WO2020093068A1 - Systèmes et procédés d'identification de dispositifs électroniques - Google Patents

Systèmes et procédés d'identification de dispositifs électroniques Download PDF

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Publication number
WO2020093068A1
WO2020093068A1 PCT/US2019/059735 US2019059735W WO2020093068A1 WO 2020093068 A1 WO2020093068 A1 WO 2020093068A1 US 2019059735 W US2019059735 W US 2019059735W WO 2020093068 A1 WO2020093068 A1 WO 2020093068A1
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signal
response
nonlinear
electronic device
nonlinear response
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PCT/US2019/059735
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English (en)
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Wenyao Xu
Zhengxiong LI
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The Research Foundation For The State University Of New York
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/44—Program or device authentication
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/08—Systems for measuring distance only
    • G01S13/32—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated
    • G01S13/34—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal
    • G01S13/343—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated using transmission of continuous, frequency-modulated waves while heterodyning the received signal, or a signal derived therefrom, with a locally-generated signal related to the contemporaneously transmitted signal using sawtooth modulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/887—Radar or analogous systems specially adapted for specific applications for detection of concealed objects, e.g. contraband or weapons
    • G01S13/888—Radar or analogous systems specially adapted for specific applications for detection of concealed objects, e.g. contraband or weapons through wall detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/03—Details of HF subsystems specially adapted therefor, e.g. common to transmitter and receiver
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G01S7/356—Receivers involving particularities of FFT processing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/411—Identification of targets based on measurements of radar reflectivity
    • G01S7/412—Identification of targets based on measurements of radar reflectivity based on a comparison between measured values and known or stored values
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/414—Discriminating targets with respect to background clutter
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/148—Wavelet transforms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/08—Systems for measuring distance only
    • G01S13/32—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated
    • G01S13/36—Systems for measuring distance only using transmission of continuous waves, whether amplitude-, frequency-, or phase-modulated, or unmodulated with phase comparison between the received signal and the contemporaneously transmitted signal
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging

Definitions

  • the present disclosure relates to detection and recognition of electronic devices.
  • An entry security check is the current method for defending against malicious, hidden e-devices, and utilizes an X-ray machine at safety-critical sites (e.g., airports and embassy offices).
  • safety-critical sites e.g., airports and embassy offices.
  • their expensive cost and poor portability make it an infeasible solution against the proliferation and the deployment of portable e-devices.
  • the radiation emitted from X-rays is harmful to workers and persons passing through the checkpoints.
  • Other scanning methods based on metal scanners can only detect the existence of e-devices rather than recognize specific types directly.
  • Conventional computer vision methods cannot be applied because the camera utilized in computer visions systems cannot see through containers or bodies.
  • the present disclosure describes systems and methods to facilitate recognition of hidden e-devices.
  • the disclosure is illustrated using an exemplary system called E-Eye.
  • E-Eye Its features are (1) cost-efficient: the cost of the solution is affordable in daily life for large scale deployment; (2) portable: it is easy to use in the inspection of different containers (e.g ., delivery boxes, luggage, or even the human body) and various environments (e.g., postal offices, airports, factories, etc.); (3) non-invasive: it can avoid the obtrusive (even illegal) opening of containers in real practice which sacrifices efficiency and may cause privacy issues.
  • the presently disclosed techniques utilize the nonlinear response effect from electronic circuits when probed by a mmWave signal.
  • the intrinsic uniqueness of a circuit s hardware characteristics (e.g, a circuit’s components and circuit layout) generates a nonlinear response distinct from other circuits, which can serve as the identity of a device and/or brand.
  • This way we enable a novel sensing modality for noninvasive and cost-effective hidden e- devices recognition based on the mmWave field.
  • a exemplary, portable (11.8 cm x 4.5 cm x 1.8 cm) and light-weight (45.4 g) 24 GHz mmWave probe device was designed and built.
  • the exemplary device was used to probe using the mmWave and capture the returned nonlinear responses. We addressed the challenges in noise-isolation and coherence to achieve high-quality signal with low complexity and cost (less than US $100).
  • Systems of some embodiments may utilize a smartphone or similar device, and the signal may be transferred to the smartphone for processing.
