CN112485792A - Three-dimensional enhanced imaging method for human body target - Google Patents

Three-dimensional enhanced imaging method for human body target Download PDF

Info

Publication number
CN112485792A
CN112485792A CN202011351970.8A CN202011351970A CN112485792A CN 112485792 A CN112485792 A CN 112485792A CN 202011351970 A CN202011351970 A CN 202011351970A CN 112485792 A CN112485792 A CN 112485792A
Authority
CN
China
Prior art keywords
image
pixel
dimensional
amplitude
value
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.)
Granted
Application number
CN202011351970.8A
Other languages
Chinese (zh)
Other versions
CN112485792B (en
Inventor
梁福来
罗丽丽
安强
张杨
吕昊
于霄
王健琪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Air Force Medical University of PLA
Original Assignee
Air Force Medical University of PLA
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Air Force Medical University of PLA filed Critical Air Force Medical University of PLA
Priority to CN202011351970.8A priority Critical patent/CN112485792B/en
Publication of CN112485792A publication Critical patent/CN112485792A/en
Application granted granted Critical
Publication of CN112485792B publication Critical patent/CN112485792B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

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
    • 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
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a three-dimensional enhanced imaging method of a human body target, wherein the amplitude of a micro-motion physiological signal is enhanced by pixel points; then carrying out visual enhancement on the micro-motion physiological signals pixel by pixel; finally, forward enhanced image slices are obtained according to the amplitude enhancement and the visual enhancement of the micro-motion physiological signals, all the forward enhanced image slices are arranged, and a three-dimensional image I is formed3D. The invention adopts a three-dimensional imaging method to distinguish mutually-aliasing body parts in two-dimensional imaging in a three-dimensional space, and better separates clutter from the body parts, so that the aliasing phenomenon in the two-dimensional imaging is obviously improved, the imaging quality is improved, and the technical problem that aliasing parts cannot be distinguished in the two-dimensional imaging in the prior art is solved.

