CN116881691A - Biological detection method for living body in security inspection scene - Google Patents
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- 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
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Abstract
The invention discloses a biological detection method for a security inspection scene living body, which comprises the following steps: the millimeter wave radar transmitting antenna generates millimeter wave signals and transmits the millimeter wave signals to the detected target object, and receives the millimeter wave signals reflected by the detected target object; mixing the reflected millimeter wave signals with the transmitting signals to generate radar target echoes; separating out amplitude phase signals, obtaining continuous frequency characteristic signals through Fourier transformation, and performing discretization treatment; converting the discretized frequency signal into a distance variable to obtain a distance parameter between the millimeter wave radar and the detected target object; extracting phase parameters according to the distance parameters; performing phase unwrapping treatment on the phase parameters to obtain living organism breathing micro-motion signals; according to the micro-motion signal caused by living respiration, the judgment of whether living organisms exist in the detected target object is realized. The detection method can accurately detect whether living things exist in the luggage case and the express piece in the security inspection scene, and improves security inspection efficiency of goods passing through airports and customs.
Description
Technical Field
The invention relates to the technical field of living body detection equipment, in particular to a living body biological detection method for a security check scene.
Background
Currently, as the demand for species safety becomes more and more common in recent years, the problem of living body detection is solved by technological means in the security inspection field.
Problems in the prior art:
1. at present, living body detection equipment of a trunk and an express piece in a security inspection scene mainly comprises an X-ray machine, is finished by manual identification after imaging treatment, and has low detection efficiency.
2. The millimeter wave radar detection technology gradually becomes a security inspection mode which is widely popularized in important places such as airports, customs and the like due to the characteristics of high biological safety, high precision, non-contact, high security inspection efficiency and the like.
3. The millimeter wave radar has high detection precision, but has certain limitation in the detection target classification processing.
4. The fast Fourier transform algorithm used for the traditional radar signal processing has high operation complexity, the problem of dynamic noise cannot be effectively processed, and the technical difficulty is that false alarm targets are removed to effectively detect moving targets when the millimeter wave radar performs real-time signal processing in a complex environment.
Disclosure of Invention
In view of the above, the present invention provides a method for biological detection of living organisms in security inspection scenes, which at least partially solves the above technical problems.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the invention provides a biological detection method for a security inspection scene living body, which comprises the following steps:
s1, a millimeter wave radar transmitting antenna generates millimeter wave signals and sends the millimeter wave signals to a detected target object, and a millimeter wave radar receiving antenna receives the millimeter wave signals reflected by the detected target object;
s2, mixing the reflected millimeter wave signals with the transmitting signals to generate radar target echoes;
s3, separating an amplitude phase signal from the radar target echo, obtaining a continuous frequency characteristic signal through Fourier transformation, and performing discretization;
s4, converting the discretized frequency signal into a distance variable to obtain a distance parameter between the millimeter wave radar and the detected target object;
s5, extracting phase parameters according to the distance parameters;
s6, performing phase unwrapping treatment on the phase parameter to obtain a living body breathing micro-motion signal;
and S7, judging whether the detected target object has a living body or not according to the living body breathing micro-motion signal.
In one embodiment, the radar target echo storage mode in the step S2 is a data matrix, including a fast time dimension M and a slow time dimension N.
In one embodiment, the step S3 includes:
s301, taking the fast time dimension M as an amplitude phase signal, and carrying out Fourier transformation to obtain a continuous frequency characteristic signal through the following formula (1);
(1) Wherein S is IF Is a signal function, f is a frequency, c is a speed of light, T c In order for the duration of the pulse to be of a duration,for phase change, f b Is a frequency change; j is an imaginary unit, exp represents an exponential function based on a natural constant e, and t is time;
s302, setting a sampling interval as deltat, setting the number of time domain sampling points in a pulse as a fast time dimension M in a radar target echo storage matrix, and satisfying the relation: (M-1) Δt=t c The discrete fourier transform of the radar target echo is then:
(3) Wherein S is IF As a signal function, f is frequency, T c In order for the duration of the pulse to be of a duration,for phase change, f b Is a frequency change; m is the dimension of the fast time, and the value of M is more than or equal to 0 and less than or equal to M.
In one embodiment, in the step S4, a distance parameter between the millimeter wave radar and the detected target object is obtained, and the following formula is adopted:
(3) Wherein γ=b/T c B is the signal bandwidth, T c For pulse duration, r c Is distance information
The information, m, is the dimension of the fast time,for phase change, Δt is between samplesA partition; j is an imaginary unit and e is a natural constant.
