CN110596705B - Human body target identity recognition method and system based on vital sign SAR imaging - Google Patents
Human body target identity recognition method and system based on vital sign SAR imaging Download PDFInfo
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Abstract
The invention discloses a human body target identity recognition method and system based on vital sign SAR imaging, wherein the method comprises the following steps: collecting breathing waveforms of the person to be identified, extracting characteristic parameters of the breathing waveforms, inputting the characteristic parameters into a classifier for training, and obtaining an identity identification model; determining the accurate position of a human body target in a scene by using a vital sign SAR imaging method based on phase demodulation; moving the radar to the right front of the determined position of the human body target to acquire the breathing waveform of the human body target; and inputting the breathing waveform to be identified into the identity identification model, so that the identity of the human body target can be accurately obtained. The system comprises a respiratory waveform extraction module, a feature extraction module, a classification model establishment module, an imaging module, a human body target positioning module and an identity recognition module. The method has high accuracy of identity recognition, can accurately obtain the position and the identity information of the human body target in the scene, and has good robustness and wider applicability.
Description
Technical Field
The invention belongs to the technical field of radars, and particularly relates to a human body target identity recognition method and system based on vital sign SAR imaging.
Background
The mobile radar platform can obtain larger synthetic aperture and better spatial resolution through the azimuth movement of the antenna, and meanwhile, the antenna used by the radar platform is much smaller than that used by a traditional beam scanning radar, so that the deployment and loading are convenient. The development of the frequency modulation continuous wave radar technology is mature day by day, and the modern frequency modulation continuous wave radar has the advantages of light weight, low cost, high resolution and the like, and has great potential in the fields of earth science, security protection, vital sign monitoring and the like.
With the continuous development of radars, the demands for positioning and identifying human targets in scenes are increasing. Because the method has great application prospect in the aspects of home security, user authentication, health monitoring and the like. The existing FMCW radar system is limited by precision, the specific position of a human body target cannot be obtained directly from imaging, and the position of the living body target can be obtained only by measuring micro Doppler information of the target by combining the CW radar system, so that the complexity and the cost of the system are increased by the method; at present, research of determining the identity of a human target according to a breathing mode is not mature, and the problems of low recognition accuracy, insufficient data set and the like mainly exist. Therefore, the method for quickly and accurately acquiring the position of the human body target and further identifying the identity of the human body target has a great application prospect.
Disclosure of Invention
The invention aims to provide a human body target identity recognition method and system based on vital sign SAR imaging.
The technical solution for realizing the purpose of the invention is as follows: a human body target identity recognition method based on vital sign SAR imaging comprises the following steps:
The system for realizing the human body target identity recognition method based on vital sign SAR imaging comprises the following steps:
the respiratory waveform extraction module is used for collecting target respiratory waveforms of all the people to be identified;
the feature extraction module is used for extracting feature parameters of the human body target respiratory waveform;
the classification model building module is used for building an identity recognition model according to the characteristic parameters of the breathing waveform;
the imaging module is used for SAR imaging of the scene;
the human body target positioning module is used for acquiring the position of a human body target in the SAR imaging result;
the identity recognition module is used for inputting the breathing waveform of the human body target acquired in the scene into the identity recognition model to acquire the identity of the human body target.
Compared with the prior art, the invention has the remarkable advantages that: 1) According to the invention, the FMCW mobile platform is used for carrying out vital sign SAR imaging on the scene area, so that the position of the human body target is determined, the position of the human body target is not required to be known in advance, the limitation is overcome, and the application scene is wider; 2) Compared with the traditional contact type monitoring, the device is simple and easy to operate, and can reduce uncomfortable feeling of a human body, so that the breathing of an experimenter is not influenced in the aspect of experiments, measurement errors are reduced, and a plurality of limitations can be overcome, such as incapability of directly contacting a contact piece due to burning of a required contact position or other factors; 3) According to the invention, the proper characteristic parameters are selected to represent different personal identities, a model capable of carrying out human body target identity recognition is trained through a machine learning method, and the final recognition result is good and the accuracy is high; 4) The method is simple and effective, the equipment is simple and easy to realize, the cost is low, the operation is easy, and the performance is reliable.
The invention is further described below with reference to the accompanying drawings.
Drawings
Fig. 1 is a flowchart of a human body target identification method based on vital sign SAR imaging.
