A security checkpoint scene living body detection method

Abstract

The application discloses a kind of security scene living body detection methods, comprising: millimeter wave radar transmitting antenna generates millimeter wave signal and sends to the detected target object, receives the millimeter wave signal reflected back by the detected target object;The millimeter wave signal reflected back is mixed with transmission signal to generate radar target echo;Separate out amplitude phase signal and get continuous frequency characteristic signal by Fourier transform, and carry out discretization processing;Convert the frequency signal after discretization processing into distance variable, obtain the distance parameter of millimeter wave radar and the detected target object;According to distance parameter, extract phase parameter;Phase parameter is phase unwrapping processing, and the living body breathing micro-motion signal is obtained;According to the micro-motion signal caused by living body breathing, whether the detected target object exists living body is judged.The detection method can accurately detect whether there is living thing in luggage, express piece in security scene, improve the security efficiency of airport, customs transit goods.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of living body detection equipment, and more particularly to a living body detection method for security check scenes. BACKGROUND

[0002] At present, in recent years, as the demand for species safety becomes more and more common, the security check field has solved the problem of living body detection through technological means.

[0003] Problems existing in the prior art:

[0004] 1. At present, the living body detection equipment for luggage and express parcels in the security check scene mainly uses an X-ray machine, and the detection efficiency is low after imaging processing and manual identification.

[0005] 2. Millimeter wave radar detection technology gradually becomes a security check method vigorously promoted in important places such as airports and customs due to its high biological safety, high precision, non-contact, high security check efficiency and other characteristics.

[0006] 3. The millimeter wave radar detection has high precision, but has certain limitations in the detection target classification processing.

[0007] 4. The fast Fourier transform algorithm used in traditional radar signal processing has high computational complexity, and cannot effectively process dynamic noise problems. In a complex environment, it is a technical difficulty to remove false alarm targets for effective moving target detection when the millimeter wave radar performs real-time signal processing. SUMMARY

[0008] Therefore, the present application provides a living body detection method for security check scenes, which at least partially solves the above technical problems.

[0009] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0010] The present application provides a living body detection method for security check scenes, comprising the following steps:

[0011] S1, a millimeter wave radar transmitting antenna generates a millimeter wave signal and sends it to a detected target object, and a millimeter wave radar receiving antenna receives the millimeter wave signal reflected back by the detected target object;

[0012] S2, the reflected millimeter wave signal is mixed with the transmitted signal to generate a radar target echo;

[0013] S3, the amplitude and phase signals are separated from the radar target echo, and a continuous frequency characteristic signal is obtained through Fourier transform and discretization processing;

[0014] S4, convert the frequency signal after the discretization processing into a distance variable to obtain a distance parameter of the millimeter wave radar and the detected target object;

[0015] S5, extract a phase parameter according to the distance parameter;

[0016] S6, perform phase unwrapping processing on the phase parameter to obtain a living body breathing micro-motion signal;

[0017] S7, according to the living body breathing micro-motion signal, realize the judgment of whether the detected target object exists living body.

[0018] In one embodiment, the radar target echo storage mode in step S2 is a data matrix, including a fast time dimension M and a slow time dimension N.

[0019] In one embodiment, step S3 includes:

[0020] S301, the fast time dimension M is taken as an amplitude phase signal to obtain a continuous frequency characteristic signal by Fourier transform through formula (1) as follows:

[0021]

[0022] (1) In the formula, S IF is a signal function, f is a frequency, c is a light speed, T c is a pulse duration, is a phase change, f b is a frequency change; j is an imaginary unit, exp represents an exponential function with a natural constant e as a base, and t is time;

[0023] S302, assuming that a sampling interval is Δt, an intra-pulse time domain sampling point number is a fast time dimension M in the radar target echo storage matrix, and a relationship formula (M-1)Δt=T c is satisfied, then the discrete Fourier transform of the radar target echo is:

[0024]

[0025] (3) In the formula, S IF is a signal function, f is a frequency, T c is a pulse duration, is a phase change, f b is a frequency change; m is a fast time dimension number, and the value is 0≤m≤M.

[0026] In one embodiment, in step S4, the distance parameter of the millimeter wave radar and the detected target object is obtained by using the following formula:

[0027]

[0028] (3) where γ = B / T c , B is signal bandwidth, T c is pulse duration, r c is distance signal

[0029] , m is fast time dimension, is phase change, Δt is sampling interval; j is imaginary unit, e is natural constant.