Description

Three-dimensional enhanced imaging method for human body target
Technical Field
The invention belongs to the field of radar monitoring, relates to radar human body three-dimensional imaging, and particularly relates to a three-dimensional enhanced imaging method of a human body target.
Background
For a long time, military targets such as airplanes and tanks are used as observation objects for radars, and living bodies are used as observation objects for biological radars, so that a new chapter of the radars in the application fields of medicine, anti-terrorism, search and rescue and the like is opened. The electromagnetic waves emitted by the biological radar can detect the body surface micromotion caused by vital signs such as respiration and heartbeat of a human target, and detect and identify the vital body target according to the body surface micromotion. The biological radar has the advantages of all-weather, non-contact, positioning and the like, and has irreplaceable advantages in the fields of emergency rescue, anti-terrorism maintenance and the like.
The single-channel biological radar can only solve the problem of detection of a single static human target facing the sight direction of the radar or rough identification of a plurality of moving human targets, and when the targets deviate from the sight direction or a plurality of static human targets exist in a scene, the detection and positioning performance of the single-channel biological radar is reduced. When the Multiple-Input Multiple-Output (MIMO) radar technology is applied to the field of life detection, the problem of detection performance reduction caused by single-channel detection can be solved by using multi-view information of the MIMO radar technology, and the detection performance of the biological radar is led to take a new step.
Two-dimensional imaging projects a target in a real three-dimensional space to a two-dimensional imaging plane area, partial detail characteristics of the target area are lost in the projection process, shadow effect and space blurring occur, for some complex scenes, aliasing of target information is caused, structures of all parts of the target information cannot be distinguished, and a clear image cannot be seen. If the three-dimensional image of the target scene can be acquired, adverse effects caused by the projection process can be eliminated, and certain benefits are brought to accurate positioning of the target and subsequent processing such as target identification and target state analysis.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a three-dimensional enhanced imaging method of a human body target, and solves the technical problem that aliasing parts cannot be distinguished in two-dimensional imaging of the human body target in the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
a method of three-dimensional enhanced imaging of a human target, the method comprising the steps of:
step one, a plurality of transmitting antennas of the MIMO biological radar radiate electromagnetic wave transmitting signals to the space in a time-sharing mode in sequence, the transmitting signals are broadband frequency modulation continuous wave signals, the electromagnetic wave transmitting signals meet the object reflection, and a plurality of receiving antennas receive the reflected electromagnetic wave signals to form a multi-channel radar original echo signal si；
Wherein: i represents the serial number of the equivalent virtual receiving channel;
step two, radar original echo signals s of each channeliRespectively carrying out preprocessing to obtain distance-slow time two-dimensional data matrix
Figure BDA0002801562390000021
Arranging the signals of each channel in sequence to form a distance-slow time-multichannel three-dimensional data matrix
Figure BDA0002801562390000022
Step three, according to the three-dimensional data matrix
Figure BDA0002801562390000023
At a certain depth of field y0And obtaining a human body forward image by adopting a backward projection two-dimensional imaging algorithm to form a slow time sequence I of the MIMO forward imagey0(p,q,l)；
p is the horizontal sampling sequence number of the forward image;
q is a height direction sampling sequence number of the forward image;
y0a certain depth of field;
l is a slow time sampling sequence number;
the method is characterized in that:
fourthly, enhancing the amplitude of the micro-motion physiological signal pixel by pixel;
the process of enhancing the amplitude of the micro-motion physiological signal pixel by pixel comprises the following steps:
step 4.1, applying formula (4-1) to slow time sequence of MIMO forward image
Figure BDA0002801562390000031
Processing to obtain amplitude enhanced image of physiological micromotion signal
Figure BDA0002801562390000032
Figure BDA0002801562390000033
Wherein:
l is the number of slow time samples in the sequence of forward and backward image projection images;
lambda is a relaxation factor for controlling the strengthening degree of the inching signal;
step 4.2, slow time sequence of MIMO forward image
Figure BDA0002801562390000034
Performing MTD processing on the pixel points one by one to obtain the Doppler spectrum of each pixel point
Figure BDA0002801562390000035
In the Doppler spectrum
Figure BDA0002801562390000036
Finding the maximum Doppler amplitude value corresponding to each pixel point
Figure BDA0002801562390000037
Obtaining a maximum Doppler amplitude value image
Figure BDA0002801562390000038
Step 4.3, the enhanced physiological inching signal image obtained in the step 4.1 and the maximum Doppler amplitude value image obtained in the step 4.2 are subjected to mask multiplication
Figure BDA0002801562390000039
Obtaining the pixel value of each pixel point;
fifthly, performing visual enhancement on the micro-motion physiological signals pixel by pixel;
the process of visually enhancing the micro-motion physiological signals pixel by pixel comprises the following steps:
step 5.1, according to the Doppler spectrum of each pixel point obtained in the step 4.2
Figure BDA00028015623900000310
Estimating a sampling numerical value of the velocity value corresponding to the maximum Doppler amplitude value on the horizontal axis by using a formula (5-1);
Figure BDA00028015623900000311
wherein:
k is a horizontal axis sampling variable;
Figure BDA0002801562390000041
an estimated value sampled by a horizontal axis;
obtaining the depth of field y0Velocity estimate corresponding to each pixel point on the image slice
Figure BDA0002801562390000042
Step 5.2, mapping colors according to sampling values of the speed values of each pixel point on a horizontal axis, mapping the speed values of each pixel point into different colors, displaying the colors of the pixel points according to the mapped colors in the subsequent image display step, displaying the similar colors of the pixel points with similar speed values, and segmenting different parts of a human body to obtain a visual enhanced image with better visual effect;