Fig. 2 is a schematic diagram of a dynamic segmentation area bit of a respiratory waveform according to an embodiment of the present invention.
Fig. 3 is a schematic representation of respiratory characteristics during pulmonary fullness in an embodiment of the present invention.
Fig. 4 is a schematic diagram of an SVM confusion matrix in an embodiment of the present invention.
Fig. 5 is a schematic diagram of experimental scenario in an embodiment of the present invention.
Fig. 6 is a BP algorithm image in an embodiment of the present invention.
Fig. 7 is a schematic diagram of a phase signal after unwrapping in an embodiment of the present invention.
Fig. 8 is a schematic diagram of unwrapped phase signals after 10-order linear fitting in an embodiment of the present invention.
Fig. 9 is a schematic diagram of an original respiratory signal in an embodiment of the present invention.
Fig. 10 is a schematic diagram of a smoothed respiratory signal according to an embodiment of the invention.
FIG. 11 is a diagram of the final positioning and recognition results according to an embodiment of the present invention.
Detailed Description
Referring to fig. 1, the human body target identity recognition method based on vital sign SAR imaging of the invention comprises the following steps:
Further, step 1, placing the FMCW radar still in front of the person to be identified by L meters, collecting radar echo signals, and preprocessing the echo signals to obtain respiration waveform Re (τ) a ) The method specifically comprises the following steps:
step 1-1, resting an FMCW radar at an L meter position right in front of a person to be identified, and collecting radar echo signals s (t);
step 1-2, preprocessing an echo signal s (t), specifically:
the echo signal s (t) is:
wherein s is T For the transmitted signal of the radar,
for complex conjugation of the received signal of the radar, A 1 For signal s T Amplitude of A 2 For signal->
K represents the frequency modulation slope, λ represents the wavelength, R represents the distance of the target from the radar, t represents the "fast time" of wave propagation, c represents the speed of wave propagation;
fourier transforming the echo signal s (t):
wherein A is 1 A 2 T represents one waveform period for the amplitude of the signal; f represents distance frequency;
step 1-3, constructing a radar echo matrix according to echo signals after Fourier transformation, wherein the method specifically comprises the following steps: constructing M from Fourier transformed echo signals p ×N p The radar echo matrix is used for storing echo signals obtained in a pulse repetition period in each row along the action distance direction of the radar echo matrix, and the columns are azimuth directions; wherein M is p Represents M p Pulse repetition period N p Indicating N in one pulse repetition period p Sampling points N p ；
Step 1-4, extracting echo fragments SH in a range gate at L meters from a radar in a radar echo matrix 1 For the echo segment SH 1 Performing phase unwrapping to obtain the distance R between the target and the radar 1 ：
Where ang is the phase angle after the phase angle alpha is unwound,
wherein the phase angle is
a is echo fragment SH 1 The real part of the complex element in b is the echo segment SH 1 Imaginary parts of complex elements in the (b);
step 1-5, obtaining the respiration waveform Re (τ) of the human body target a ) The method specifically comprises the following steps: distance R of target from radar 1 Slow time τ corresponding to each azimuth sample a Obtaining R 1 (τ a )，R 1 (τ a ) Comprising the facing distance R of the radar to the target 0 And respiration waveform Re (τ) of human body target a ) Thus:
Re(τ a )＝R 1 (τ a )-R 0 。
further, step 2 extracts each of the respiration waveforms Re (τ) obtained in step 1 a ) The characteristic parameters of (a) are as follows:
step 2-1, calculating the respiratory rate Bf, specifically: for respiration waveform Re (τ) a ) Performing FFT (fast Fourier transform) to obtain the respiratory frequency Bf;
step 2-2, calculating an average expiration start time period T ex The method specifically comprises the following steps:
step 2-2-1, acquisition of respiratory waveform Re (τ a ) Is a set of shift peaks of (a):
P ex ＝[p x1 p x2 p x3 p x4 … … p xN ]