[0030] In one embodiment, the step S5 comprises:

[0031] S501, processing the slow time dimension N to determine the distance unit where the target is located, and obtaining the phase of the distance unit where the target is located from 1 to N along the slow time dimension N;

[0032] S502, extracting the phase of the distance unit where the target is located, and obtaining the phase after arctangent operation, the value range of which is [-π, π].

[0033] In one embodiment, the step S6 comprises:

[0034] restoring the phase value between [-π, π], extracting the phase from the second one, and obtaining the difference between the latter phase and the former phase, that is

[0035] if the difference is greater than π, subtracting 2π; if the difference is less than -π, adding 2π; if the difference is between [-π, π], keeping the value unchanged; at this time, the mth loop ends, and the m+1th loop starts;

[0036] until the loop ends, and the phase value obtained each time forms the living body respiratory micro-motion signal.

[0037] In one embodiment, the method further comprises:

[0038] removing static interference and dynamic interference from the living body respiratory micro-motion signal in step S6.

[0039] In one embodiment, the static interference removal comprises:

[0040] eliminating the redundant displacement caused by the interference signal on the sampling point complex frequency domain, and removing the static interference in the living body respiratory micro-motion signal.

[0041] In one embodiment, the dynamic interference removal comprises:

[0042] ​​​The dynamic interference is irregular or regular movement of the whole or part of the living body, and the dynamic interference in the living body detection scene is regular movement of the security transmission belt, and the method of shortening the slow time sampling interval At is adopted to solve the waveform distortion phenomenon of the movement signal caused by too fast dynamic interference speed.

[0043] According to the technical solution, compared with the prior art, the application provides a security scene living body detection method, and adopts millimeter wave radar detection to solve the technical problem of fast and accurate detection and identification of the living body target in the security scene. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0045] Figure 1 The detection equipment structure schematic diagram based on the millimeter wave radar provided by the application is shown in the figure.

[0046] Figure 2 The three-dimensional structure schematic diagram of the shielding box provided by the application is shown in the figure.

[0047] Figure 3 The security scene living body detection principle schematic diagram based on the millimeter wave radar provided by the application is shown in the figure.

[0048] Figure 4 The security scene living body detection method flow chart provided by the application is shown in the figure.

[0049] Figure 5 The radar target echo storage form in the host computer provided by the application is shown in the figure.

[0050] Figure 6a The range image of a certain detection target provided by the application is shown in the figure.

[0051] Figure 6b The distance unit schematic diagram in which the target is located provided by the application is shown in the figure.

[0052] Figure 7 The living body breathing micro-motion signal schematic diagram with clutter provided by the application is shown in the figure.

[0053] Figure 8 The phase unwrapping specific flow chart provided by the application is shown in the figure.

[0054] Figure 9A schematic diagram of the signals before and after phase unwinding provided by the present invention;

[0055] In the attached diagram: 1-Detection device body; 2-Host computer; 11-Shielding box; 12-Security inspection conveyor belt; 13-Camera equipment; 14-Electromagnetic shielding curtain; 15-Start button; 16-Host computer data interface; 17-Alarm equipment; 18-Wave-absorbing wedge; 19-Millimeter-wave radar equipment. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] This invention provides a method for detecting live organisms in security inspection scenarios. The equipment used for detecting live organisms in security inspection scenarios is described in reference to... Figure 1 As shown, it includes: the detection device body 1 and the host computer 2;

[0058] The detection device body 1 includes a shielded box 11 and a security conveyor belt 12. The security conveyor belt 12 is used to transport the target object to be detected through the shielded box 11. The shielded box 11 is U-shaped, upside down on the security conveyor belt 12, and is connected to the frames on both sides of the security conveyor belt 12 by bolts.

[0059] like Figure 2 As shown, a millimeter-wave radar device 19 is installed inside the shielded box 11. The millimeter-wave radar device 19 is fixed to the top or side wall of the U-shaped shielded box 11 using plastic parts or metal screws. The millimeter-wave radar device 19 transmits millimeter-wave radar signals to the target object being detected, receives the reflected millimeter-wave radar signals, and mixes them with the transmitted millimeter-wave radar signals to form a radar target echo. This echo is then transmitted to the host computer 2 via wired or wireless means. The host computer 2 processes the received radar target echo to detect whether a living being exists in the target object.

