CN116148850A - Method, system and storage medium for detecting remote human respiratory signals - Google Patents

Method, system and storage medium for detecting remote human respiratory signals Download PDF

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CN116148850A
CN116148850A CN202310438788.3A CN202310438788A CN116148850A CN 116148850 A CN116148850 A CN 116148850A CN 202310438788 A CN202310438788 A CN 202310438788A CN 116148850 A CN116148850 A CN 116148850A
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雷文太
庞泽邦
粟毅
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Abstract

The invention provides a method, a system and a storage medium for detecting a long-distance human respiratory signal, wherein the method comprises the following steps: transmitting a plurality of probe waves to a target object through a target radar based on a plurality of different carrier frequencies; receiving scattered echoes of multiple detection waves reflected by a target object under different carrier frequencies respectively; acquiring a plurality of time domain echoes under corresponding carrier frequencies based on the plurality of scattered echoes, and forming the plurality of time domain echoes into a time domain echo record section under the corresponding carrier frequencies; calculating according to the time domain echo record profile to generate a one-dimensional range profile of the time domain echo; selecting a distance interval with periodic variation in the one-dimensional distance image; performing first FFT spectrum characteristic analysis on each time domain echo in a distance interval under different carrier frequencies to obtain first spectrum characteristics under different carrier frequencies; if the first frequency spectrum characteristics under different carrier frequencies are the same, the presence of a respiratory signal in the scattered echo is judged. The invention has the effect of higher accuracy of detecting the remote human respiratory signals.

Description

Method, system and storage medium for detecting remote human respiratory signals
Technical Field
The invention relates to the technical field of radar signal processing, in particular to a method, a system and a storage medium for detecting a long-distance human respiratory signal.
Background
Human vital sign signal detection based on radar detection is an important research direction in the field of radar signal processing. Since the beginning of the 70 s of the 20 th century, continuous-wave (CW) doppler radar has been widely used for victim searching under seismic ruins. It emits a monotone CW signal, demodulates the phase change of the reflected wave to obtain the respiration and heartbeat frequency of the human target. This is because the phase change of the reflected wave is linearly proportional to the chest displacement caused by cardiopulmonary activity. The radar has a simple structure and limited range resolution.
In order to provide accurate distance information and vital sign estimation, a chirped continuous wave (Linear Frequency Modulated Continuous Wave, LFMCW) radar, a stepped frequency continuous wave (Step Frequency Continuous Wave, SFCW) radar and an Impulse Radio ultra wideband (IR-UWB) radar have also been proposed. The LFMCW radar and the SFCW radar respectively transmit a linear frequency modulated continuous wave and a step frequency continuous wave, and then detect a frequency/phase change of an echo signal and a corresponding change history along a recording time axis to obtain distance and vital sign information of a human body. However, their signal generators require low phase noise, fast settling time and accurate frequency control, and thus the radar structure is complicated, and cost and power consumption are high. The pulse radio ultra wideband radar emits an ultra short pulse and then obtains distance information and vital sign estimates by detecting the time-of-flight (TOF) of the reflected pulse and the corresponding TOF changes along the recorded time axis.
Since the above method can only transmit a Signal energy level of a weak intensity, a Signal-to-noise ratio (SNR) is reduced, thereby reducing detection accuracy. In addition, the above method is also susceptible to noise and radio frequency interference (Radio Frequency Interference, RFI). Therefore, the method is only suitable for detecting human respiratory signals at a short distance, such as personnel detection in seismic ruins, respiratory detection when infants or old people are bedridden, and the like. In the remote personnel detection occasion of hundred meters, such as personnel latency detection in security protection, the transmission and receiving propagation paths of the radar become complex, and the propagation paths have the effects of cluster shake, weak shielding objects, movement or micro movement of a human body and the like, so that the difficulty of detecting the breathing signals of the human body is increased, and if the detection is performed by adopting the method, the accuracy is lower.
Disclosure of Invention
The invention provides a method, a system and a storage medium for detecting a remote human respiratory signal, which are used for solving the problems of high detection difficulty and low detection precision of the remote human respiratory signal.
In a first aspect, the present invention provides a method of remote human respiratory signal detection, the method comprising the steps of:
Transmitting a plurality of probe waves to a target object through a target radar based on a plurality of different carrier frequencies;
receiving scattering echoes reflected by the target object for a plurality of times under different carrier frequencies respectively;
acquiring a plurality of time domain echoes under corresponding carrier frequencies based on the plurality of scattered echoes, and forming the plurality of time domain echoes into a time domain echo recording section under the corresponding carrier frequencies;
preprocessing the time domain echoes in the time domain echo recording profiles under different carrier frequencies respectively, and calculating and generating a one-dimensional range profile of the time domain echo according to the time domain echo recording profiles;
selecting a distance interval with periodic variation in the one-dimensional distance image;
performing first FFT spectrum characteristic analysis on each time domain echo in the distance interval under different carrier frequencies to obtain first spectrum characteristics under different carrier frequencies;
judging whether the first frequency spectrum characteristics under different carrier frequencies are the same or not;
and if the first frequency spectrum characteristics at different carrier frequencies are the same, judging that respiratory signals exist in the scattered echo.
Optionally, the acquiring a plurality of time domain echoes at the corresponding carrier frequencies based on the plurality of scattered echoes, and forming the plurality of time domain echoes into a time domain echo record profile at the corresponding carrier frequencies includes the following steps:
Acquiring the amplitude and the phase of the scattered echo at each frequency point relative to the detection wave according to a plurality of preset frequency points to obtain frequency response sequences corresponding to the frequency points;
converting the frequency response sequence into a time domain to obtain a time domain echo corresponding to the single scattered echo;
and forming the time domain echo corresponding to all the scattered echoes under the same carrier frequency into a time domain echo record section.