In one embodiment, the step S5 includes:
s501, processing the slow time dimension N, determining a distance unit where a target is located, and solving the phase of the distance unit where the target is located from 1 to N along the slow time dimension N;
s502, extracting the phase of a distance unit where the target is located, and obtaining the phase after arctangent operationThe value range is [ -pi, pi]。
In one embodiment, the step S6 includes:
the phase value is within [ -pi, pi]Recovering the phase between the two phases, and starting from the second phase, and differencing the latter phase with the former phase
If the difference is greater than pi, thenSubtracting 2π; if the difference is smaller than-pi, then +.>Plus 2 pi; if it is between [ -pi, pi]The middle part is kept->The value is unchanged; at this time, the mth cycle is ended, and the (m+1) th cycle is entered;
until the cycle is finished, the phase value obtained each time forms a living body breathing micro-motion signal.
In one embodiment, the method further comprises:
and (3) performing static interference removal and dynamic interference removal on the living body breathing micro-motion signals in the step S6.
In one embodiment, the static interference removal includes:
and eliminating redundant displacement caused by interference signals on a complex frequency domain of the sampling points, and removing static interference in the living body respiratory micro-motion signals.
In one embodiment, the dynamic interference removal includes:
the dynamic interference is expressed as irregular or regular movement of the whole or part of living body, the dynamic interference in the living body detection scene is the regular movement of the security detection driving belt, and the phenomenon of waveform distortion of the movement signal caused by the too high speed of the dynamic interference is solved by adopting a method for shortening the slow time sampling interval delta t.
Compared with the prior art, the invention discloses a living organism detection method for a security inspection scene, which solves the technical problems faced by rapid and accurate detection and identification of living organism targets in the security inspection scene by adopting millimeter wave radar detection; the method can accurately detect whether living things exist in the luggage case and the express delivery piece, and improves the security inspection efficiency of goods passing through airports and customs.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a millimeter wave radar-based detection device according to the present invention;
fig. 2 is a schematic perspective view of a shielding box provided by the invention;
fig. 3 is a schematic diagram of a security inspection scene living body detection principle based on millimeter wave radar provided by the invention;
FIG. 4 is a flowchart of a method for detecting living organisms in a security inspection scene provided by the invention;
FIG. 5 is a diagram showing the storage form of the radar target echo in the upper computer;
FIG. 6a is a range profile of a detected object according to the present invention;
FIG. 6b is a schematic diagram of a distance unit for determining a target according to the present invention;
FIG. 7 is a schematic diagram of a live respiratory micro-motion signal with clutter provided by the present invention;
FIG. 8 is a specific flow chart of phase unwrapping provided by the present invention;
FIG. 9 is a schematic diagram of signals before and after phase unwrapping according to the present invention;
in the accompanying drawings: 1-a detection device body; 2-an upper computer; 11-shielding box; 12-a security inspection conveyor belt; 13-a camera device; 14-electromagnetic shielding curtain; 15-an actuation button; 16-an upper computer data interface; 17-an alarm device; 18-wave absorbing wedges; 19-millimeter wave radar apparatus.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a living organism detection method of a security inspection scene, which aims at detecting living organisms in the security inspection scene, and equipment used by the method is shown by referring to FIG. 1, and comprises the following steps: a detection device body 1 and an upper computer 2;
wherein, detection device body 1 includes: a shielding box 11 and a security inspection conveyor belt 12; the security inspection conveyor belt 12 is used for conveying the detected target object to pass through the shielding box 11; the shielding box 11 is U-shaped, reversely buckled on the security inspection conveyor belt 12 and connected to frames on two sides of the security inspection conveyor belt 12 through bolts.
As shown in fig. 2, a millimeter wave radar device 19 is provided in the shield case 11; the millimeter wave radar device 19 is fixed above or on the side wall of the U-shaped shielding case 11 by plastic members or metal screws. The millimeter wave radar device 19 transmits a millimeter wave radar signal to the detected target object, receives the reflected millimeter wave radar signal, mixes the reflected millimeter wave radar signal with the transmitted millimeter wave radar signal to be processed into a radar target echo, and transmits the radar target echo to the upper computer 2 in a wired or wireless mode, and the upper computer 2 processes the radar target echo according to the received radar target echo to realize whether the detected target object has a living body or not.