[0060] For further electromagnetic interference signal shielding, such as Figure 2 As shown, the inner wall of the shielding box can be equipped with wave-absorbing wedges 18 in the shape of pyramids or strips, mainly composed of polyurethane foam, non-woven flame-retardant fabric, silicate board metal film assembly, etc. In addition, a camera device 13 is also provided on the top wall inside the shielding box 11, which can be used to take pictures of the detection target as tags.

[0061] like Figure 3As shown, the millimeter-wave radar device 19 includes: a transmitting antenna, a receiving antenna, a sawtooth wave generator, and an analog-to-digital converter;

[0062] The sawtooth wave signal generated by the sawtooth wave generator is frequency multiplied and then transmitted by the transmitting antenna to the target object. The receiving antenna receives the millimeter wave signal reflected back by the target object and mixes it with the transmitted millimeter wave radar signal to form a radar target echo, which is then stored in the ADC buffer through analog-to-digital conversion.

[0063] The host computer includes: a sampling module, a digital signal processing module, and a display terminal;

[0064] The sampling module is used to acquire digital signals from the ADC buffer;

[0065] The digital signal processing module is used to process the acquired digital signals in real time, thereby extracting the micro-movement signals of live respiration and realizing the detection of whether there is a living body in the target object.

[0066] The display terminal is used to display the waveform of the micro-movement signal of in vivo respiration. The host computer also includes: such as... Figure 1 As shown, the alarm device 17 installed on the top of the shielded box is used to trigger an audible and visual alarm when the presence of a living person is determined based on the micro-movement signal of the living person's breathing.

[0067] The detection method is as follows: Figure 4 As shown, it includes the following steps:

[0068] S1. The millimeter-wave radar transmitting antenna generates millimeter-wave signals and sends them to the target object being detected. The millimeter-wave radar receiving antenna receives the millimeter-wave signals reflected back from the target object being detected.

[0069] S2. The reflected millimeter-wave signal is mixed with the transmitted signal to generate a radar target echo;

[0070] S3. Separate the amplitude and phase signals from the radar target echo and obtain continuous frequency characteristic signals through Fourier transform, and then perform discretization processing.

[0071] S4. Convert the discretized frequency signal into a range variable to obtain the distance parameters between the millimeter-wave radar and the detected target object.

[0072] S5. Extract the phase parameters based on the distance parameters;

[0073] S6. Perform phase unwinding processing on the phase parameters to obtain the micro-motion signal of in vivo respiration;

[0074] S7. Based on the micro-motion signal of the living body's respiration, the determination of whether there is a living body in the target object being detected is realized.

[0075] The steps described above are explained below:

[0076] In step S2, the data matrix for sampling radar wave signals includes a fast time dimension M and a slow time dimension N. The radar target echo is stored in the host computer in the following format: Figure 5 As shown, this is called the data matrix. n represents the number of the slow-time dimension index, m represents the number of the fast-time dimension index, N represents the total number of pulse signals transmitted by the radar, and M represents the number of sampling points for each radar target echo. The product of N and the pulse repetition time is the total monitoring duration, called the slow-time dimension; M points are sampled within the duration of each pulse, and since the pulse duration is extremely short, this is called the fast-time dimension.

[0077] In step S3, in order to determine the target position, a Fourier transform is performed on the fast time dimension (i.e., each row) of the data matrix. Computers can only process discretized data, so continuous signals need to be discretized.

[0078] Step S3 specifically includes:

[0079] S301. The fast time dimension M is used as the amplitude and phase signal and Fourier transform is performed using the following formula (1) to obtain the continuous frequency characteristic signal;

[0080]

[0081] (1) In the formula, S IF Let f be the signal function, c be the speed of light, and T be the frequency. c The duration of the pulse. For phase change, f b t represents frequency variation; j is the imaginary unit, exp represents an exponential function with the natural constant e as the base, and t is time.

[0082] S302. Let the sampling interval be Δt, and the number of time-domain sampling points within a pulse be M, which is the fast time dimension of the radar target echo storage matrix, satisfying the relationship: (M-1)Δt=T c Then the discrete Fourier transform of the radar target echo is:

[0083]

[0084] (2) In the formula, S IF Let f be the signal function, and T be the frequency. c The duration of the pulse. For phase change, f b denoted as frequency variation; m is the fast time dimension, with a value of 0 ≤ m ≤ M.