step six, obtaining forward enhancement image slices according to the amplitude enhancement of the physiological micro-motion signals in the step four and the visual enhancement of the physiological micro-motion signals in the step five, and arranging all forward enhancementsImage slicing, forming a three-dimensional image amplitude data matrix A3DAnd a color value matrix C3DFor data matrix A3DMagnitude value screening is performed, then according to C3DDisplaying color to obtain three-dimensional image I3D；
The three-dimensional image I is formed3DComprises the following steps:
step 6.1, obtaining a certain depth of field y according to the step four and the step five0Corresponding forward image slice, y0Taking different values to obtain different forward image slices, uniformly arranging all the forward image slices along the depth of field from small to large to form an amplitude matrix A of the three-dimensional image3DAnd a color value matrix C3D；
Wherein:
A3Dand C3DIs a U, P, Q three-dimensional matrix;
u is the number of the image depth slices;
p is the number of transverse sampling points of the three-dimensional image;
q is the number of sampling points in the height direction of the three-dimensional image;
A3D＝[au,p,q]，au,p,qrepresenting the amplitude values of pixel points at the u-th image depth of field, the p-th transverse sampling and the q-th height direction sampling positions;
C3D＝[cu,p,q]，cu,p,qexpressing the color values of pixel points at the u-th image depth of field, the p-th transverse sampling and the q-th height direction sampling positions;
step 6.2, for A3DSetting amplitude screening according to the pixel values;
for amplitude matrix A3DNormalizing the amplitude value of the pixel point, taking logarithm of the normalized value, comparing the obtained amplitude value with a set threshold value T, and setting the amplitude value of the pixel point to be zero when the amplitude value is smaller than the threshold value T; when the amplitude value is larger than the threshold value T, the amplitude value of the pixel point is kept unchanged, and an uncolored three-dimensional image is obtained; step 6.3, coloring the uncolored three-dimensional image in the step 6.2;
step 6.3, coloring the uncolored three-dimensional image in the step 6.2;
by means of a colour matrix C3DColoring the corresponding pixel points in the uncolored three-dimensional image in the step 6.2 by the colors of the pixel points, and then drawing scattered points corresponding to all the pixel points in the colored three-dimensional image into a three-dimensional coordinate system to obtain a final three-dimensional image.
The invention also has the following technical characteristics:
specifically, in the second step, the pretreatment process includes the following steps:
step 2.1, for broadband frequency modulation continuous wave echo siPerforming deskew processing to obtain original echo signal s of real radari；
Step 2.2, for real radar original echo signal siLow-pass filtering is carried out to obtain a filtered radar echo signal siFor the filtered radar echo signal siPerforming FFT processing to obtain a radar one-dimensional range profile;
step 2.3, radar echo signal s after filteringiCarrying out system correction to obtain a radar echo signal s after the system correctioni；
The system correction is based on system closed loop measurement data, relative time delay difference between channels is obtained through estimation, the difference is recorded, and filtered radar echo signals s are subjected to correctioniEliminating the difference;
step 2.4, carrying out average cancellation MTI processing on the radar echo signals corrected by the system, and eliminating the background;
the method for processing the average cancellation MTI is shown as formula (2-1),
Figure BDA0002801562390000061
wherein:
t is a fast time;
τ is the slow time;
τ0is a slow time start time;
τ1at a slow timeAnd (4) an intermediate end time.
Specifically, in the third step, a slow time sequence of the MIMO image is obtained
Figure BDA0002801562390000062
The method comprises the following specific steps:
step 3.1, for a certain depth of field y0Dividing a forward imaging area into uniform grids along the azimuth direction and the altitude direction, wherein each grid comprises 1 pixel point (x, z);
step 3.2, traversing the pixel points (x, z) on the uniform grid point by point, and obtaining the amplitude value of the pixel points (x, z) by adopting a rear image projection imaging formula (3-1):
Figure BDA0002801562390000063
Figure BDA0002801562390000064
wherein:
m is the serial number of the transmitting antenna;
n is the serial number of the receiving antenna;
m is the number of transmitting antennas;
n is the number of receiving antennas;
t is fast time, and t is 2R/c;
r is a distance;
c is the speed of light;
δ (·) is a dirichlet function;
xTm、yTmand zTmThe azimuth direction, distance direction and height direction coordinates of the mth transmitting antenna are obtained;
xRn、yRnand zRnThe azimuth, distance and altitude coordinates of the nth receiving antenna.
Compared with the prior art, the invention has the beneficial technical effects that:
the invention (I) adopts a three-dimensional imaging method to distinguish mutually aliasing body parts in two-dimensional imaging in a three-dimensional space, and simultaneously better separates clutter from the body parts, so that the aliasing phenomenon in the two-dimensional imaging is obviously improved, the imaging quality is improved, and the technical problem that aliasing parts cannot be distinguished in the two-dimensional imaging in the prior art is solved.
And (II) the invention adds the human body motion micro Doppler characteristic to accumulate and enhance the micro signal energy and improve the signal-to-noise ratio of three-dimensional imaging.
And (III) the invention adopts the estimated speed values of all parts to code the colors, obtains the color enhanced visual image, can strengthen the image display of the human body parts and is beneficial to the understanding and interpretation of the image.
Drawings
Fig. 1 is an information processing flow diagram.
Fig. 2 is a schematic diagram of an antenna array configuration.
Fig. 3 is a simulated human actual measurement experiment scene.
Fig. 4 is a schematic diagram of an echo range profile after pretreatment of a human simulator.
Fig. 5 is a schematic diagram of a result of simulating human three-dimensional imaging.
Fig. 6 is a schematic view of a human body target actual measurement scene.