Optionally, the method further comprises the steps of:
if the first frequency spectrum features at different carrier frequencies are different, decomposing each time domain echo at different carrier frequencies in the distance interval by an empirical mode decomposition method to obtain a plurality of eigenmode functions at different carrier frequencies;
selecting a plurality of target eigen mode functions from a plurality of eigen mode functions at different carrier frequencies according to a preset frequency range, and adding the plurality of target eigen mode functions to obtain superimposed waveforms at different carrier frequencies;
performing second FFT spectrum characteristic analysis on the superimposed waveforms under different carrier frequencies to obtain second spectrum characteristics under different carrier frequencies;
judging whether the second frequency spectrum characteristics under different carrier frequencies are the same or not;
And if the second frequency spectrum characteristics at different carrier frequencies are the same, judging that the respiratory signal exists in the scattered echo.
Optionally, the method further comprises the steps of:
if the second frequency spectrum characteristics under different carrier frequencies are different, performing cyclic cross-correlation spectrum operation on the superimposed waveforms under different carrier frequencies to obtain a plurality of slice frequency spectrums under different carrier frequencies;
selecting the slice spectrum with the largest energy from a plurality of slice spectrums under different carrier frequencies as a target slice spectrum;
performing third FFT spectrum characteristic analysis on the target slice spectrum under different carrier frequencies to obtain third spectrum characteristics under different carrier frequencies;
judging whether the third frequency spectrum characteristics under different carrier frequencies are equal or whether a frequency multiplication relationship exists;
if the third frequency spectrum features under different carrier frequencies are equal or have a frequency multiplication relation, judging that the respiratory signal exists in the scattered echo;
and if the third frequency spectrum characteristics under different carrier frequencies are not equal and the frequency multiplication relation does not exist, judging that the respiratory signal does not exist in the scattered echo.
Optionally, the decomposing, by an empirical mode decomposition method, each time domain echo in the distance interval at different carrier frequencies to obtain a plurality of eigenmode functions at different carrier frequencies includes the following steps:
Taking the time domain echo as a target signal, and acquiring a maximum value point and a minimum value point of the target signal;
fitting a maximum value envelope curve based on the maximum value points, and fitting a minimum value envelope curve based on the minimum value points;
calculating a mean value envelope of the time domain echo according to the maximum value envelope and the minimum value envelope;
subtracting the mean envelope from the target signal to obtain an alternative signal;
judging whether the alternative signal is an eigenmode function component of the time domain echo;
if the alternative signal is not the eigenmode function component of the time domain echo, taking the alternative signal as the target signal, and repeatedly acquiring an extreme point of the target signal to acquire a new alternative signal until the new alternative signal is the eigenmode function component of the time domain echo;
taking the eigenmode function component as the target signal, and repeatedly obtaining extreme points of the target signal to obtain the eigenmode function component of the time domain echo multi-order until the eigenmode function allowance of any one order or the eigenmode function component is smaller than a preset decomposition threshold;
or alternatively, the first and second heat exchangers may be,
And the residual of the eigenmode function up to any one order is a monotonic function or constant.
Optionally, the method further comprises the steps of:
if the candidate signal is an eigenmode function component of the time domain echo, the eigenmode function component is used as the target signal, and extreme points of the target signal are repeatedly obtained to obtain eigenmode function components of multiple orders of the time domain echo until eigenmode function allowance of any one order or the eigenmode function component is smaller than the decomposition threshold;
or alternatively, the first and second heat exchangers may be,
and the residual of the eigenmode function up to any one order is a monotonic function or constant.
Optionally, the scattered echo includes a doppler signal generated based on the motion of the object, a micro-doppler signal generated based on the respiration and heartbeat of the human body, and an ambient clutter signal.
Alternatively, the scattered echo is expressed as:
Figure SMS_1
wherein:
Figure SMS_5
for the scattered echo +.>
Figure SMS_6
Said Doppler signal representing a moving object, < >>
Figure SMS_9
For the carrier frequency of the target radar, +.>
Figure SMS_3
Radar cross-sectional area of the moving object, < >>
Figure SMS_7
Representing the speed of the moving object, +.>
Figure SMS_11
Representing a distance of the moving object;
Figure SMS_12
Representing said micro Doppler signal, >
Figure SMS_2
RCS representing jog moiety,/->
Figure SMS_8
Representing the vibration frequency of the chest or heart, +.>
Figure SMS_10
Representing the ambient clutter signal,
Figure SMS_13
Representing imaginary units, ++>
Figure SMS_4
Indicating the speed of light.
In a second aspect, the present invention also provides a system for remote human respiratory signal detection, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as described in the first aspect when executing the computer program.
In a third aspect, the invention also provides a computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of the method as described in the first aspect.
The beneficial effects of the invention are as follows:
the method for detecting the long-distance human respiratory signals provided by the invention comprises the following steps: transmitting a plurality of probe waves to a target object through a target radar based on a plurality of different carrier frequencies; receiving scattering echoes reflected by the target object for a plurality of times under different carrier frequencies respectively; acquiring a plurality of time domain echoes under corresponding carrier frequencies based on the plurality of scattered echoes, and forming the plurality of time domain echoes into a time domain echo recording section under the corresponding carrier frequencies; preprocessing the time domain echoes in the time domain echo recording profiles under different carrier frequencies respectively, and calculating and generating a one-dimensional range profile of the time domain echo according to the time domain echo recording profiles; because the human breath belongs to a periodic variation, selecting a distance interval with the periodic variation in the one-dimensional distance image; performing first FFT spectrum characteristic analysis on each time domain echo in the distance interval under different carrier frequencies to obtain first spectrum characteristics under different carrier frequencies; judging whether the first frequency spectrum characteristics under different carrier frequencies are the same or not; and if the first frequency spectrum characteristics at different carrier frequencies are the same, judging that respiratory signals exist in the scattered echo. By the method, the human respiratory signal can be detected under the condition of long distance and interference.
Drawings
Fig. 1 is a flow chart of a method for detecting a respiration signal of a human body at a long distance.
FIG. 2 is a schematic diagram of a one-dimensional range profile of a detection process of a 10G frequency detection wave under a dense shielding state of a 110m simulator.