[0085] In step S4, the distance parameters between the millimeter-wave radar and the detected target object are obtained using the following formula:

[0086]

[0087] (3) In the formula, γ=B / T c B is the signal bandwidth, T c r is the pulse duration. c For distance information, m is the fast time dimension. For phase change, Δt is the sampling interval; j is the imaginary unit, and e is the natural constant.

[0088] Figure 6a This image shows the range profile of a target located 1.5m in front of the radar. Based on the target's position and the radar's range resolution, the target's range cell is calculated, providing a basis for subsequent phase extraction.

[0089] In steps S5 to S7:

[0090] Next, phase extraction and unwinding are performed; as follows: Figure 6b As shown, first, the range cell where the target is located is determined, that is, the grid where m1 is located in the figure; then, the phase of the range cell where the target is located from 1 to N is calculated along the slow time dimension, where N is the slow time dimension in the radar target echo storage matrix.

[0091] extract Figure 6a The phase of the target's range cell is obtained by performing an arctangent operation. The value range is [-π, π]. The extracted phase is converted into distance to obtain the in vivo respiratory micromotion signal. The results are as follows: Figure 7 As shown, the original data is almost cluttered due to phase entanglement; Figure 7 The results in the diagram do not accurately reflect the movement of the living organism, and an untangling operation is required.

[0092] The phase between [-π, π] is restored by phase unwrapping. The specific algorithm flow is as follows: Figure 8 As shown, n is the sampling point, k is the integer coefficient for subtracting 2π from the phase, and i is the integer coefficient for adding 2π to the phase. The extracted phases start from the second one, and the difference between each subsequent phase and the previous phase is calculated... If the difference is greater than π, then Subtract 2π; if the difference is less than -π, then... Add 2π; if it is between [-π, π], then keep it unchanged. The value remains unchanged. At this point, the m-th iteration ends, and the (m+1)-th iteration begins.

[0093] The arctangent function yields a phase within the interval [-π, π]. A reasonable slow sampling interval Δt is used. s right During sampling, the phase difference between two consecutive samples will be kept within π. This corresponds to the distance information r. c (t) at sampling interval Δt s The change within the wavelength should be less than λ / 4, where λ is the wavelength. When the sampling interval is reasonable, any phase change greater than π will be corrected by adding or subtracting 2π during phase unwrapping, but at least three sampling points are required to recover the correct phase change. If the above conditions are not met, some sampling points will fail to unwrap. Simulation results are as follows... Figure 9 As shown, this allows us to obtain ideal micro-motion signals for in vivo respiration. Figure 9 The left part represents the waveform before phase unwinding. Figure 9 The right part represents the waveform after phase unwinding.

[0094] In one embodiment, to further improve the accuracy of the in vivo respiratory micro-motion signal, redundant displacement caused by interference signals can be eliminated in the complex frequency domain of the sampling point to remove static interference in the in vivo respiratory micro-motion signal.

[0095] The phase change is obtained after Fourier transform of the radar target echo. As shown in the following formula. Phase change After phase unwinding and unit conversion, the in vivo respiratory micromotion signal is obtained, which includes static interference. Before phase extraction, the redundant displacements σ2cos(θ2) and σ2sin(θ2) are eliminated to remove the static interference in the in vivo respiratory micromotion signal.

[0096]

[0097] In the formula The phase changes are represented by σ1cos(θ1) and the imaginary part σ1sin(θ1), which are the real and imaginary parts of the micro-motion signal of in vivo respiration, respectively, and σ2cos(θ2) and σ2sin(θ2) are the displacements of the micro-motion signal of in vivo respiration in the complex plane.

[0098] Dynamic interference manifests as irregular or regular movement of the entire or part of a living object. In liveness detection scenarios, dynamic interference is mainly the regular movement of the security inspection conveyor belt. The distortion of the motion signal waveform caused by excessively fast dynamic interference speed can be addressed by shortening the slow sampling interval Δt. This allows for the output of a moving target signal, feeding back the micro-movement information of the effective moving target's breathing to the security inspection system.

[0099] Finally, based on the vital signs information of the aforementioned effective moving targets, the system determines whether a living being exists within the detected target object. For example, by judging whether the signal matches the heart rate and respiratory rate of an animal, it can be determined whether a living being is hidden inside the target object; if the signal matches the heart rate and respiratory rate of an animal, it is determined that a living being is hidden inside the target object, and an alarm is triggered through the alarm device.