wherein p is xi For the peak of the ith respiration waveform, N represents N respiration waveforms;
step 2-2-2, obtaining the expiration start time period T according to the time index corresponding to the displacement peak value exi The method comprises the following steps:
T exi ＝tp x(i+1) -tp x(i)
in tp xi Peak p for the ith respiration waveform xi A corresponding time index;
step 2-2-3, solvingAveraging expiration start time period T ex The method comprises the following steps:
step 2-3, calculating an average respiratory depth, specifically:
step 2-3-1, acquisition of respiratory waveform Re (τ a ) Is the lowest point set of (2):
N in ＝[n x1 n x2 n x3 n x4 ... n xN ]
wherein n is xi N represents N respiration waveforms for the lowest point of the ith respiration waveform;
step 2-3-2, finding the depth of respiration d xi The method comprises the following steps:
d xi ＝p xi -n xi
step 2-3-3, the average respiratory depth is calculated as follows:
step 2-4, calculating the average expiration starting time period T ex Specifically, the standard deviation of (2) is:
step 2-5, calculating an average inspiration starting time period Tin, specifically:
step 2-5-1, obtaining the inspiration starting time period T according to the time index corresponding to the lowest point of the respiration waveform ini The method comprises the following steps:
T ini ＝tn x(i+1) -tn xi
wherein tn is xi Is the lowest point n of the ith respiration waveform xi A corresponding time index;
step 2-5-2, the average inspiration starting time period Tin is calculated as follows:
step 2-6, obtaining the standard deviation of the average inspiration starting time period Tin
The method comprises the following steps:
step 2-7, calculating the average expiratory velocity v exi The method specifically comprises the following steps:
step 2-8, obtaining the average suction velocity v ini The method specifically comprises the following steps:
step 2-9, calculating the average area ratio, specifically:
and 2-9-1, extracting the point with the displacement value of the peak value q% in the respiratory segment as follows:
p 0.qi ＝0.q*[p x1 p x2 p x3 p x4 ... ... p xN ]
extracting the point in the respiratory segment where the displacement value is higher than the lowest point by s%:
n 1.si ＝1.s*[n x1 n x2 n x3 n x4 … n xN ]
step 2-9-2, obtaining the exhalation-inhalation area A-ex according to step 2-9-1 i Is: [ p ] 0.qi n 1.si p 0.qi n 1.si+1 ]The method comprises the steps of carrying out a first treatment on the surface of the Inhalation-exhalation area A-in i Is: [ p ] 0.qi n 1.si+1 p 0.qi+1 n 1.si+1 ]；
Step 2-9-3, calculating the area ratio, and solving the average value r1 as follows:
step 2-10, calculating an average value r2 of Euclidean distance ratios of 1 second before and after each respiration waveform peak value, wherein the average value r2 is as follows:
where da denotes the Euclidean distance from the data point to the vertex after one second of the occurrence of the vertex, dp denotes the Euclidean distance from the data point to the vertex before one second of the occurrence of the vertex.
Exemplary preferred q% = 70% in step 2-9-1; s% = 30%.
Further, step 3 establishes an identification model according to the characteristic parameters of the respiratory waveform extracted in step 2, specifically: respiration waveform Re (τ) a ) As samples, the characteristics of each sample and the corresponding identity label are input into a classifier for training, and an identity recognition model is obtained.
Further, in step 4, the FMCW radar mobile platform acquires echo signals according to a linear track running in the scene, and performs SAR imaging after preprocessing the echo signals to obtain an imaging result I, specifically:
step 4-1, the FMCW radar acquires an original echo signal s' (t) along a linear track, wherein the running direction is a direction, the irradiation direction of the FMCW radar is perpendicular to the running track, and the irradiation direction is a distance direction;
step 4-2, constructing a radar echo matrix after carrying out Fourier transform on an original echo signal s' (t), wherein the process is the same as that of the steps 1-2 and 1-3;
and 4-3, SAR imaging is carried out on the radar echo matrix in the step 4-2 by utilizing a back projection BP algorithm, and an imaging result I is obtained.