Fig. 3 is a schematic diagram of a time domain echo of a detection process of a 10G frequency detection wave in a dense shielding state of a 110m simulator.
Fig. 4 is a schematic diagram of a time domain echo FFT of a detection process of a 10G frequency detection wave in a 110m simulator dense shielding state.
Fig. 5 is an IMF signal FFT schematic diagram of a detection process of a 10G frequency detection wave in a 110m simulator close-shielded state.
Fig. 6 is a schematic diagram of a target slice spectrum FFT of a detection process of a 10G frequency detection wave in a 110m simulator close-shielding state.
FIG. 7 is a schematic diagram of a one-dimensional range profile of the detection process of the 8.5G frequency detection wave in a dense shielding state of the 110m simulator.
Fig. 8 is a schematic diagram of a time domain echo of the 8.5G frequency probe in a 110m simulator close-shielded state.
Fig. 9 is a schematic diagram of a time domain echo FFT of an 8.5G frequency probe in a 110m simulator close-shielded state.
Fig. 10 is an IMF signal FFT schematic diagram of the 8.5G frequency probe wave detection process in the 110m simulator close-shielded state.
FIG. 11 is a schematic diagram of a target slice spectrum FFT of an 8.5G frequency detection wave detection process in a 110m simulator dense shielding state.
FIG. 12 is a schematic diagram of a one-dimensional range profile of a detection process of a 10G frequency detection wave under a dense shielding state of a 110m target person.
Fig. 13 is a schematic diagram of a time domain echo of a detection process of a 10G frequency detection wave under a 110m target person close shielding state.
Fig. 14 is a schematic diagram of time domain echo FFT of a detection process of a 10G frequency detection wave in a 110m target person close shielding state.
Fig. 15 is an IMF signal FFT schematic diagram of a detection process of a 10G frequency detection wave in a 110m target person close shielding state.
Fig. 16 is a schematic diagram of a target slice spectrum FFT of a detection process of a 10G frequency detection wave in a 110m target person close shielding state.
FIG. 17 is a schematic diagram of a one-dimensional range profile of the 8.5G frequency probe wave detection process in a 110m target person close-shielding state.
Fig. 18 is a schematic diagram of a time domain echo of the 8.5G frequency probe in a 110m target person close-shielding state.
Fig. 19 is a schematic diagram of time domain echo FFT of the 8.5G frequency probe wave detection process in a 110m target person close-shielding state.
Fig. 20 is an IMF signal FFT schematic diagram of the 8.5G frequency probe wave detection process in the 110m target person close-shielding state.
Fig. 21 is a schematic diagram of a target slice spectrum FFT of an 8.5G frequency detection wave detection process in a 110m target person close-shielding state.
Detailed Description
The invention discloses a method for detecting a long-distance human respiratory signal.
In one embodiment, referring to fig. 1, the method for detecting a remote human respiratory signal specifically includes the following steps:
s101, transmitting detection waves to a target object for multiple times based on multiple different carrier frequencies through a target radar.
Wherein the target object has different contents according to different scenesThe target object may be an obstacle with a hidden human body, or a target human body which is desired to be detected and located at a long distance, which is generally a distance of 100m or more. The number of carrier frequencies set by the target radar is 2 or more, and in the present embodiment, the number of carrier frequencies set by the target radar is assumed to be 2, respectively denoted as
Figure SMS_16
，
Figure SMS_18
. When the carrier frequency of the target radar is +.>
Figure SMS_20
When the working frequency interval of the radar is
Figure SMS_15
. When the carrier frequency of the target radar is +.>
Figure SMS_17
The operating frequency interval of the radar is +.>
Figure SMS_19
And->
Figure SMS_21
And->
Figure SMS_14
Are not overlapped with each other.
For the working frequency interval of any carrier frequency, the frequency stepping interval is assumed to be
Figure SMS_22
The interval is provided with +.>
Figure SMS_23
Frequency points. When the target radar carries out sweep frequency detection on a target object, a transmitter of the target radar generates detection waves of the frequency at each frequency point of a working frequency interval, and the detection waves are sine waves and are transmitted through a transmitting antenna of the target radar.
S102, respectively receiving scattered echoes of multiple detection waves reflected by the target object under different carrier frequencies.
The receiver of the target radar can receive scattered echoes of multiple detection waves reflected by the target object under different carrier frequencies. The scattered echoes include Doppler signals generated based on object motion, micro Doppler signals generated based on target human breath and heartbeat, and ambient clutter signals. The object motion includes non-living objects that move freely under natural laws, such as a shaky bush, and also includes human body motion of a target human body.
The scattered echo is specifically expressed as:
Figure SMS_24
wherein:
Figure SMS_27
for the scattered echo +.>
Figure SMS_30
Said Doppler signal representing a moving object, < >>
Figure SMS_33
For the carrier frequency of the target radar, +.>
Figure SMS_26
Radar cross-sectional area of the moving object, < >>
Figure SMS_31
Representing the speed of the moving object, +.>
Figure SMS_34
Representing a distance of the moving object;
Figure SMS_36
Representing said micro Doppler signal,>
Figure SMS_25
RCS representing jog moiety,/->
Figure SMS_29
Representing the vibration frequency of the chest or heart, +.>
Figure SMS_32
Representing the ambient clutter signal,
Figure SMS_35
Representing imaginary units, ++>
Figure SMS_28
Indicating the speed of light.
In the present embodiment, the respiratory signal of the target human body is mainly used as the detection target, and therefore, the signal model of the scattered echo can be simplified as:
Figure SMS_37
Wherein,,
Figure SMS_38
representing the frequency of the respiratory signal.