Fig. 7 is a schematic diagram of a one-dimensional range profile of a human target.
Fig. 8 is a schematic diagram of the result of three-dimensional imaging of a human body.
Fig. 9 shows velocity sample values corresponding to each pixel point of the forward image at an image depth of 4.5 m.
Fig. 10 is a schematic diagram of the result of enhanced visualization of a human target.
The present invention will be explained in further detail with reference to examples.
Detailed Description
The MIMO biological radar of the present invention is collectively referred to as a Multiple-Input Multiple-Output biological radar.
The FMCW signal in the present invention is collectively referred to as a Frequency Modulated Continuous Wave signal, that is, a wideband Frequency Modulated Continuous Wave signal.
It should be noted that, in the present invention, the Back Projection two-dimensional imaging algorithm, that is, the BP two-dimensional imaging algorithm, BP is called Back project. Backprojection two-dimensional imaging algorithms are algorithms known in the art.
It should be noted that the MTD in the present invention is collectively called Moving Target Detection, i.e., Moving Target Detection.
Note that the FFT in the present invention is collectively called Fast Fourier transform and means Fast Fourier transform.
It should be noted that the MTI in the present invention is collectively called Moving Target Indication and refers to Moving Target Indication.
It should be noted that, in the present invention, each pixel point in the three-dimensional matrix corresponds to a scatter point, a3DThe amplitude of the middle pixel controls the size of the scatter, C3DThe coloring of each scatter point is controlled by the color of the middle pixel point.
It should be noted that the process of obtaining a three-dimensional image in the present invention is mainly implemented in MATLAB, and the specific operation is that, in MATLAB, a three-dimensional image is drawn by using a scatter3 function, and a color matrix input C of a scatter3 function is provided3DThe number of the displayed color bars can be set by using the lines and the colormap commands; finally, the corresponding relationship between the speed value and the display color is displayed by the color. Wherein, the amplitude value only displays the pixel points with the amplitude value in the range of-40 dB to 0 dB. Amplitude matrix A of pixel points3DAnd controlling the size of the scattered points, wherein the larger the amplitude value, the larger the volume displayed by the pixel point.
Slow time: the MIMO radar completes data transmission and acquisition once in one period, then completes the next data transmission and acquisition again in one period, and the steps are repeated. In this process, a plurality of repeated periodic sequences is a slow time.
Fast time
Figure BDA0002801562390000091
Where R represents distance, i.e. the distance described in the three-dimensional data matrix of distance-slow time-multipaths.
y0: refers to the depth of the image, referred to in the optical arts as depth of field.
Transverse: refers to the direction perpendicular to the plane formed by the radial direction and the height direction of the radar, and is generally parallel to the extending direction of the aperture of the radar antenna.
Height direction: which refers to a direction perpendicular to the ground.
The target reflection echo received by the radar already contains three-dimensional information of a target scene area, however, when the radar receives insufficient target scene echo information, three-dimensional reconstruction of the target scene is difficult to realize. At least three dimensions are needed to obtain scene information of the target to realize inversion. Therefore, in addition to the time-distance dimension of the transmitted signal, three-dimensional imaging also requires a two-dimensional spatial spread of radar receiving positions to obtain three-dimensional information of the target scene.
The radar system comprises a signal source, a transmitter, a switch array, an antenna array, a receiving channel, a real-time signal acquisition and processing module, a display control module, a battery and the like. The signal source generates FMCW signals, and the FMCW signals are amplified by the power of the transmitter to generate transmission signals and a coupled local oscillator signal; the transmitting signals are transmitted in a time division manner from 8 transmitting antennas by a transmitting switch array, echo signals received by 8 receiving antennas are received in a time division manner by a receiving switch array and are respectively sent to 4 receiving channels, and meanwhile local oscillation signals are also fed to the receiving channels through a power divider; the receiving channel comprises a down-conversion circuit and an intermediate frequency signal conditioning circuit, FMCW modulation waveforms, antenna coupling leakage and part of wall clutter are filtered by using low-pass filtering, and then high-frequency noise is filtered by using a high-pass filter to improve the signal-to-noise ratio; and 4 paths of intermediate frequency signals are fed into the real-time signal acquisition and processing module, and the embedded GPU completes the information processing flow.
The equivalent receiving channels are 64, while the actual receiving channels are 4, during the operation of each transmitting antenna, the receiving switch array is switched 2 times, i.e. 16 times of scanning are completed to complete one frame imaging.
Aiming at the technical problem of aliasing of target information in two-dimensional imaging, the invention adds the micro-Doppler characteristic of human motion to accumulate and enhance the energy of a micro-motion signal, and simultaneously obtains a color enhanced visual image by using a method of coding colors by using the estimated speed values of all parts, thereby enhancing the image display of the human parts, obviously improving the aliasing phenomenon in the two-dimensional imaging, mutually distinguishing the body parts which are aliased in the two-dimensional imaging on a three-dimensional space, better separating clutter from the human parts and improving the imaging quality.
The invention provides a three-dimensional enhanced imaging method of a human body target, which is carried out according to the following steps:
step one, a plurality of transmitting antennas of the MIMO biological radar radiate electromagnetic wave transmitting signals to the space in a time-sharing mode in sequence, the transmitting signals are broadband frequency modulation continuous wave signals, the electromagnetic wave transmitting signals meet the object reflection, and a plurality of receiving antennas receive the reflected electromagnetic wave signals to form a multi-channel radar original echo signal si；