Further, stepStep 5 in the imaging result I of step 4, the human body target position L is located x The method specifically comprises the following steps:
step 5-1, in an imaging diagram I, obtaining all points with maximum local energy, namely all targets;
step 5-2, positioning the azimuth position L of each target n The method specifically comprises the following steps: for each target, the row coordinate value and the azimuth resolution rho of the corresponding local energy maximum point are calculated a As the product of the azimuth position L of the target n The method comprises the steps of carrying out a first treatment on the surface of the Where azimuthal resolution ρ a The method comprises the following steps:
wherein L is a Is the true aperture of the antenna;
step 5-3, locating the distance direction position H of each target n I.e. the distance gate H where the target is located n The method specifically comprises the following steps: for each target, the column coordinate value and the distance resolution rho of the corresponding local energy maximum point are calculated r As the product of the distance to the position H of the target n The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the distance resolution ρ r The method comprises the following steps:
wherein c is the speed of light, B p Bandwidth for FMCW radar signals;
step 5-4, extracting the range gate H of the target n And (3) carrying out phase unwrapping on the echo fragment SHn after internal preprocessing to obtain the distance R between the target and the radar:
where ang is the phase angle after the phase angle alpha is unwound,
wherein the phase angle is
a is the real part of the complex element in the echo segment SHn, b is the imaginary part of the complex element in the echo segment SHn;
step 5-5, performing fitting removal processing on the distance R between the target and the radar in the step 5-4 to obtain micro Doppler information of the target, wherein the method specifically comprises the following steps:
step 5-5-1, corresponding the distance R between the target and the radar to the slow time tau of sampling in each azimuth a Obtaining R (τ) a )，R(τ a ) Comprising distance R resulting from radar motion x (τ a ) And micro Doppler information Re' (τ) of the target a ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein:
wherein R is 0 Indicating the distance length tau between the radar and the target when the radar is right in front of the target a Indicating the slow time of azimuthal motion, v indicating the speed of radar motion;
step 5-5-2, all R (τ) corresponding to the target distance R from the radar a ) Obtaining a linear fit curve function fit (τ) using the principle of least squares of deviations a ) And is composed of fit (τ) a ) Instead of R x (τ a )；
Step 5-5-3, according to R (τ a ) And fit (τ) a ) Obtaining micro Doppler information Re' (τ) of target a ) The method comprises the following steps:
Re'(τ a )＝R(τ a )-fit(τ a )
step 5-6, micro Doppler information Re' (τ) on target a ) Performing smoothing treatment, wherein a smoothing formula is as follows:
wherein Res (τ) a ) m The smoothed target micro Doppler waveform is represented by m, which is the micro Doppler information Re' (τ) of the target a ) M-th sampling point of middle azimuth slow time, m E [1, M n ]；M n The number of the sampling points is calculated; re' (τ) a ) m For target micro Doppler information Re' (τ a ) Is the m-th sampling point of (a); re' (τ) a ) m+k For target micro Doppler information Re' (τ a ) M+k sampling points; k is the weighted point number of each sampling point, and the smooth point number can be set by setting the value of K;
step 5-7, assuming that the radar moves to the azimuth position L where the target is n At time t a According to t a Time Res (τ) a ) m Waveform judgment positioning human body target: if at t a Res (τ) near the time a ) m If there is a regular waveform in the waveform, it indicates that the target is a human target and records the azimuth position L corresponding to the target x The method comprises the steps of carrying out a first treatment on the surface of the If at t a Res (τ) near the time a ) m The waveform is not much different from the waveform at other moments, indicating that the object is an inanimate object.
Further, after the human body target position is determined in the step 6, the FMCW radar is moved to the human body target azimuth position L x The radar echo signals are collected, and the echo signals are preprocessed to obtain respiratory waveforms Rex (tau) a ) The method specifically comprises the following steps:
step 6-1, moving the FMCW radar to the azimuth position L of the target x Collecting echo data;
step 6-2, after carrying out Fourier transform on the echo data acquired in the step 6-1, constructing a radar echo matrix, wherein the specific process is the same as that of the step 1-3;
step 6-3, positioning a distance direction position Hx of the human body target, namely a distance door Hx of the human body target, specifically: column coordinate value of maximum energy point in radar echo matrix in step 6-2 and distance resolution ρ r As the distance to the position Hx of the human target;
step 6-4, extracting an echo fragment SHx preprocessed in a range gate Hx where a human body target is located, and performing phase unwrapping on the echo fragment SHx to obtain a distance R between the human body target and a radar x ：
Step 6-5, obtaining the respiration waveform Rex (τ) of the human body target a ) The method specifically comprises the following steps: distance R of target from radar x Slow time τ corresponding to each azimuth sample a Obtaining R x (τ a )，R x (τ a ) Comprising the facing distance R of the radar to the target 0 And respiration waveform Rex (τ) of human body target a ) Then:
Rex(τ a )＝R x (τ a )-R 0 。
the system for realizing the human body target identity recognition method based on vital sign SAR imaging comprises the following steps:
the respiratory waveform extraction module is used for collecting target respiratory waveforms of all the people to be identified;
the feature extraction module is used for extracting feature parameters of the human body target respiratory waveform;
the classification model building module is used for building an identity recognition model according to the characteristic parameters of the breathing waveform;
the imaging module is used for SAR imaging of the scene;
the human body target positioning module is used for acquiring the position of a human body target in the SAR imaging result;
the identity recognition module is used for inputting the breathing waveform of the human body target acquired in the scene into the identity recognition model to acquire the identity of the human body target.