Assume that the transmitter of the target radar transmits a carrier frequency
Figure SMS_40
Is->
Figure SMS_44
Carrier frequency->
Figure SMS_48
Is->
Figure SMS_41
Two scattering echoes corresponding to the target radar receiver are received by carrier frequency signals of two different frequencies>
Figure SMS_45
And->
Figure SMS_51
The method comprises the following steps of:
Figure SMS_52
wherein the subscript 1 indicates the carrier frequency +.>
Figure SMS_39
Corresponding to the response parameters of the scattered echo, subscript 2 indicates the carrier frequency +.>
Figure SMS_43
Response parameters corresponding to scattered echoes, speed +.>
Figure SMS_47
Distance->
Figure SMS_50
And respiratory frequency->
Figure SMS_42
All are unchanged.
Figure SMS_46
And->
Figure SMS_49
Respectively, doppler signals and micro-doppler signals.
S103, acquiring a plurality of time domain echoes under the corresponding carrier frequency based on the plurality of scattered echoes, and forming the plurality of time domain echoes into a time domain echo recording section under the corresponding carrier frequency.
Wherein, along with the respiration of the long-distance human body, the target radar continuously transmits signals to the target object and receives scattered echoes. And performing mixing processing and time domain conversion processing in a working frequency interval corresponding to the carrier frequency, so that a time domain echo record profile of the working frequency interval can be obtained.
S104, preprocessing the time domain echoes in the time domain echo recording profiles under different carrier frequencies respectively, and calculating according to the time domain echo recording profiles to generate a one-dimensional range profile of the time domain echoes.
Wherein, the preprocessing comprises the steps of filtering, denoising and the like.
S105, selecting a distance interval with periodic variation in the one-dimensional distance image.
Because the human breath is a periodic action, the respiratory signal of the target human body will show periodic variation, so a distance interval with periodic variation in the one-dimensional distance image is selected.
S106, performing first FFT spectrum characteristic analysis on each time domain echo in the distance interval under different carrier frequencies to obtain first spectrum characteristics under different carrier frequencies.
Wherein the FFT spectral signature analysis is a discrete Fourier transform spectral signature analysis. Further illustrated by the illustration in the embodiment of step S102:
two scatter echoes
Figure SMS_53
And->
Figure SMS_54
The FFT of the mid doppler signal is: />
Figure SMS_55
Wherein:
Figure SMS_56
representing spectral amplitude +.>
Figure SMS_57
Representing the carrier frequency.
Two scatter echoes
Figure SMS_58
And->
Figure SMS_59
The FFT of the medium micro doppler signal is:
Figure SMS_60
thus, the post-FFT spectral peak position of the Doppler signal is related to the carrier frequency, while the post-FFT spectral peak position of the micro Doppler signal is independent of the carrier frequency.
S107, judging whether the first frequency spectrum characteristics under different carrier frequencies are the same, and if so, executing step S108; if the first frequency spectrum characteristics under different carrier frequencies are different, the time domain echo under different carrier frequencies is further analyzed by an empirical mode decomposition method.
S108, judging that a respiratory signal exists in the scattered echo.
In one embodiment, step S103, namely, acquiring a plurality of time domain echoes at the corresponding carrier frequencies based on the plurality of scattered echoes, and forming the plurality of time domain echoes into a time domain echo recording profile at the corresponding carrier frequencies specifically includes the following steps:
acquiring the amplitude and the phase of a scattered echo at each frequency point relative to the detection wave according to a plurality of preset frequency points, and obtaining a frequency response sequence corresponding to the plurality of frequency points;
converting the frequency response sequence into a time domain to obtain a time domain echo corresponding to the single scattered echo;
and forming the time domain echo corresponding to all the scattered echoes under the same carrier frequency into a time domain echo record section.
Wherein, the amplitude of the scattered echo at each frequency point relative to the detection wave is obtained
Figure SMS_61
And phase->
Figure SMS_62
When all are obtained
Figure SMS_63
After the amplitude and phase of the frequency points, the amplitude sequence is +.>
Figure SMS_64
And phase sequence->
Figure SMS_65
And are combined into a frequency response sequence.
A complete detection process is that the target radar transmitter transmits detection waves to the target object, and the detection waves pass through the target objectThe volume reflects back the scattered echoes and is received by the target radar receiver. For example, in a complete detection process, the distance between the target object and the target radar is that
Figure SMS_67
The number of frequency points is->
Figure SMS_71
Assume that a certain frequency point is +.>
Figure SMS_73
The frequency response of the frequency point is
Figure SMS_68
In this complete detection procedure +.>
Figure SMS_69
The frequency response sequence of the individual frequency points can be expressed as +.>
Figure SMS_72
The frequency response sequence is converted into the time domain, i.e. the time domain echo obtained by this complete detection process. When converting to the time domain, the sampling point number of the one-dimensional time domain echo is also set to be +.>
Figure SMS_75
The point is recorded as the time domain echo measured in the complete detection process
Figure SMS_66
. Along with the respiration of the long-distance human body, the complete detection process is continuously repeated, and multiple time domain echoes can be obtained and respectively marked as +.>
Figure SMS_70
，
Figure SMS_74
Etc.
And acquiring a plurality of time domain echoes through repeating the complete detection process for a plurality of times, and forming a two-dimensional digital matrix by the plurality of time domain echoes, namely a time domain echo record section. Time of dayThe domain echo recording profile can be expressed as one
Figure SMS_76
Comprising two dimensions. Wherein each column is each time domain echo acquired in each complete detection process, the length is N, and the dimension is a fast time dimension. In another dimension, which is a slow time dimension, the matrix has a total of M complete detection processes, and M time-domain echoes, each of which also varies with time.
In one embodiment, when step S107 is to determine whether the first spectrum features at different carrier frequencies are the same, and the first spectrum features at different carrier frequencies are different, the following specific steps are as follows:
if the first frequency spectrum features of the different carrier frequencies are different, decomposing each time domain echo in the distance interval of the different carrier frequencies by an empirical mode decomposition method to obtain a plurality of eigenmode functions of the different carrier frequencies;
selecting a plurality of target eigen mode functions from a plurality of eigen mode functions at different carrier frequencies according to a preset frequency range, and adding the plurality of target eigen mode functions to obtain superimposed waveforms at different carrier frequencies;
performing second FFT spectrum characteristic analysis on the superimposed waveforms under different carrier frequencies to obtain second spectrum characteristics under different carrier frequencies;
judging whether the second frequency spectrum characteristics under different carrier frequencies are the same or not;
and if the second frequency spectrum characteristics under different carrier frequencies are the same, judging that respiratory signals exist in the scattered echo.