Wherein:
i represents the serial number of the equivalent virtual receiving channel;
step two, radar original echo signals s of each channeliRespectively carrying out preprocessing to obtain distance-slow time two-dimensional data matrix
Figure BDA0002801562390000111
Arranging the signals of each channel in sequence to form a distance-slow time-multichannel three-dimensional data matrix
Figure BDA0002801562390000112
Step three, according to the three-dimensional data matrix
Figure BDA0002801562390000113
At a certain depth of field y0And obtaining a human body forward image by adopting a backward projection two-dimensional imaging algorithm to form a slow time sequence of the MIMO forward image
Figure BDA0002801562390000114
p is the horizontal sampling sequence number of the forward image;
q is a height direction sampling sequence number of the forward image;
y0a certain depth of field;
l is a slow time sampling sequence number;
fourthly, enhancing the amplitude of the micro-motion physiological signal pixel by pixel;
the process of enhancing the amplitude of the micro-motion physiological signal pixel by pixel comprises the following steps:
step 4.1, applying formula (4-1) to slow time sequence of MIMO forward image
Figure BDA0002801562390000115
Processing to obtain amplitude enhanced image of physiological micromotion signal
Figure BDA0002801562390000116
The motion, respiration and heartbeat micromotion of the body surface of a human body are the most remarkable characteristics between a life body and static environment clutter; variance is taken for the jogging signals in the image slow time sequence to accumulate, so that the physiological jogging signals of the human body can be effectively enhanced;
Figure BDA0002801562390000117
wherein:
l is the number of slow time samples in the sequence of forward and backward image projection images;
lambda is a relaxation factor for controlling the strengthening degree of the inching signal;
for each pixel point sequence, the process of solving the variance retains and enhances the changed components, the static components are generally regarded as the mean value, and the average reduction operation can also effectively inhibit the change components;
in this embodiment, the number of slow time samples is the maximum value that can be obtained in an actual situation.
Step 4.2, slow time sequence of MIMO forward image
Figure BDA0002801562390000121
Performing MTD processing on the pixel points one by one to obtain each pixelDoppler spectrum of points
Figure BDA0002801562390000122
In the Doppler spectrum
Figure BDA0002801562390000123
Finding the maximum Doppler amplitude value corresponding to each pixel point
Figure BDA0002801562390000124
Obtaining a maximum Doppler amplitude value image
Figure BDA0002801562390000125
At the depth position of interest, each pixel point corresponds to a slow time sequence; the human body part is presented as approximate cosine signals along with slow time, Fourier transform is carried out on the sequence signals, micro Doppler spectrum signals of which the micro Doppler spectrums of the part contain single frequency are obtained, and the micro Doppler spectrums of the clutter do not have obvious peak values; the component can be enhanced by taking the maximum value of the micro Doppler spectrum corresponding to each pixel point, and clutter is inhibited;
step 4.3, the enhanced physiological inching signal image obtained in the step 4.1 and the maximum Doppler amplitude value image obtained in the step 4.2 are subjected to mask multiplication
Figure BDA0002801562390000126
Obtaining the pixel value of each pixel point;
fifthly, performing visual enhancement on the micro-motion physiological signals pixel by pixel;
the process of visually enhancing the micro-motion physiological signals pixel by pixel comprises the following steps:
step 5.1, according to the Doppler spectrum of each pixel point obtained in the step 4.2
Figure BDA0002801562390000127
Estimating a sampling numerical value of the velocity value corresponding to the maximum Doppler amplitude value on the horizontal axis by using a formula (5-1);
Figure BDA0002801562390000128
wherein:
k is a horizontal axis sampling variable;
Figure BDA0002801562390000129
an estimated value sampled by a horizontal axis;
obtaining the depth of field y0Velocity estimate corresponding to each pixel point on the image slice
Figure BDA0002801562390000131
Step 5.2, mapping colors according to sampling values of the speed values of each pixel point on a horizontal axis, mapping the speed values of each pixel point into different colors, displaying the colors of the pixel points according to the mapped colors in the subsequent image display step, displaying the similar colors of the pixel points with similar speed values, and segmenting different parts of a human body to obtain a visual enhanced image with better visual effect;
step six, obtaining forward enhanced image slices according to the amplitude enhancement of the physiological micro-motion signals in the step four and the visual enhancement of the physiological micro-motion signals in the step five, arranging all the forward enhanced image slices, and forming an amplitude data matrix A of the three-dimensional image3DAnd a color value matrix C3DFor data matrix A3DMagnitude value screening is performed, then according to C3DDisplaying color to obtain three-dimensional image I3D；
The three-dimensional image I is formed3DComprises the following steps:
step 6.1, obtaining a certain depth of field y according to the step four and the step five0Corresponding forward image slice, y0Taking different values to obtain different forward image slices, uniformly arranging all the forward image slices along the depth of field from small to large to form an amplitude matrix A of the three-dimensional image3DAnd a color value matrix C3D；
A3DAnd C3DIs a U, P, Q three-dimensional matrix;
u is the number of the image depth slices;
p is the number of transverse sampling points of the three-dimensional image;
q is the number of sampling points in the height direction of the three-dimensional image;
A3D＝[au,p,q]，au,p,qrepresenting the amplitude values of pixel points at the u-th image depth of field, the p-th transverse sampling and the q-th height direction sampling positions;
C3D＝[cu,p,q]，cu,p,qexpressing the color values of pixel points at the u-th image depth of field, the p-th transverse sampling and the q-th height direction sampling positions;
step 6.2, for A3DSetting amplitude screening according to the pixel values;