The present invention will be described in further detail with reference to examples.
Examples
Referring to fig. 1, the human body target identification method based on vital sign SAR imaging of the invention comprises the following steps:
1. in the embodiment, a FMCW radar system based on a mobile platform is adopted, the carrier frequency of the system radar is 5.8GHz, the bandwidth of a transmitted signal is 320MHz, the gain of an antenna is 11.3dB, the half-power angle is 46 degrees, and the sampling frequency is 192kHz.
2. The FMCW radar is placed at rest 1 meter in front of the experimenters, 10 experimenters' respiration waveform data are collected, each packet of respiration data is 30 seconds, and 50 packets of data are collected for each experimenter, which is 500 packets of data.
3. The characteristic parameters of the respiratory waveform are extracted, wherein the dynamic segmentation area ratio characteristic of the respiratory waveform is shown in figure 2, and the respiratory characteristic of one second before and after the lung is full is shown in figure 3.
4. And respectively labeling 10 experimenters with labels 1-10, and then sending the extracted characteristic parameters and the corresponding labels into a classifier for training to obtain an identification model, wherein a classification result confusion matrix is shown in figure 4.
5. Setting a scene as a rectangular area with the azimuth length of 3.6m and the distance of 5m, and placing two targets in the scene area, wherein the targets L 1 As reflector, target L 2 For a living body target, the identity of the target is an experimenter with a label of 1, the distance between the target and the scene left Bian Yan is 1.2m and the distance between the target and the edge under the scene are 1m, and the specific experimental scene design is shown in fig. 5.
6. The edge (azimuth direction) of a Lei Dayan 3.6.6 m long scene moves from left to right at the speed v=0.2m/s, echo data are preprocessed, the echo matrix is subjected to SAR imaging by using a BP algorithm, an imaging result I is shown in fig. 6, and two targets L are shown in the figure 1 And L 2 。
7. Firstly, as the distance direction distance of two targets is the same, the distance gate H where any target is located is extracted 1 Is processed to obtain the distance R (tau) a ) As shown in fig. 7; second, in order to eliminate the distance information R generated by the radar motion x (τ a ) Calculate a fitting function fit (τ) of order 10 a ) The results are shown in FIG. 8; finally, R (τ) a ) Subtracting the fitting function fit (τ) a ) Can obtain the purposeTarget micro Doppler information Re' (τ a ) As shown in fig. 9; for Re' (τ) a ) Smoothing at 250 points to obtain smoothed target micro Doppler waveform Res (τ) a ) As shown in fig. 10; as can be seen from FIG. 10, the regular vibration signal is clearly seen from 12 to 27 seconds in the dashed box, the amplitude is 10 times greater than the previous 12 seconds, and the radar is moving to the target L for 12 to 27 seconds 2 Nearby time, i.e. target L 2 Is the human target in fig. 6.
8. Moving FMCW to human target L 2 Is a stationary measurement of the respiration waveform of the subject.
9. Human body target L obtained by measurement 2 Is input into an identity recognition model to obtain a human body target L 2 Is the identity of (a). The final recognition result of this embodiment is shown in fig. 11, and the human body target L 2 Is the experimenter with a label of 1.
The invention collects physiological signals through a radar sensor, extracts characteristic parameters of human target respiration waveforms, trains a body identification model, then positions human targets by utilizing a vital sign SAR imaging method based on phase demodulation, finally extracts the characteristic parameters of the human target respiration waveforms and sends the characteristic parameters into the body identification model to identify the identity of the human targets. The human body target positioning method has the advantages of good human body target positioning effect, high identification accuracy, good robustness and wider applicability.