Therein, in the empirical mode decomposition (Empirical Mode Decomposition, EMD) method it is assumed that any signal is composed of several finite eigenmode functions IMF. Under different carrier frequencies, the vibration of the breathing signal has stronger periodicity, and the Doppler motion of the human body has poorer frequency spectrum correlation under different carrier frequencies, so the frequency spectrum of the breathing signal can be extracted by adopting a processing method of empirical mode decomposition.
In this embodiment, if the judgment result of judging whether the second spectrum features at different carrier frequencies are the same is that the second spectrum features at different carrier frequencies are different, the following steps are specifically executed:
if the second frequency spectrum characteristics under different carrier frequencies are different, performing cyclic cross-correlation spectrum operation on the superimposed waveforms under different carrier frequencies to obtain a plurality of slice frequency spectrums under different carrier frequencies;
selecting a slice spectrum with the largest energy from a plurality of slice spectrums under different carrier frequencies as a target slice spectrum;
performing third FFT spectrum characteristic analysis on the target slice spectrums under different carrier frequencies to obtain third spectrum characteristics under different carrier frequencies;
judging whether third frequency spectrum characteristics under different carrier frequencies are equal or whether a frequency multiplication relation exists;
if the third frequency spectrum features under different carrier frequencies are equal or have a frequency multiplication relation, judging that respiratory signals exist in the scattered echo;
if the third frequency spectrum characteristics under different carrier frequencies are not equal and the frequency multiplication relation does not exist, judging that no breathing signal exists in the scattered echo.
Wherein, a zero-mean sequence is assumed
Figure SMS_77
Is periodic and periodic +.>
Figure SMS_78
The generalized stable process of the cyclic cross-correlation spectrum is satisfied, and the formula is as follows:
Figure SMS_79
Wherein,,
Figure SMS_80
，
Figure SMS_81
representing the desired function. For a random plateau process, the autocorrelation function can be expressed in the form of a fourier series:
Figure SMS_82
wherein,,
Figure SMS_83
representing the cycle frequency. Fourier coefficients of the cyclic cross-correlation function (cyclic autocorrelation function, CAF) are expressed as:
Figure SMS_84
from CAF, the determination is made
Figure SMS_85
The cyclic spectral density (cyclic spectrum density, CSD) is obtained as follows:
Figure SMS_86
if at least one fundamental cyclic frequency exists
Figure SMS_87
Make->
Figure SMS_88
Sequence->
Figure SMS_89
Is generalized polymorphic cyclostationary. In this state, each cycle frequency +.>
Figure SMS_90
In CSD, a series of symmetrical spectral peaks are corresponding, the spectral peak interval is
Figure SMS_91
Is an integer multiple of (a).
The signal with periodicity of the mean and autocorrelation is called cyclostationary signal, i.e. the signal fulfils the following filling conditions:
Figure SMS_92
wherein,,
Figure SMS_93
for periods of->
Figure SMS_94
For mean value->
Figure SMS_95
For instantaneous cross-correlation, expressed as:
Figure SMS_96
an autocorrelation function is the correlation of the function itself, and the maxima of the autocorrelation function can represent this periodicity when there is a periodic component in the function. The cross-correlation is the periodicity of two functions, and when the two functions have the same periodic component, the maximum value of the two functions can represent the periodic component, so that the two functions can be used for extracting the human respiratory signal with the periodic characteristic.
The periodic function can be expanded into a fourier series, resulting in:
Figure SMS_97
wherein the Fourier coefficients
Figure SMS_99
Indicating instantaneous cross-correlation +.>
Figure SMS_101
At frequency->
Figure SMS_103
Amplitude of the region and +.>
Figure SMS_100
。
Figure SMS_102
The frequency corresponding to the instantaneous cross-correlation is the accumulated result of the instantaneous moment and is the AND signal +.>
Figure SMS_104
Frequency discrimination of->
Figure SMS_105
For the circulation frequency +.>
Figure SMS_98
May be defined as a cyclic cross-correlation.
Fourier transforming the cyclic cross-correlation to obtain:
Figure SMS_106
Figure SMS_107
called spectral correlation, or spectral correlation function, or cyclic spectral density, is related to frequency +.>
Figure SMS_108
Cycle frequency
Figure SMS_109
Is a dual frequency planar function of (a).
Therefore, in the present embodiment, the method of calculating detection based on the cyclic cross correlation spectrum of different carrier frequencies is as follows:
the cyclic cross-correlation spectrum of Doppler signals generated by Doppler motion of human bodies under different carrier frequencies is as follows:
Figure SMS_110
therefore, the peak positions of the doppler signal cyclic cross correlation spectrum are:
Figure SMS_111
the same result can be obtained by frequency domain correlation.
In this embodiment, for micro doppler signals of respiratory motion of a human body at different carrier frequencies, the cyclic cross correlation spectrum is:
Figure SMS_112
therefore, the peak positions of the cyclic cross-correlation spectrum of the micro Doppler signal are:
Figure SMS_113
for the environmental clutter signals, the cyclic cross correlation spectrum has no specific rule. Consider that for a signal containing micro-Doppler features, the spectrum peak position of the cyclic cross-correlation spectrum is relatively unchanged at any carrier frequency; for Doppler signals, the cross correlation of different carrier frequencies or the auto correlation of the same carrier frequency can change the circular cross correlation spectrum. Selecting a slice spectrum with the largest energy from a plurality of slice spectrums in the cyclic cross-correlation spectrum as a target slice spectrum, and then carrying out FFT spectrum characteristic analysis on the target slice spectrum for the third time to obtain third spectrum characteristics under different carrier frequencies.