for amplitude matrix A3DNormalizing the amplitude value of the pixel point, taking logarithm of the normalized value, comparing the obtained amplitude value with a set threshold value T, and setting the amplitude value of the pixel point to be zero when the amplitude value is smaller than the threshold value T; when the amplitude value is larger than the threshold value T, the amplitude value of the pixel point is kept unchanged, and an uncolored three-dimensional image is obtained;
step 6.3, coloring the uncolored three-dimensional image in the step 6.2;
by means of a colour matrix C3DColoring the corresponding pixel points in the uncolored three-dimensional image in the step 6.2 by the colors of the pixel points, and then drawing scattered points corresponding to all the pixel points in the colored three-dimensional image into a three-dimensional coordinate system to obtain a final three-dimensional image.
In the second step, the pretreatment process comprises the following steps:
step 2.1, for broadband frequency modulation continuous wave echo siPerforming deskew processing to obtain real echo signals;
step 2.2, radar original echo signal siPerforming low-pass filtering to obtain a filtered radar echo signal, and performing FFT processing on the filtered radar echo signal to obtain a radar one-dimensional range profile;
step 2.3, performing system correction on the filtered radar echo signals to obtain radar echo signals after system correction;
the system correction is based on system closed loop measurement data, relative time delay difference between channels is obtained through estimation, the difference is recorded, and the difference is eliminated for the filtered radar echo signals;
step 2.4, carrying out average cancellation MTI processing on the radar echo signals corrected by the system, and eliminating the background;
the method for processing the average cancellation MTI is shown as formula (2-1),
Figure BDA0002801562390000141
wherein:
t is a fast time;
τ is the slow time;
τ0is a slow time start time;
τ1is the slow time end time.
In step three, a slow time sequence of MIMO forward images is obtained
Figure BDA0002801562390000151
The method comprises the following specific steps:
step 3.1, for a certain depth of field y0Dividing a forward imaging area into uniform grids along the azimuth direction and the altitude direction, wherein each grid comprises 1 pixel point (x, z);
step 3.2, traversing the pixel points (x, z) on the uniform grid point by point, and obtaining the amplitude value of the pixel points (x, z) by adopting a rear image projection imaging formula (3-1):
Figure BDA0002801562390000152
Figure BDA0002801562390000153
wherein:
m is the serial number of the transmitting antenna;
n is the serial number of the receiving antenna;
m is the number of transmitting antennas;
n is the number of receiving antennas;
t is fast time, and t is 2R/c;
r is a distance;
c is the speed of light;
δ (·) is a dirichlet function;
xTm、yTmand zTmThe azimuth direction, distance direction and height direction coordinates of the mth transmitting antenna are obtained;
xRn、yRnand zRnThe azimuth, distance and altitude coordinates of the nth receiving antenna.
In this embodiment, the number of the transmitting antenna and the number of the receiving antenna are generally 8, and the number of the transmitting antenna and the number of the receiving antenna can be specifically adjusted according to actual conditions.
The present invention is not limited to the following specific examples, and all equivalent changes based on the technical solutions of the present invention fall within the scope of the present invention.
Actual measurement example 1:
according to the technical scheme, the actual measurement example provides a three-dimensional enhanced imaging method for the human body target, the method is carried out by adopting the processes from the first step to the sixth step, and the method adopts a simulator to simulate the human body target in a normal breathing state. As shown in fig. 2, the MIMO radar array is composed of 8 transmitting units and 8 receiving units arranged in a square shape. The transmission signal is a pulse waveform of FMCW having a center frequency of 3 GHz. The center of the antenna array is set as the origin of the coordinate system. The dummy is located at a distance of 5m from the longitudinal direction, 0m from the lateral direction and the height direction, and the breathing frequency of the dummy is set to 1 Hz. As shown in fig. 3, a solid wall with a thickness of 0.3m is arranged between the antenna array and the detection target, and the antenna array is tightly attached to the wall.
The preprocessed echo range image is shown in fig. 4. In the image, a point-like reflected echo simulating the uniform change of the intensity of a human can be seen at a distance of 5m, and the reflected echo is a simulated uniform respiration signal. Therefore, after the preprocessing, the simulated micro-motion vital sign signals are enhanced to a certain degree.
The three-dimensional imaging result is shown in fig. 5, the position of the imaging result is consistent with the measured value of the placement position of the human simulator, the error is about 0.25 m, and the resolution in three directions is about 0.3 m.
Actual measurement example 2:
according to the technical scheme, the actual measurement example provides a three-dimensional enhanced imaging method for the human body target, the method is carried out by adopting the processes from the first step to the sixth step, the scene is shown as figure 6, in the free space, the human body target directly faces the radar antenna array, stands at a position 4 meters away from the radar in the direction of the radar sight line, and the human body slightly shakes front and back.
The echo range image after preprocessing is shown in fig. 7, and the echo of the human body target has a certain extension in the range direction. The result of monochromatic imaging of a human subject is shown in fig. 8, which is a display result drawn based on only the amplitude matrix, in which the outline of the human subject can be roughly seen.
Estimating a sampling value of the velocity value corresponding to the maximum doppler amplitude value on the horizontal axis according to the doppler spectrum, to obtain a velocity sampling value corresponding to each pixel point of the forward image where the image depth (i.e., depth of field) is 4.5m, as shown in fig. 9; color matrix input C of scatter3 function in MATLAB3DThe number of the displayed color bars can be set by using the lines and the colormap commands; finally, the correspondence between the speed value and the display color is shown by color, and the result is shown in fig. 10.
In the imaging result, the human body imaging result is divided into a plurality of color areas, so that the visual effect of the human body target is better, and the subsequent further applications such as target identification, action classification and the like are facilitated.