Claims (6)
1. The human body target identity recognition method based on vital sign SAR imaging is characterized by comprising the following steps of:
step 1, resting an FMCW radar at a position L meters in front of a person to be identified, collecting radar echo signals, and preprocessing the echo signals to obtain a respiration waveform Re (τ) a )；
Step 2, extracting each respiratory waveform Re (τ) a ) Is a characteristic parameter of (a); the method comprises the following steps:
step 2-1, calculating the respiratory rate Bf, specifically: for respiration waveform Re (τ) a ) FFT conversion is carried out to obtain the callThe absorption frequency Bf;
step 2-2, calculating an average expiration start time period T ex The method specifically comprises the following steps:
step 2-2-1, acquisition of respiratory waveform Re (τ a ) Is a set of shift peaks of (a):
P ex ＝[p x1 p x2 p x3 p x4 … … p xN ]
wherein p is xi For the peak of the ith respiration waveform, N represents N respiration waveforms;
step 2-2-2, obtaining the expiration start time period T according to the time index corresponding to the displacement peak value exi The method comprises the following steps:
T exi ＝tp x(i+1) -tp x(i)
in tp xi Peak p for the ith respiration waveform xi A corresponding time index;
step 2-2-3, calculating the average expiration start time period T ex The method comprises the following steps:
step 2-3, calculating an average respiratory depth, specifically:
step 2-3-1, acquisition of respiratory waveform Re (τ a ) Is the lowest point set of (2):
N in ＝[n x1 n x2 n x3 n x4 … n xN ]
wherein n is xi N represents N respiration waveforms for the lowest point of the ith respiration waveform;
step 2-3-2, finding the depth of respiration d xi The method comprises the following steps:
d xi ＝p xi -n xi
step 2-3-3, the average respiratory depth is calculated as follows:
step 2-4, calculating the average expiration starting time period T ex Specifically, the standard deviation of (2) is:
step 2-5, calculating an average inspiration starting time period Tin, specifically:
step 2-5-1, obtaining the inspiration starting time period T according to the time index corresponding to the lowest point of the respiration waveform ini The method comprises the following steps:
T ini ＝tn x(i+1) -tn xi
wherein tn is xi Is the lowest point n of the ith respiration waveform xi A corresponding time index;
step 2-5-2, the average inspiration starting time period Tin is calculated as follows:
step 2-6, obtaining the standard deviation of the average inspiration starting time period Tin
The method comprises the following steps: />
Step 2-7, calculating the average expiratory velocity v exi The method specifically comprises the following steps:
step 2-8, obtaining the average suction velocity v ini The method specifically comprises the following steps:
step 2-9, calculating the average area ratio, specifically:
and 2-9-1, extracting the point with the displacement value of the peak value q% in the respiratory segment as follows:
p 0.qi ＝0.q*[p x1 p x2 p x3 p x4 … … p xN ]
extracting the point in the respiratory segment where the displacement value is higher than the lowest point by s%:
n 1.si ＝1.s*[n x1 n x2 n x3 n x4 … n xN ]
step 2-9-2, obtaining the exhalation-inhalation area A_ex according to step 2-9-1 i Is: [ p ] 0.qi n 1.si p 0.qi n 1.si+1 ]The method comprises the steps of carrying out a first treatment on the surface of the Inhalation-exhalation area A_in i Is: [ p ] 0.qi n 1.si+1 p 0.qi+1 n 1.si+1 ]；
Step 2-9-3, calculating the area ratio, and solving the average value r1 as follows:
step 2-10, calculating an average value r2 of Euclidean distance ratios of 1 second before and after each respiration waveform peak value, wherein the average value r2 is as follows:
where da represents the Euclidean distance from the data point to the vertex after one second of occurrence of the vertex, dp represents the Euclidean distance from the data point to the vertex before one second of occurrence of the vertex;
step 3, building an identity recognition model according to the characteristic parameters of the respiratory waveform extracted in the step 2, wherein the identity recognition model specifically comprises the following steps: respiration waveform Re (τ) a ) As a sample, each ofInputting the characteristics of the sample and the corresponding identity tag thereof into a classifier for training to obtain an identity recognition model;
step 4, randomly distributing the personnel to be identified in a certain scene, acquiring echo signals by the FMCW radar mobile platform in the scene according to linear track operation, preprocessing the echo signals, and then performing SAR imaging to obtain an imaging result I; the method comprises the following steps:
step 4-1, the FMCW radar acquires an original echo signal s' (t) along a linear track, wherein the running direction is a direction, the irradiation direction of the FMCW radar is perpendicular to the running track, and the irradiation direction is a distance direction;
step 4-2, constructing a radar echo matrix after carrying out Fourier transform on an original echo signal s' (t), wherein the process is the same as that of the steps 1-2 and 1-3;
step 4-3, SAR imaging is carried out on the radar echo matrix in the step 4-2 by utilizing a back projection BP algorithm, and an imaging result I is obtained;
step 5, in the imaging result I of step 4, locating the human body target position L x ；
Step 6, after the human body target position is determined, the FMCW radar is moved to the human body target azimuth position L x Collecting radar echo signals, and preprocessing the echo signals to obtain a human target respiratory waveform Rex (tau) a )；
Step 7, the human body target respiration waveform Rex (tau) to be identified in the step 6 a ) And (3) inputting the identification model obtained in the step (3) to identify the identity of the human body target to be identified.