The first FFT spectral feature analysis, the second FFT spectral feature analysis, and the third FFT spectral feature analysis may be the same FFT spectral feature analysis or may be different FFT spectral feature analysis, and the terms "first", "second", and "third" are used herein only for distinction, and do not represent any other meaning. Similarly, the first spectral feature, the second spectral feature, and the third spectral feature may be the same spectral feature, or may be different spectral features, and the terms "first," "second," and "third" are used herein only for distinction and do not represent any other meaning.
Based on the test of the present embodiment, it is assumed that two carrier frequencies set by the target radar are carrier frequencies, respectively
Figure SMS_114
Carrier frequency of
Figure SMS_115
Bandwidth of each band->
Figure SMS_116
. The experimental scene is shown below, and the simulator and the target personnel arranged at the 110m position are respectively subjected to non-shielding and close-shielding experimental verification. The simulator is a periodically moving metal disc, the disc surface is opposite to the antenna, the amplitude is 3cm, and the period is 0.7Hz. The target person takes a front sitting posture.
The detection results are as follows:
the detection results of the 10G frequency detection wave in the 110m simulator dense shielding state are shown in FIGS. 2 to 6. Wherein, fig. 2 is a one-dimensional range profile of a 10G frequency detection wave detection process in a 110m simulator close-shielding state, fig. 3 is a time domain echo of the 10G frequency detection wave detection process in the 110m simulator close-shielding state, fig. 4 is a time domain echo FFT of the 10G frequency detection wave detection process in the 110m simulator close-shielding state, fig. 5 is an IMF signal FFT of the 10G frequency detection wave detection process in the 110m simulator close-shielding state, and fig. 6 is a target slice spectrum FFT of the 10G frequency detection wave detection process in the 110m simulator close-shielding state.
The detection results of the 8.5G frequency detection wave in the 110m simulator dense shielding state are shown in fig. 7 to 11. Wherein, fig. 7 is a one-dimensional range profile of the 8.5G frequency detection wave detection process in the 110m simulator close-shielding state, fig. 8 is a time domain echo of the 8.5G frequency detection wave detection process in the 110m simulator close-shielding state, fig. 9 is a time domain echo FFT of the 8.5G frequency detection wave detection process in the 110m simulator close-shielding state, fig. 10 is an IMF signal FFT of the 8.5G frequency detection wave detection process in the 110m simulator close-shielding state, and fig. 11 is a target slice spectrum FFT of the 8.5G frequency detection wave detection process in the 110m simulator close-shielding state.
The detection results of the 10G frequency detection wave in the 110m target person close shielding state are shown in fig. 12 to 16. Fig. 12 is a one-dimensional range profile of a 10G frequency detection wave detection process in a 110m target person close-shielding state, fig. 13 is a time domain echo of the 10G frequency detection wave detection process in the 110m target person close-shielding state, fig. 14 is a time domain echo FFT of the 10G frequency detection wave detection process in the 110m target person close-shielding state, fig. 15 is an IMF signal FFT of the 10G frequency detection wave detection process in the 110m target person close-shielding state, and fig. 16 is a target slice spectrum FFT of the 10G frequency detection wave detection process in the 110m target person close-shielding state.
The detection results of the 8.5G frequency detection wave in the 110m target person close shielding state are shown in fig. 17 to 21. Wherein, fig. 17 is a one-dimensional range profile of an 8.5G frequency detection wave detection process in a 110m target person close-shielding state, fig. 18 is a time domain echo of the 8.5G frequency detection wave detection process in the 110m target person close-shielding state, fig. 19 is a time domain echo FFT of the 8.5G frequency detection wave detection process in the 110m target person close-shielding state, fig. 20 is an IMF signal FFT of the 8.5G frequency detection wave detection process in the 110m target person close-shielding state, and fig. 21 is a target slice spectrum FFT of the 8.5G frequency detection wave detection process in the 110m target person close-shielding state.
In one embodiment, decomposing each time domain echo in a distance interval under different carrier frequencies by an empirical mode decomposition method to obtain a plurality of eigenmode functions under different carrier frequencies specifically includes the following steps:
taking the time domain echo as a target signal, and acquiring a maximum value point and a minimum value point of the target signal;
fitting a maximum value envelope curve based on the maximum value points, and fitting a minimum value envelope curve based on the minimum value points;
calculating according to the maximum value envelope line and the minimum value envelope line to obtain the average value envelope of the time domain echo;
Subtracting the average envelope from the target signal to obtain an alternative signal;
judging whether the candidate signal is an eigenmode function component of the time domain echo;
if the alternative signal is not the eigenmode function component of the time domain echo, taking the alternative signal as a target signal, and repeatedly acquiring an extreme point of the target signal to acquire a new alternative signal until the new alternative signal is the eigenmode function component of the time domain echo;
repeatedly acquiring extreme points of the target signal by taking the eigenmode function component as the target signal to acquire eigenmode function components of multiple orders of the time domain echo until the eigenmode function allowance or the eigenmode function component of any one order is smaller than a preset decomposition threshold;
or alternatively, the first and second heat exchangers may be,
the residual of the eigenmode function up to any one order is a monotonic function or constant.
The definition of the eigenmode function IMF is as follows:
(1) The number of local extremum points and zero crossing points of the function must be equal or differ by at most one in the whole time range;
(2) At any point in time, the mean envelope of the local maximum point maximum envelope and the local minimum point minimum envelope must be zero.