[0100] This invention provides a method for detecting live organisms in security inspection scenarios. In security inspection scenarios, it can be used to accurately extract the micro-movement signals of the target's breathing, thereby achieving accurate detection of tiny live organisms in security inspection targets. This method can improve the operating efficiency of security inspection systems and the accuracy of effective moving target detection, and enhance the real-time performance and reliability of security inspection systems.

[0101] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0102] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not 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

1. A method for detecting live organisms in a security inspection scenario, characterized in that, Includes the following steps: S1. The millimeter-wave radar transmitting antenna generates millimeter-wave signals and sends them to the target object being detected. The millimeter-wave radar receiving antenna receives the millimeter-wave signals reflected back from the target object being detected. S2. The reflected millimeter-wave signal is mixed with the transmitted signal to generate a radar target echo; S3. Separate the amplitude and phase signals from the radar target echo and obtain continuous frequency characteristic signals through Fourier transform, and then perform discretization processing. S4. Convert the discretized frequency signal into a range variable to obtain the distance parameters between the millimeter-wave radar and the detected target object. S5. Extract the phase parameters based on the distance parameters; S6. Perform phase unwinding processing on the phase parameters to obtain the micro-motion signal of respiration in living organisms; S7. Based on the micro-motion signal of the living organism's respiration, determine whether there is a living organism in the target object being detected; The radar target echo storage method in step S2 is a data matrix, including fast time dimension M and slow time dimension N; Step S3 includes: S301. The fast time dimension M is used as the amplitude and phase signal and Fourier transform is performed using the following formula (1) to obtain the continuous frequency characteristic signal; (1) In the formula, S IF Let f be the signal function, c be the speed of light, and T be the frequency. c The duration of the pulse. For phase change, f b t represents frequency variation; j is the imaginary unit, exp represents an exponential function with the natural constant e as the base, and t is time. S302. Let the sampling interval be Δt, and the number of time-domain sampling points within a pulse be M, which is the fast time dimension of the radar target echo storage matrix, satisfying the relationship: (M-1)Δt=T c Then the discrete Fourier transform of the radar target echo is: (2) In the formula, S IF Let f be the signal function, and T be the frequency. c The duration of the pulse. For phase change, f b The frequency variation is represented by m, which is the fast time dimension and takes a value of 0 ≤ m ≤ M. In step S4, the distance parameters between the millimeter-wave radar and the detected target object are obtained using the following formula: (3) In the formula, y = B / T c B is the signal bandwidth, T c r is the pulse duration. C For distance information, m is the fast time dimension. For phase change, Δt is the sampling interval; j is the imaginary unit, and e is the natural constant.

2. The method for detecting live organisms in a security inspection scenario according to claim 1, characterized in that, Step S5 includes: S501. Process the slow time dimension N to determine the distance cell where the target is located, and calculate the phase of the distance cell where the target is located from 1 to N along the slow time dimension N; S502, Extract the phase of the target's range cell. After arctangent operation, the phase range is found to be [-π, π].

3. The method for detecting live organisms in a security inspection scenario according to claim 2, characterized in that, Step S6 includes: The phases with phase values ​​between [-π, π] are restored. Starting from the second extracted phase, the difference between each subsequent phase and the previous phase is calculated. If the difference is greater than π, then 2π will be subtracted; if the difference is less than -π, then... Add 2π; if it is between [-π, π], then keep it unchanged. The value remains unchanged; at this point, the m-th iteration ends, and the (m+1)-th iteration begins. Until the end of the cycle, the phase values ​​acquired each time form a micro-motion signal of in vivo respiration.

4. The method for detecting live organisms in a security inspection scenario according to claim 3, characterized in that, The method further includes: Static and dynamic interference removal is performed on the in vivo respiratory micro-motion signals in step S6.

5. A method for detecting live organisms in a security inspection scenario according to claim 4, characterized in that, The static interference removal includes: Redundant displacement caused by interference signals is eliminated in the complex frequency domain of the sampling point, thus removing static interference from the micro-motion signal of in vivo respiration.

6. The method for detecting live organisms in a security inspection scenario according to claim 4, characterized in that, The dynamic interference removal includes: Dynamic interference manifests as irregular or regular movement of the whole or part of a living object. In the liveness detection scenario, dynamic interference is the regular movement of the security inspection conveyor belt. The method of shortening the slow time sampling interval Δt is used to solve the distortion of the motion signal waveform caused by the excessive speed of dynamic interference.

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