For further electromagnetic interference signal shielding, as shown in fig. 2, a pyramid-shaped or long-strip-shaped wave-absorbing wedge 18 may be provided on the inner wall of the shielding case, and mainly composed of polyurethane foam, non-woven fabric flame retardant, silicate board metal film assembly, and the like. In addition, on the inner top wall of the shielding case 11, there is also provided a camera device 13 usable for photographing a detection target as a tag.
As shown in fig. 3, the millimeter wave radar device 19 described above includes: the antenna comprises a transmitting antenna, a receiving antenna, a sawtooth wave generator and an analog-to-digital converter;
the sawtooth wave signal generated by the sawtooth wave generator is subjected to frequency multiplication treatment, and a millimeter wave radar signal is transmitted to a detected target object by a transmitting antenna; the receiving antenna receives the millimeter wave signal reflected by the detected target object, and the millimeter wave signal and the emitted millimeter wave radar signal are mixed to form a radar target echo, and the radar target echo is stored in the ADC buffer through analog-to-digital conversion.
Wherein, the host computer includes: the system comprises a sampling module, a digital signal processing module and a display terminal;
the sampling module is used for collecting digital signals in the ADC buffer;
the digital signal processing module is used for processing the acquired digital signals in real time so as to extract breathing micro-motion signals of the living body and realize detection of whether the detected target object exists or not;
and the display terminal is used for displaying the waveform diagram of the living body breathing micro-motion signal. The host computer still includes: as shown in fig. 1, an alarm device 17 is installed at the top of the shielding case for performing an audible and visual alarm when it is judged that a living body exists based on the living body respiratory micro-motion signal.
The detection method, referring to fig. 4, comprises the following steps:
s1, a millimeter wave radar transmitting antenna generates millimeter wave signals and sends the millimeter wave signals to a detected target object, and a millimeter wave radar receiving antenna receives the millimeter wave signals reflected by the detected target object;
s2, mixing the reflected millimeter wave signals with the transmitting signals to generate radar target echoes;
s3, separating an amplitude phase signal from the radar target echo, obtaining a continuous frequency characteristic signal through Fourier transformation, and performing discretization;
s4, converting the discretized frequency signal into a distance variable to obtain a distance parameter between the millimeter wave radar and the detected target object;
s5, extracting phase parameters according to the distance parameters;
s6, performing phase unwrapping treatment on the phase parameter to obtain a living body breathing micro-motion signal;
and S7, judging whether the detected target object has a living body or not according to the living body breathing micro-motion signal.
The following describes the above steps:
in step S2, a data matrix of the radar wave signal is sampled, including a fast time dimension M and a slow time dimension N. The storage form of the radar target echo in the upper computer is shown in fig. 5 and is called a data matrix. N represents the slow time dimension sequence number, M represents the fast time dimension sequence number, N represents the total number of pulse signals transmitted by the radar, and M is the sampling point number of each radar target echo. The product of N and pulse repetition time is the total monitoring time length, which is called slow time dimension; m points are sampled during the duration of each pulse, which is referred to as the fast time dimension because of the extremely short pulse duration.
In step S3, in order to determine the target position, fourier transform is performed on the fast time dimension (i.e. each row) of the data matrix, and only discretized data is processed in the computer, so that continuous signals need to be discretized.
The step S3 specifically comprises the following steps:
s301, taking the fast time dimension M as an amplitude phase signal, and carrying out Fourier transformation to obtain a continuous frequency characteristic signal through the following formula (1);
(1) Wherein S is IF Is a signal function, f is a frequency, c is a speed of light, T c In order for the duration of the pulse to be of a duration,for phase change, f b Is a frequency change; j is an imaginary unit, exp represents an exponential function based on a natural constant e, and t is time;
s302, setting a sampling interval as deltat, setting the number of time domain sampling points in a pulse as a fast time dimension M in a radar target echo storage matrix, and satisfying the relation: (M-1) Δt=t c The discrete fourier transform of the radar target echo is then:
(2) Wherein S is IF As a signal function, f is frequency, T c In order for the duration of the pulse to be of a duration,for phase change, f b Is a frequency change; m is the dimension of the fast time, and the value of M is more than or equal to 0 and less than or equal to M.
In step S4, a distance parameter between the millimeter wave radar and the detected target object is obtained, and the following formula is adopted:
(3) Wherein γ=b/T c B is the signal bandwidth, T c For pulse duration, r c For distance information, m is the fast time dimension,for phase change, Δt is the sampling interval; j is an imaginary unit and e is a natural constant.