Assume that a certain time domain echo as a target signal is
Figure SMS_117
First the target signal can be found +.>
Figure SMS_118
Fitting a maximum envelope by means of a cubic spline function to all maximum points of (a)>
Figure SMS_119
The method comprises the steps of carrying out a first treatment on the surface of the Similarly, find the target signal +.>
Figure SMS_120
Fitting the minimum envelope of the signal by means of a cubic spline function>
Figure SMS_121
. Calculating the mean envelope of the time domain echo according to the maximum value envelope and the minimum value envelope>
Figure SMS_122
The calculation formula is as follows:
Figure SMS_123
subtracting the target signal sequence
Figure SMS_124
An alternative signal with low frequency removed is obtained>
Figure SMS_125
The calculation formula is as follows:
Figure SMS_126
alternative signal in general
Figure SMS_127
Not a stationary signal, does not satisfy the two conditions defined by the IMF, so the process of obtaining extreme points, fitting the envelope, calculating the mean envelope, and calculating the alternative signal is repeated, assuming that after k repeated steps (k is generally less than 10), the alternative signal->
Figure SMS_128
Meets the definition of the intrinsic mode function IMF, the target signal +.>
Figure SMS_129
The first order eigenmode function IMF component of (a) is:
Figure SMS_130
in this embodiment, if the determination result of determining whether the candidate signal is the eigenmode function component of the time-domain echo is that the candidate signal is the eigenmode function component of the time-domain echo, the following steps are specifically performed:
If the candidate signal is an eigenmode function component of the time domain echo, taking the eigenmode function component as a target signal, and repeatedly obtaining extreme points of the target signal to obtain eigenmode function components of multiple orders of the time domain echo until eigenmode function allowance or eigenmode function components of any one order are smaller than a decomposition threshold;
or alternatively, the first and second heat exchangers may be,
the residual of the eigenmode function up to any one order is a monotonic function or constant.
Wherein, to
Figure SMS_131
Repeat get->
Figure SMS_132
The procedure of (2) obtaining the second eigenmode function IMF component->
Figure SMS_133
Repeating the above steps until the intrinsic mode function IMF component of the nth order is +.>
Figure SMS_134
Or the intrinsic mode function margin thereof>
Figure SMS_135
Less than a preset decomposition threshold; or when the eigenmode function margin +.>
Figure SMS_136
When monotonic or constant, the EMD decomposition process stops.
Finally, the target signal
Figure SMS_137
After EMD decomposition, the product is obtained:
Figure SMS_138
in the method, in the process of the invention,
Figure SMS_139
as trend terms, represent the average trend or mean of the signal. Target signal->
Figure SMS_140
After EMD decomposition, the +.>
Figure SMS_141
The frequency is from high to lowIs a component of the eigenmode function IMF.
The invention also discloses a system for detecting the remote human respiratory signals, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for detecting the remote human respiratory signals when executing the computer program.
The implementation principle of the embodiment is as follows:
by calling the program, the following steps are executed: transmitting a plurality of probe waves to a target object through a target radar based on a plurality of different carrier frequencies; receiving scattered echoes of multiple detection waves reflected by a target object under different carrier frequencies respectively; acquiring a plurality of time domain echoes under corresponding carrier frequencies based on the plurality of scattered echoes, and forming the plurality of time domain echoes into a time domain echo record section under the corresponding carrier frequencies; preprocessing time domain echoes in the time domain echo recording profiles under different carrier frequencies respectively, and calculating according to the time domain echo recording profiles to generate a one-dimensional range profile of the time domain echoes; because the human breath belongs to a periodic variation, a distance interval with the periodic variation in the one-dimensional distance image is selected; performing first FFT spectrum characteristic analysis on each time domain echo in a distance interval under different carrier frequencies to obtain first spectrum characteristics under different carrier frequencies; judging whether the first frequency spectrum characteristics under different carrier frequencies are the same or not; if the first frequency spectrum characteristics under different carrier frequencies are the same, the presence of a respiratory signal in the scattered echo is judged. By the method steps, the human respiratory signal can be detected in the case of long distance and interference.
The invention also discloses a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of remote human respiratory signal detection as described above.
The implementation principle of the embodiment is as follows:
by calling the program, the following steps are executed: transmitting a plurality of probe waves to a target object through a target radar based on a plurality of different carrier frequencies; receiving scattered echoes of multiple detection waves reflected by a target object under different carrier frequencies respectively; acquiring a plurality of time domain echoes under corresponding carrier frequencies based on the plurality of scattered echoes, and forming the plurality of time domain echoes into a time domain echo record section under the corresponding carrier frequencies; preprocessing time domain echoes in the time domain echo recording profiles under different carrier frequencies respectively, and calculating according to the time domain echo recording profiles to generate a one-dimensional range profile of the time domain echoes; because the human breath belongs to a periodic variation, a distance interval with the periodic variation in the one-dimensional distance image is selected; performing first FFT spectrum characteristic analysis on each time domain echo in a distance interval under different carrier frequencies to obtain first spectrum characteristics under different carrier frequencies; judging whether the first frequency spectrum characteristics under different carrier frequencies are the same or not; if the first frequency spectrum characteristics under different carrier frequencies are the same, the presence of a respiratory signal in the scattered echo is judged. By the method steps, the human respiratory signal can be detected in the case of long distance and interference.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to imply that the scope of the present application is limited to such examples; the technical features of the above embodiments or in the different embodiments may also be combined under the idea of the present application, the steps may be implemented in any order, and there are many other variations of the different aspects of one or more embodiments in the present application as above, which are not provided in details for the sake of brevity.
One or more embodiments herein are intended to embrace all such alternatives, modifications and variations that fall within the broad scope of the present application. Any omissions, modifications, equivalents, improvements, and the like, which are within the spirit and principles of the one or more embodiments in the present application, are therefore intended to be included within the scope of the present application.

Claims (10)

1. A method for detecting a respiratory signal of a human body at a long distance, comprising the steps of:
transmitting a plurality of probe waves to a target object through a target radar based on a plurality of different carrier frequencies;
receiving scattering echoes reflected by the target object for a plurality of times under different carrier frequencies respectively;
Acquiring a plurality of time domain echoes under corresponding carrier frequencies based on the plurality of scattered echoes, and forming the plurality of time domain echoes into a time domain echo recording section under the corresponding carrier frequencies;
preprocessing the time domain echoes in the time domain echo recording profiles under different carrier frequencies respectively, and calculating and generating a one-dimensional range profile of the time domain echo according to the time domain echo recording profiles;
selecting a distance interval with periodic variation in the one-dimensional distance image;
performing first FFT spectrum characteristic analysis on each time domain echo in the distance interval under different carrier frequencies to obtain first spectrum characteristics under different carrier frequencies;
judging whether the first frequency spectrum characteristics under different carrier frequencies are the same or not;
and if the first frequency spectrum characteristics at different carrier frequencies are the same, judging that respiratory signals exist in the scattered echo.
