2021
- 2021-12-30 CN CN202111652208.8A patent/CN114355329A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117031464A (en) * | 2023-07-25 | 2023-11-10 | 南京航空航天大学 | Method and device for distinguishing interference of moving living body and moving target in cabin |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111352102B (en) | Multi-target number detection method and device based on frequency modulation continuous wave radar | |
| US20220142478A1 (en) | Radar cardiography: a precise cardiac data reconstruction method | |
| CN111856455A (en) | Multi-target heart rate and respiration measuring method and system matched with different radar bandwidths | |
| CN110161491B (en) | Ranging and respiratory frequency estimation method for weak life body | |
| CN104215951A (en) | System and method for detecting low-speed small target under sea cluster background | |
| CN108196241B (en) | Hough transform-based high-speed moving target speed estimation method | |
| CN112220464A (en) | Human body respiration and heartbeat signal detection method and system based on UWB radar | |
| CN113854981A (en) | Millimeter wave radar-based sports multi-person vital sign monitoring method | |
| CN115399747B (en) | Multi-person vital sign monitoring method based on millimeter wave radar | |
| KR20210006225A (en) | Method and Apparatus for Measuring Multi Object Bio Signal | |
| CN110879388A (en) | Non-contact detection distinguishing method for human and animal based on IR-UWB (infrared-ultra wide band) biological radar signals | |
| KR20210001575A (en) | Real-time cardiac rate detection apparatus in noisy environment and method thereof | |
| CN111505631A (en) | Heart rate estimation algorithm based on L FMCW radar | |
| CN115460980A (en) | Non-contact respiration monitoring method based on Doppler radar | |
| CN116077044A (en) | Vital sign detection method based on millimeter wave radar | |
| CN110837079A (en) | Target detection method and device based on radar | |
| CN115736886A (en) | Multi-target respiration rate parameter estimation method based on millimeter wave radar | |
| CN114355329A (en) | Method for detecting vital sign distance of frequency modulated continuous wave radar | |
| CN114296076A (en) | Indoor fall detection method and device based on millimeter wave radar | |
| CN116299299B (en) | Speed disambiguation method, device, radar equipment and storage medium | |
| CN116299303B (en) | Speed disambiguation method, device, radar equipment and storage medium | |
| CN115407279B (en) | Pulse compression radar distance Doppler coupling error compensation method | |
| Park et al. | Multipath signal mitigation for indoor localization based on mimo fmcw radar system | |
| CN114903469A (en) | Vital sign monitoring method and device | |
| CN115754967A (en) | Human body falling detection method based on millimeter wave radar |
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 |