Fig. 6a is a range profile of a detected target, which is 1.5m in front of the radar. And calculating a distance unit where the target is located according to the position where the target is located and the distance resolution of the radar, and providing a basis for the next phase extraction.
In steps S5 to S7:
then extracting phase and unwrapping; as shown in fig. 6b, first, determining a distance unit where the target is located, that is, a grid where m1 is located in the graph; then, the phase of the distance unit from 1 to N of the target is calculated along the slow time dimension, wherein N is the slow time dimension in the radar target echo storage matrix.
Extracting the phase of the distance unit of the target in FIG. 6a, obtaining the phase after arctangent operationThe value range is [ -pi, pi]And converting the extracted phase into a distance to obtain a living body breathing micro-motion signal. As a result, as shown in fig. 7, the raw data is nearly cluttered due to phase wrapping problems; the result in fig. 7 is not the correct movement of the living being, which requires an unwrapping operation.
The phase value is at [ -pi, pi through phase unwrapping]The phase recovery between the two is shown in fig. 8, where n is a sampling point, k is an integer coefficient of phase 2 pi, and i is an integer coefficient of phase 2 pi. The extracted phase is started from the second phase, and the latter phase is differed from the former phaseIf the difference is greater than pi, then +.>Subtracting 2π; if the difference is smaller than-pi, then +.>Plus 2 pi; if it is between [ -pi, pi]The middle part is kept->The value is unchanged. At this time, the mth cycle ends, and the (m+1) th cycle is entered.
The arctangent function obtains the phase in the interval [ -pi, pi]And (3) inner part. With a reasonably slow time sampling interval deltat s For a pair ofSampling is performed and the phase difference between two consecutive samples will remain within pi. Corresponding distance information r c (t) at sampling interval Δt s The variation in should be less than lambda/4, lambda being the wavelength. When the condition of reasonable sampling interval is satisfied, any phase change larger than pi can be corrected through the addition and subtraction 2 pi operation of phase unwrapping, but at least three sampling points can recover the correct phase change. If the above condition is not satisfied, a part of the sampling points cannot be unwound. The simulation results are shown in fig. 9, and thus an ideal living body breathing micro-motion signal can be obtained. The left part of fig. 9 represents the waveform diagram before the phase unwrapping, and the right part of fig. 9 represents the waveform diagram after the phase unwrapping.
In one embodiment, in order to further improve accuracy of the living body respiratory micro-motion signal, redundant displacement caused by the interference signal can be eliminated on a complex frequency domain of the sampling point, and static interference in the living body respiratory micro-motion signal can be removed.
The radar target echo is subjected to Fourier transformation to obtain phase changeAs shown in the following formula. Phase change->The living body respiratory micro-motion signal is obtained after phase unwrapping and unit conversion, wherein the static interference is included. The redundant displacement sigma is eliminated before the phase extraction 2 cos(θ 2 ) Sum sigma 2 sin(θ 2 ) The static interference in the living body breathing micro-motion signal can be removed.
In the middle ofPhase change, sigma 1 cos(θ 1 ) Imaginary part sigma 1 sin(θ 1 ) For the real and imaginary parts, σ, of the live respiratory micro-motion signal 2 cos(θ 2 ) Sum sigma 2 sin(θ 2 ) Is the displacement of the respiratory micro-motion signal of the living body on the complex plane.
The dynamic interference is expressed as irregular or regular movement of the whole or part of living body, the dynamic interference in the living body detection scene is mainly the regular movement of the security detection driving belt, and the waveform distortion phenomenon of the movement signal caused by the too high speed of the dynamic interference can be solved by adopting a method for shortening the slow time sampling interval delta t. Thus, a moving target signal is output, and living body breathing micro-motion information of an effective moving target is fed back to the security inspection system.
Finally, based on the vital sign information of the effective moving target, the judgment of whether the detected target object has a living body or not is realized. For example, whether the living things are hidden in the target object is judged by judging whether the signal accords with the heartbeat frequency and the respiratory frequency of the animal; if the judging signal accords with the heartbeat frequency and the respiratory frequency of the animal, judging that the living things are hidden in the target object, and sending out an alarm through the alarm equipment.