2. The human body target identity recognition method based on vital sign SAR imaging of claim 1, wherein in step 1, the FMCW radar is stationary placed in front of the person to be recognized for L meters, radar echo signals are collected, and the echo signals are preprocessed to obtain respiration waveforms Re (τ) a ) The method specifically comprises the following steps:
step 1-1, resting an FMCW radar at an L meter position right in front of a person to be identified, and collecting radar echo signals s (t);
step 1-2, preprocessing an echo signal s (t), specifically:
the echo signal s (t) is:
wherein s is T For the transmitted signal of the radar,
for complex conjugation of the received signal of the radar, A 1 For signal s T Amplitude of A 2 For signal->
K represents the frequency modulation slope, λ represents the wavelength, R represents the distance of the target from the radar, t represents the "fast time" of wave propagation, c represents the speed of wave propagation;
fourier transforming the echo signal s (t):
wherein A is 1 A 2 T represents one waveform period for the amplitude of the signal; f represents distance frequency;
step 1-3, constructing a radar echo matrix according to echo signals after Fourier transformation, wherein the method specifically comprises the following steps: constructing M from Fourier transformed echo signals p ×N p The radar echo matrix is used for storing echo signals obtained in a pulse repetition period in each row along the action distance direction of the radar echo matrix, and the columns are azimuth directions; wherein M is p Represents M p Pulse repetition period N p Indicating N in one pulse repetition period p Sampling points N p ；
Step 1-4, extracting echo fragments SH in a range gate at L meters from a radar in a radar echo matrix 1 For the echo segment SH 1 Performing phase unwrapping to obtain the target and lightningDistance R to 1 ：
Where ang is the phase angle after the phase angle alpha is unwound,
wherein the phase angle is
a is echo fragment SH 1 The real part of the complex element in b is the echo segment SH 1 Imaginary parts of complex elements in the (b);
step 1-5, obtaining the respiration waveform Re (τ) of the human body target a ) The method specifically comprises the following steps: distance R of target from radar 1 Slow time τ corresponding to each azimuth sample a Obtaining R 1 (τ a )，R 1 (τ a ) Comprising the facing distance R of the radar to the target 0 And respiration waveform Re (τ) of human body target a ) Thus:
Re(τ a )＝R 1 (τ a )-R 0 。
3. the human target identity recognition method based on vital sign SAR imaging according to claim 1, wherein q% = 70% in step 2-9-1; s% = 30%.
4. The human body target identity recognition method based on vital sign SAR imaging according to claim 1, wherein in step 5, in the imaging result I of step 4, the human body target position L is located x The method specifically comprises the following steps:
step 5-1, in an imaging diagram I, obtaining all points with maximum local energy, namely all targets;
step 5-2, locating each targetAzimuth position L of (2) n The method specifically comprises the following steps: for each target, the row coordinate value and the azimuth resolution rho of the corresponding local energy maximum point are calculated a As the product of the azimuth position L of the target n The method comprises the steps of carrying out a first treatment on the surface of the Where azimuthal resolution ρ a The method comprises the following steps:
wherein L is a Is the true aperture of the antenna;
step 5-3, locating the distance direction position H of each target n I.e. the distance gate H where the target is located n The method specifically comprises the following steps: for each target, the column coordinate value and the distance resolution rho of the corresponding local energy maximum point are calculated r As the product of the distance to the position H of the target n The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the distance resolution ρ r The method comprises the following steps:
wherein c is the speed of light, B p Bandwidth for FMCW radar signals;
step 5-4, extracting the range gate H of the target n And (3) carrying out phase unwrapping on the echo fragment SHn after internal preprocessing to obtain the distance R between the target and the radar:
where ang is the phase angle after the phase angle alpha is unwound,
wherein the phase angle is
a is the real part of the complex element in the echo segment SHn, b is the imaginary part of the complex element in the echo segment SHn;
step 5-5, performing fitting removal processing on the distance R between the target and the radar in the step 5-4 to obtain micro Doppler information of the target, wherein the method specifically comprises the following steps:
step 5-5-1, corresponding the distance R between the target and the radar to the slow time tau of sampling in each azimuth a Obtaining R (τ) a )，R(τ a ) Comprising distance R resulting from radar motion x (τ a ) And micro Doppler information Re' (τ) of the target a ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein:
wherein R is 0 Indicating the distance length tau between the radar and the target when the radar is right in front of the target a Indicating the slow time of azimuthal motion, v indicating the speed of radar motion;
step 5-5-2, all R (τ) corresponding to the target distance R from the radar a ) Obtaining a linear fit curve function fit (τ) using the principle of least squares of deviations a ) And is composed of fit (τ) a ) Instead of R x (τ a )；
Step 5-5-3, according to R (τ a ) And fit (τ) a ) Obtaining micro Doppler information Re' (τ) of target a ) The method comprises the following steps:
Re'(τ a )＝R(τ a )-fit(τ a )
step 5-6, micro Doppler information Re' (τ) on target a ) Performing smoothing treatment, wherein a smoothing formula is as follows:
wherein Res (τ) a ) m The smoothed target micro Doppler waveform is represented by m, which is the micro Doppler information Re' (τ) of the target a ) M-th sampling point of middle azimuth slow time, m E [1, M n ]；M n The number of the sampling points is calculated; re' (τ) a ) m For target micro Doppler information Re' (τ a ) Is the m-th sampling point of (a); re' (τ) a ) m+k For target micro Doppler information Re' (τ a ) M+k sampling points; k is the weighted point number of each sampling point, and the smooth point number can be set by setting the value of K;
step 5-7, assuming that the radar moves to the azimuth position L where the target is n At time t a According to t a Time Res (τ) a ) m Waveform judgment positioning human body target: if at t a Res (τ) near the time a ) m If there is a regular waveform in the waveform, it indicates that the target is a human target and records the azimuth position L corresponding to the target x The method comprises the steps of carrying out a first treatment on the surface of the If at t a Res (τ) near the time a ) m The waveform is not much different from the waveform at other moments, indicating that the object is an inanimate object.
5. The human body target identity recognition method based on vital sign SAR imaging of claim 4, wherein after determining the human body target position in step 6, the FMCW radar is moved to the human body target azimuth position L x The radar echo signals are collected, and the echo signals are preprocessed to obtain respiratory waveforms Rex (tau) a ) The method specifically comprises the following steps:
step 6-1, moving the FMCW radar to the azimuth position L of the target x Collecting echo data;
step 6-2, after carrying out Fourier transform on the echo data acquired in the step 6-1, constructing a radar echo matrix, wherein the specific process is the same as that of the step 1-3;
step 6-3, positioning a distance direction position Hx of the human body target, namely a distance door Hx of the human body target, specifically: column coordinate value of maximum energy point in radar echo matrix in step 6-2 and distance resolution ρ r As the distance to the position Hx of the human target;
step 6-4, extracting the distance gate where the human body target is locatedThe pre-processed echo fragment SHx in Hx is subjected to phase unwrapping to obtain the distance R between the human body target and the radar x ：
Step 6-5, obtaining the respiration waveform Rex (τ) of the human body target a ) The method specifically comprises the following steps: distance R of target from radar x Slow time τ corresponding to each azimuth sample a Obtaining R x (τ a )，R x (τ a ) Comprising the facing distance R of the radar to the target 0 And respiration waveform Rex (τ) of human body target a ) Then:
Rex(τ a )＝R x (τ a )-R 0 。
6. a system for implementing the vital sign SAR imaging-based human target identification method of any one of claims 1 to 5, comprising:
the respiratory waveform extraction module is used for collecting target respiratory waveforms of all the people to be identified;
the feature extraction module is used for extracting feature parameters of the human body target respiratory waveform;
the classification model building module is used for building an identity recognition model according to the characteristic parameters of the breathing waveform;
the imaging module is used for SAR imaging of the scene;
the human body target positioning module is used for acquiring the position of a human body target in the SAR imaging result;
the identity recognition module is used for inputting the breathing waveform of the human body target acquired in the scene into the identity recognition model to acquire the identity of the human body target.
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