Claims (3)

1. A method of three-dimensional enhanced imaging of a human target, the method comprising the steps of:
step one, a plurality of transmitting antennas of the MIMO biological radar are sequentially time-sharing spaceRadiating an electromagnetic wave transmitting signal, wherein the transmitting signal is a broadband frequency modulation continuous wave signal, the electromagnetic wave transmitting signal is reflected when encountering an object, and a plurality of receiving antennas receive the reflected electromagnetic wave signal to form a multi-channel radar original echo signal si；
Wherein:
i represents the serial number of the equivalent virtual receiving channel;
step two, radar original echo signals s of each channeliRespectively carrying out preprocessing to obtain distance-slow time two-dimensional data matrix
Figure FDA0002801562380000011
Arranging the signals of each channel in sequence to form a distance-slow time-multichannel three-dimensional data matrix
Figure FDA0002801562380000012
Step three, according to the three-dimensional data matrix
Figure FDA0002801562380000013
At a certain depth of field y0And obtaining a human body forward image by adopting a backward projection two-dimensional imaging algorithm to form a slow time sequence of the MIMO forward image
Figure FDA0002801562380000014
p is the horizontal sampling sequence number of the forward image;
q is a height direction sampling sequence number of the forward image;
y0a certain depth of field;
l is a slow time sampling sequence number;
the method is characterized in that:
fourthly, enhancing the amplitude of the micro-motion physiological signal pixel by pixel;
the process of enhancing the amplitude of the micro-motion physiological signal pixel by pixel comprises the following steps:
step 4.1, applying formula (4-1) to slow time sequence of MIMO forward image
Figure FDA0002801562380000015
Processing to obtain amplitude enhanced image of physiological micromotion signal
Figure FDA0002801562380000016
Figure FDA0002801562380000017
Wherein:
l is the number of slow time samples in the sequence of forward and backward image projection images;
lambda is a relaxation factor for controlling the strengthening degree of the inching signal;
step 4.2, slow time sequence of MIMO forward image
Figure FDA0002801562380000021
Performing MTD processing on the pixel points one by one to obtain the Doppler spectrum of each pixel point
Figure FDA0002801562380000022
In the Doppler spectrum
Figure FDA0002801562380000023
Finding the maximum Doppler amplitude value corresponding to each pixel point
Figure FDA0002801562380000024
Obtaining a maximum Doppler amplitude value image
Figure FDA0002801562380000025
Step 4.3, the enhanced physiological inching signal image obtained in the step 4.1 and the maximum Doppler amplitude value image obtained in the step 4.2 are subjected to mask multiplication
Figure FDA0002801562380000026
Obtaining the pixel value of each pixel point;
fifthly, performing visual enhancement on the micro-motion physiological signals pixel by pixel;
the process of visually enhancing the micro-motion physiological signals pixel by pixel comprises the following steps:
step 5.1, according to the Doppler spectrum of each pixel point obtained in the step 4.2
Figure FDA0002801562380000027
Estimating a sampling numerical value of the velocity value corresponding to the maximum Doppler amplitude value on the horizontal axis by using a formula (5-1);
Figure FDA0002801562380000028
wherein:
k is a horizontal axis sampling variable;
Figure FDA0002801562380000029
an estimated value sampled by a horizontal axis;
obtaining the depth of field y0Velocity estimate corresponding to each pixel point on the image slice
Figure FDA00028015623800000210
Step 5.2, mapping colors according to sampling values of the speed values of each pixel point on a horizontal axis, mapping the speed values of each pixel point into different colors, displaying the colors of the pixel points according to the mapped colors in the subsequent image display step, displaying the similar colors of the pixel points with similar speed values, and segmenting different parts of a human body to obtain a visual enhanced image with better visual effect;
step six, obtaining forward enhanced image slices according to the amplitude enhancement of the physiological micro-motion signals in the step four and the visual enhancement of the physiological micro-motion signals in the step five, and arranging all the forward enhanced image slices to formAmplitude data matrix A of three-dimensional image3DAnd a color value matrix C3DFor data matrix A3DMagnitude value screening is performed, then according to C3DDisplaying color to obtain three-dimensional image I3D；
The three-dimensional image I is formed3DComprises the following steps:
step 6.1, obtaining a certain depth of field y according to the step four and the step five0Corresponding forward image slice, y0Taking different values to obtain different forward image slices, uniformly arranging all the forward image slices along the depth of field from small to large to form an amplitude matrix A of the three-dimensional image3DAnd a color value matrix C3D；
Wherein:
A3Dand C3DIs a U, P, Q three-dimensional matrix;
u is the number of the image depth slices;
p is the number of transverse sampling points of the three-dimensional image;
q is the number of sampling points in the height direction of the three-dimensional image;
A3D＝[au,p,q]，au,p,qrepresenting the amplitude values of pixel points at the u-th image depth of field, the p-th transverse sampling and the q-th height direction sampling positions;
C3D＝[cu,p,q]，cu,p,qexpressing the color values of pixel points at the u-th image depth of field, the p-th transverse sampling and the q-th height direction sampling positions;
step 6.2, for A3DSetting amplitude screening according to the pixel values;
for amplitude matrix A3DNormalizing the amplitude value of the pixel point, taking logarithm of the normalized value, comparing the obtained amplitude value with a set threshold value T, and setting the amplitude value of the pixel point to be zero when the amplitude value is smaller than the threshold value T; when the amplitude value is larger than the threshold value T, the amplitude value of the pixel point is kept unchanged, and an uncolored three-dimensional image is obtained;
step 6.3, coloring the uncolored three-dimensional image in the step 6.2;
by means of a colour matrix C3DColoring the corresponding pixel points in the uncolored three-dimensional image in the step 6.2 by the colors of the pixel points, and then drawing scattered points corresponding to all the pixel points in the colored three-dimensional image into a three-dimensional coordinate system to obtain a final three-dimensional image.
2. The method for three-dimensionally enhancing imaging of a human target as defined in claim 1, wherein in step two, said preprocessing procedure comprises the steps of:
step 2.1, for broadband frequency modulation continuous wave echo siPerforming deskew processing to obtain original echo signal s of real radari；
Step 2.2, for real radar original echo signal siLow-pass filtering is carried out to obtain a filtered radar echo signal siFor the filtered radar echo signal siPerforming FFT processing to obtain a radar one-dimensional range profile;
step 2.3, radar echo signal s after filteringiCarrying out system correction to obtain a radar echo signal s after the system correctioni；
The system correction is based on system closed loop measurement data, relative time delay difference between channels is obtained through estimation, the difference is recorded, and filtered radar echo signals s are subjected to correctioniEliminating the difference;
step 2.4, carrying out average cancellation MTI processing on the radar echo signals corrected by the system, and eliminating the background;
the method for processing the average cancellation MTI is shown as formula (2-1),
Figure FDA0002801562380000041
wherein:
t is a fast time;
τ is the slow time;
τ0is a slow time start time;
τ1is the slow time end time.
3. The method of claim 1, wherein in step three, a slow time series of MIMO images is obtained
Figure FDA0002801562380000053
The method comprises the following specific steps:
step 3.1, for a certain depth of field y0Dividing a forward imaging area into uniform grids along the azimuth direction and the altitude direction, wherein each grid comprises 1 pixel point (x, z);
step 3.2, traversing the pixel points (x, z) on the uniform grid point by point, and obtaining the amplitude value of the pixel points (x, z) by adopting a rear image projection imaging formula (3-1):
Figure FDA0002801562380000051
Figure FDA0002801562380000052
wherein:
m is the serial number of the transmitting antenna;
n is the serial number of the receiving antenna;
m is the number of transmitting antennas;
n is the number of receiving antennas;
t is fast time, and t is 2R/c;
r is a distance;
c is the speed of light;
δ (·) is a dirichlet function;
xTm、yTmand zTmThe azimuth direction, distance direction and height direction coordinates of the mth transmitting antenna are obtained;
xRn、yRnand zRnThe azimuth, distance and altitude coordinates of the nth receiving antenna.
CN202011351970.8A 2020-11-26 2020-11-26 Three-dimensional enhanced imaging method for human body target Active CN112485792B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011351970.8A CN112485792B (en) 2020-11-26 2020-11-26 Three-dimensional enhanced imaging method for human body target