The biological detection method for the living body of the security inspection scene provided by the embodiment of the invention can be used for accurately extracting the breathing micro-motion signal of the living body of the detected target in the security inspection scene, thereby realizing the accurate detection of the micro-size living body in the security inspection target; the operation efficiency of the security inspection system and the accuracy of effective moving object detection can be improved, and the instantaneity and reliability of the security inspection system are enhanced.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. The biological detection method for the living body of the security inspection scene is characterized by comprising the following steps of:
s1, a millimeter wave radar transmitting antenna generates millimeter wave signals and sends the millimeter wave signals to a detected target object, and a millimeter wave radar receiving antenna receives the millimeter wave signals reflected by the detected target object;
s2, mixing the reflected millimeter wave signals with the transmitting signals to generate radar target echoes;
s3, separating an amplitude phase signal from the radar target echo, obtaining a continuous frequency characteristic signal through Fourier transformation, and performing discretization;
s4, converting the discretized frequency signal into a distance variable to obtain a distance parameter between the millimeter wave radar and the detected target object;
s5, extracting phase parameters according to the distance parameters;
s6, performing phase unwrapping treatment on the phase parameter to obtain a living organism breathing micro-motion signal;
and S7, judging whether the detected target object has a living body or not according to the living body biological respiration micro-motion signal.
2. The method according to claim 1, wherein the radar target echo storage mode in the step S2 is a data matrix including a fast time dimension M and a slow time dimension N.
3. The method for biological detection of a security scene as set forth in claim 2, wherein the step S3 includes:
s301, taking the fast time dimension M as an amplitude phase signal, and carrying out Fourier transformation to obtain a continuous frequency characteristic signal through the following formula (1);
(1) Wherein S is IF Is a signal function, f is a frequency, c is a speed of light, T c In order for the duration of the pulse to be of a duration,for phase change, f b Is a frequency change; j is an imaginary unit, exp represents an exponential function based on a natural constant e, and t is time;
s302, setting a sampling interval as deltat, setting the number of time domain sampling points in a pulse as a fast time dimension M in a radar target echo storage matrix, and satisfying the relation: (M-1) Δt=t c The discrete fourier transform of the radar target echo is then:
(2) Wherein S is IF As a signal function, f is frequency, T c In order for the duration of the pulse to be of a duration,for phase change, f b Is a frequency change; m is the dimension of the fast time, and the value of M is more than or equal to 0 and less than or equal to M.
4. The method for biological detection of a security inspection scene according to claim 3, wherein in step S4, a distance parameter between the millimeter wave radar and the detected target object is obtained by adopting the following formula:
(3) Wherein γ=b/T c B is the signal bandwidth, T c In order for the duration of the pulse to be of a duration,r c for distance information, m is the fast time dimension,for phase change, Δt is the sampling interval; j is an imaginary unit and e is a natural constant.
5. The method for biological detection of a security scene as recited in claim 4, wherein the step S5 includes:
s501, processing the slow time dimension N, determining a distance unit where a target is located, and solving the phase of the distance unit where the target is located from 1 to N along the slow time dimension N;
s502, extracting the phase of a distance unit where the target is located, and obtaining the phase after arctangent operationThe value range is [ -pi, pi]。
6. The method for biological detection of a security scene as recited in claim 5, wherein the step S6 includes:
the phase value is within [ -pi, pi]Recovering the phase between the two phases, and starting from the second phase, and differencing the latter phase with the former phase
If the difference is greater than pi, thenSubtracting 2π; if the difference is smaller than-pi, then +.>Plus 2 pi; if it is between [ -pi, pi]The middle part is kept->The value is unchanged; at this time, the mth cycle is ended, and the (m+1) th cycle is entered;
until the cycle is finished, the phase value obtained each time forms a living body breathing micro-motion signal.
7. The method for biological detection of a security scene living organism according to claim 6, further comprising:
and (3) performing static interference removal and dynamic interference removal on the living body breathing micro-motion signals in the step S6.
8. The method for biological detection of a security scene living organism according to claim 7, wherein the static interference removal comprises:
and eliminating redundant displacement caused by interference signals on a complex frequency domain of the sampling points, and removing static interference in the living body respiratory micro-motion signals.
9. The method for biological detection of a security scene in vivo according to claim 7, wherein said dynamic interference removal comprises:
the dynamic interference is expressed as irregular or regular movement of the whole or part of living body, the dynamic interference in the living body detection scene is the regular movement of the security detection driving belt, and the phenomenon of waveform distortion of the movement signal caused by the too high speed of the dynamic interference is solved by adopting a method for shortening the slow time sampling interval delta t.
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