2. The method for detecting a remote human respiratory signal according to claim 1, wherein the acquiring a plurality of time domain echoes at the corresponding carrier frequencies based on the plurality of scattered echoes and forming the plurality of time domain echoes into a time domain echo recording profile at the corresponding carrier frequencies includes the steps of:
acquiring the amplitude and the phase of the scattered echo at each frequency point relative to the detection wave according to a plurality of preset frequency points to obtain frequency response sequences corresponding to the frequency points;
Converting the frequency response sequence into a time domain to obtain a time domain echo corresponding to the single scattered echo;
and forming the time domain echo corresponding to all the scattered echoes under the same carrier frequency into a time domain echo record section.
3. The method of remote human breath signal detection according to claim 1, further comprising the steps of:
if the first frequency spectrum features at different carrier frequencies are different, decomposing each time domain echo at different carrier frequencies in the distance interval by an empirical mode decomposition method to obtain a plurality of eigenmode functions at different carrier frequencies;
selecting a plurality of target eigen mode functions from a plurality of eigen mode functions at different carrier frequencies according to a preset frequency range, and adding the plurality of target eigen mode functions to obtain superimposed waveforms at different carrier frequencies;
performing second FFT spectrum characteristic analysis on the superimposed waveforms under different carrier frequencies to obtain second spectrum characteristics under different carrier frequencies;
judging whether the second frequency spectrum characteristics under different carrier frequencies are the same or not;
and if the second frequency spectrum characteristics at different carrier frequencies are the same, judging that the respiratory signal exists in the scattered echo.
4. A method of remote human breath signal detection according to claim 3, further comprising the steps of:
if the second frequency spectrum characteristics under different carrier frequencies are different, performing cyclic cross-correlation spectrum operation on the superimposed waveforms under different carrier frequencies to obtain a plurality of slice frequency spectrums under different carrier frequencies;
selecting the slice spectrum with the largest energy from a plurality of slice spectrums under different carrier frequencies as a target slice spectrum;
performing third FFT spectrum characteristic analysis on the target slice spectrum under different carrier frequencies to obtain third spectrum characteristics under different carrier frequencies;
judging whether the third frequency spectrum characteristics under different carrier frequencies are equal or whether a frequency multiplication relationship exists;
if the third frequency spectrum features under different carrier frequencies are equal or have a frequency multiplication relation, judging that the respiratory signal exists in the scattered echo;
and if the third frequency spectrum characteristics under different carrier frequencies are not equal and the frequency multiplication relation does not exist, judging that the respiratory signal does not exist in the scattered echo.
5. A method for detecting a remote human respiratory signal according to claim 3, wherein the decomposing each time domain echo in the distance interval at different carrier frequencies by an empirical mode decomposition method to obtain a plurality of eigenmode functions at different carrier frequencies includes the following steps:
Taking the time domain echo as a target signal, and acquiring a maximum value point and a minimum value point of the target signal;
fitting a maximum value envelope curve based on the maximum value points, and fitting a minimum value envelope curve based on the minimum value points;
calculating a mean value envelope of the time domain echo according to the maximum value envelope and the minimum value envelope;
subtracting the mean envelope from the target signal to obtain an alternative signal;
judging whether the alternative signal is an eigenmode function component of the time domain echo;
if the alternative signal is not the eigenmode function component of the time domain echo, taking the alternative signal as the target signal, and repeatedly acquiring an extreme point of the target signal to acquire a new alternative signal until the new alternative signal is the eigenmode function component of the time domain echo;
taking the eigenmode function component as the target signal, and repeatedly obtaining extreme points of the target signal to obtain the eigenmode function component of the time domain echo multi-order until the eigenmode function allowance of any one order or the eigenmode function component is smaller than a preset decomposition threshold;
or alternatively, the first and second heat exchangers may be,
And the residual of the eigenmode function up to any one order is a monotonic function or constant.
6. The method of remote human breath signal detection according to claim 5, further comprising the steps of:
if the candidate signal is an eigenmode function component of the time domain echo, the eigenmode function component is used as the target signal, and extreme points of the target signal are repeatedly obtained to obtain eigenmode function components of multiple orders of the time domain echo until eigenmode function allowance of any one order or the eigenmode function component is smaller than the decomposition threshold;
or alternatively, the first and second heat exchangers may be,
and the residual of the eigenmode function up to any one order is a monotonic function or constant.
7. The method of claim 1, wherein the scattered echoes include doppler signals generated based on object motion, micro-doppler signals generated based on human respiration and heartbeat, and ambient clutter signals.
8. The method of claim 7, wherein the scattered echoes are represented as:
Figure QLYQS_2
wherein: s (t) is the scattering echo, < > >
Figure QLYQS_6
Said Doppler signal representing a moving object, < >>
Figure QLYQS_8
For the carrier frequency of the target radar, +.>
Figure QLYQS_3
Radar cross-sectional area of the moving object, < >>
Figure QLYQS_5
Representing the speed of the moving object, +.>
Figure QLYQS_9
Representing a distance of the moving object;
Figure QLYQS_11
Representing said micro Doppler signal,>
Figure QLYQS_1
RCS representing jog moiety,/->
Figure QLYQS_7
Representing the vibration frequency of the chest or heart, +.>
Figure QLYQS_10
Representing the signal of the ambient clutter,
Figure QLYQS_12
representing imaginary units, ++>
Figure QLYQS_4
Indicating the speed of light.
9. A system for remote human breath signal detection comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 8 when the computer program is executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 8.
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