  • a wavelet-based analysis module may be used, which module takes into consideration the unavoidable variance in the signal’s scaling and magnitude in practical usage.
  • a fine-tuned support vector machine (SVM) classifier was developed for robust recognition under various conditions. In the experiment, 46 e-devices were employed and the comprehensive results show that the exemplary E-Eye was able to accurately recognize each e- device brand under different scenarios.
  • SVM support vector machine
  • the present disclosure provides systems ⁇ and methods for recognizing hidden e- devices by exploring the nonlinear response effect of mmWave of e-devices. It was found that the circuit inside an e-device acts as a passive signal modulator which reflects back radio frequency (RF) signals with intrinsic identity information.
  • RF radio frequency
  • E-Eye was developed, providing an end-to-end system to facilitate the low-cost, non-invasive and robust hidden electronics recognition. Sensing hardware of the exemplary system was prototyped and a recognition algorithm was implemented for efficient and effective classification. E-Eye was evaluated under different sensing time efficiencies, sensing distances, and device orientations; E-Eye achieved more than 99% recognition rate.
  • a field study and a threat model study were deployed for evaluating the robustness of E-Eye under the impact of ambient environment, alien device, combined e-devices, and various cover materials. In both studies, the system obtained over 97% accuracy.
  • Figure 3 Six different e-devices present different nonlinear responses (the spectrums in the white box are distinct in frequency and amplitude) when forced by the same mmWave probe. The main circuit board of each e-device is displayed on the left.
  • Figure 4A The detected nonlinear response from cardboard and a Nexus 5. The cardboard’s nonlinear responses are negligible when compared to Nexus 5’s, indicating the feasibility of hidden e-device recognition.
  • Figure 5 A system overview for an exemplary embodiment of the disclosed E-
  • Figure 7 A flowchart of e-device recognition module, including three parts signal preprocessing and demodulation, wavelet-based nonlinear response analysis and feature extraction and fine-tuning recognition.
  • Figure 8 A Nexus 5 smartphone is sensed within a USPS box at 20 cm distance using the portable 24 GHz mmWave probe. The raw sensing signal was preprocessed and demodulated to extract the nonlinear response (a) The baseband signal parsed from the audio signal (b) The preprocessed reference signal (c) The nonlinear response signal after the signal demodulation (d) The spectrum for the nonlinear response signal.
  • Figure 9 The first level wavelet decomposition result of Nexus 5 nonlinear response, where (a) and (b) represent its low and high frequency information respectively.
  • Figure 11 Commodity electronic devices included in a study.
  • Figure 12 A setup used to evaluate a system of the present disclosure: (a) in a controlled lab environment, (b) in an open hall at the first floor of the building, and (c) at the entrance of an outdoor public parking lot.
  • Figure 15 Measurement accuracy under different sensing distances.
  • Figure 17 The alien device detection under six different alien device numbers.
  • Figure 18 Detection accuracy under six different human interventions.
  • Figure 20 Detection of combined e-devices.
  • Figure 21 Program 1 for fine-tune recognition according to an embodiment of the present disclosure.
  • Figure 22 A diagram of a system according to another embodiment of the present disclosure.
  • Figure 23 A chart of a method according to another embodiment of the present disclosure.
  • the present disclosure may be embodied as a system 10 for detecting an electronic device (e-device 90).
  • the e-device 90 may be hidden, for example, contained in or covered by an object 95.
  • the system 10 includes a transmitter 20 configured to transmit a probe signal.
  • the probe signal may be a frequency- modulated, continuous-wave signal.
  • the transmitter 20 may be a continuous-wave transmitter.
  • the frequency may be modified with a sawtooth pattern (as described below), but other patterns may be used.
  • the system may include a waveform generator (for example, a sawtooth voltage generator (SVG)) and the waveform generator may drive a voltage-controlled oscillator (VCO) so as to generate a frequency-modulated probe signal (see, e.g., Figure 6).
  • VCO voltage- controlled oscillator
  • the VCO may generate the probe signal at a center frequency, f c (which may be, or may be derived from, a reference signal).
  • the system 10 includes a receiver 30 configured to receive a response signal.
  • the receiver 30 and transmitter 20 are synchronized such that they are coherent.
  • the transmitter and receiver may be synchronized using a reference signal.