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011351970.8A CN112485792B (en) 2020-11-26 2020-11-26 Three-dimensional enhanced imaging method for human body target

Publications (2)

Publication Number Publication Date
CN112485792A true CN112485792A (en) 2021-03-12
CN112485792B CN112485792B (en) 2023-08-08

Family

ID=74935356

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011351970.8A Active CN112485792B (en) 2020-11-26 2020-11-26 Three-dimensional enhanced imaging method for human body target

Country Status (1)

Country Link
CN (1) CN112485792B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114680862A (en) * 2022-06-01 2022-07-01 中国科学技术大学 Biological surface micro-motion imaging method and device and biological signal detection device

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012055148A1 (en) * 2010-10-27 2012-05-03 中国人民解放军第四军医大学 Multichannel ultra wide band based (uwb-based) radar life detector and positioning method thereof
CN106054156A (en) * 2016-06-22 2016-10-26 中国人民解放军第四军医大学 Static human target recognizing and positioning method based on UWB (Ultra Wideband) MIMO (Multiple-Input Multiple-Output) bio-radar
CN106291494A (en) * 2016-07-21 2017-01-04 深圳大学 The SAR cheating interference target identification method and system strengthened based on differential characteristics
CN109541585A (en) * 2018-10-09 2019-03-29 中国人民解放军第四军医大学 A kind of human body detection imaging method through walls based on kurtosis assessment

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012055148A1 (en) * 2010-10-27 2012-05-03 中国人民解放军第四军医大学 Multichannel ultra wide band based (uwb-based) radar life detector and positioning method thereof
CN106054156A (en) * 2016-06-22 2016-10-26 中国人民解放军第四军医大学 Static human target recognizing and positioning method based on UWB (Ultra Wideband) MIMO (Multiple-Input Multiple-Output) bio-radar
CN106291494A (en) * 2016-07-21 2017-01-04 深圳大学 The SAR cheating interference target identification method and system strengthened based on differential characteristics
CN109541585A (en) * 2018-10-09 2019-03-29 中国人民解放军第四军医大学 A kind of human body detection imaging method through walls based on kurtosis assessment

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
梁福来;李浩楠;祁富贵;安强;王健琪;: "UWB MIMO生物雷达多静止人体目标成像方法研究", 雷达学报, vol. 5, no. 5 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114680862A (en) * 2022-06-01 2022-07-01 中国科学技术大学 Biological surface micro-motion imaging method and device and biological signal detection device
CN114680862B (en) * 2022-06-01 2022-09-30 中国科学技术大学 Biological surface micro-motion imaging method and device and biological signal detection device

Also Published As

Publication number Publication date
CN112485792B (en) 2023-08-08

Similar Documents

Publication Publication Date Title
CN105974405B (en) Ground Penetrating Radar rear orientation projection imaging method based on amplitude weighting
CN112444811B (en) Target detection and imaging method for comprehensive MIMO radar and ISAR
CN102058411B (en) UVB based multi-channel radar life detection instrument
CN106054156A (en) Static human target recognizing and positioning method based on UWB (Ultra Wideband) MIMO (Multiple-Input Multiple-Output) bio-radar
CN106908771B (en) Pulse radar target acquisition analogy method in microwave dark room
CN109633598B (en) Array radar target detection method based on characteristic analysis
CN109541585B (en) Human body through-wall detection imaging method based on kurtosis evaluation
KR20100032816A (en) Range and azimuth resolution enhancement for real-beam radar
CN111220955A (en) Airborne weather radar ground clutter suppression method based on vertical array covariance matrix characteristic decomposition
CN109782279A (en) A kind of compressed sensing based bistatic ISAR imaging method
CN109270529A (en) Forward sight array SAR high-resolution imaging method and system based on virtual-antenna
CN110596706B (en) Radar scattering sectional area extrapolation method based on three-dimensional image domain projection transformation
CN112485792B (en) Three-dimensional enhanced imaging method for human body target
RU2416105C1 (en) Method of determining motion parametres of aerial objects in surveillance radar by using coherent properties of reflected signals
CN105738887A (en) Airborne radar clutter power spectrum optimization method based on Doppler channel division
EP2817655B1 (en) Systems and methods for image sharpening
CN107589421B (en) Array foresight SAR imaging method
US5305753A (en) Method and apparatus for determining the velocity of a flowing liquid
CN103954962B (en) A kind of ISAR imaging pulse algorithm for estimating based on compressed sensing
CN110456362A (en) A kind of target acoustic imaging and speed-measuring method and system based on pulse pair transmitting
CN110441398A (en) A kind of synthetic aperture ultrasonic imaging method based on multilayer dielectricity velocity of sound model
CN109061626A (en) A kind of method that Step Frequency coherent processing detects low signal to noise ratio moving-target
CN112781530A (en) RCS detection system based on binocular vision and detection method thereof
CN112230221A (en) RCS (Radar Cross section) measurement method based on three-dimensional sparse imaging
CN113359196B (en) Multi-target vital sign detection method based on subspace method and DBF

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