  • the response signal may be, or may include, the probe signal as modulated by the ambient environment.
  • the response signal may be, or may include, the probe signal as modulated by the e-device (further described below).
  • the response signal may be a reflected signal (e.g, the probe signal reflected back to the receiver of the system).
  • the system 10 includes a processor 40.
  • the processor may be, for example, a smartphone or other portable device, or make up a part of such a device. In other embodiments, the processor is, or makes up a part of a computer.
  • the processor may be a general purposed processor, field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), or any other such processing device.
  • the processor 40 is configured to extract a feature vector of a nonlinear response of the response signal.
  • the processor may locate each cycle of the response signal using a falling edge and/or adjacent rising edge of a reference signal (e.g, a clock signal), demodulate the response signal using the cycle location, and remove a baseband signal from the demodulated response signal to extract the nonlinear response.
  • a reference signal e.g, a clock signal
  • the processor may be configured to analyze the nonlinear response using a wavelet-based analysis, such as, for example, a wavelet transform (further described below).
  • a wavelet-based analysis may include removing a DC component of the nonlinear response and decomposing the nonlinear response using a wavelet transform to obtain an approximation signal and a detail signal.
  • the processor may be further configured to extract one or more time domain features and/or one or more frequency domain features of the
  • a nonlinear response may be extracted 115 from the response signal.
  • One or more time domain features and/or one or more frequency domain features of the approximation signal and the detail signal may be extracted 133 as a feature vector.
  • the feature vector may be classified 136 so as to recognize the electronic device.
  • the feature vector may be classified using a support vector machine (SVM).
  • SVM support vector machine
  • Embodiments of the presently-disclosed methods and systems may recognize the electronics hidden behind different cover materials (e.g ., paper, plastic, sweater, leather, wood, brick, and so on) and in different body positions (e.g., orientations).
  • Embodiments of the presently-disclosed methods and systems may recognize a category and/or a brand of electronic device(s). Recognition of electronic devices may occur at distances ranging from 1 cm to 20 meters, though other distances may be possible depending on the selected parameters (for example, probe signal frequency).
  • Nonlinear Effects from E-device As shown in Figure 2, when an e-device enters an RF beam field, chips, connectors, and metal traces of printed circuit board (PCB) of the e-device can be viewed as an array of antennas in the resolution of mmWave. These antennas with inductance (L), capacitance (C), and resistance (R), act as a passive processor and manipulate the transmitted mmWave signals of the RF beam.
  • L inductance
  • C capacitance
  • R resistance
  • a(t) is the complex power-series for the nonlinear system, stands for convolution computing, and hf(t ' ) is the ideal bandpass filter function for the carrier bandwidth.
  • Carrier Frequency Selection Selecting a transmit frequency (and consequently a receive frequency) requires common trade-offs associated with longer versus shorter wavelengths for radar, which include availability of components (e.g, amplifiers and filters), realization of an acceptable gain for the antennas to achieve a sufficient signal -to-noise ratio (SNR) and exploitation of the radar cross-section (RCS) associated with a particular set of targets.
  • SNR signal -to-noise ratio
  • RCS radar cross-section
  • c is the propagation speed of a radar wave in air.
  • SHF super high frequency
  • the radar will transmit frequencies in or near the super high frequency (SHF) band, ranging from 3 GHz to 30 GHz.
  • SHF super high frequency
  • FIG. 4(a) shows that within the area of the nonlinear response , there is little demodulated signal amplitude for cardboard (less than 0.016 V, ambient noise and thermal noise actually), while for a Nexus 5 e-device, the demodulated nonlinear response signal is quite visible (more than 0.212 V, 13.5x larger than cardboard’s) (more detailed analysis about the nonlinear response below).
  • Figure 4(b) shows that their signal spectrums are significantly different, which proves the feasibility of unobtrusive hidden e-device recognition.
  • E-Eye a portable, non-invasive and robust system to facilitate recognition of the hidden e-devices.
  • E-Eye a portable, non-invasive and robust system to facilitate recognition of the hidden e-devices.
  • FIG. 5 An end-to-end overview of a non-limiting system is shown in Figure 5.
  • E-Eye Hardware A mmWave probe using a smartphone is designed to remotely and robustly acquire an e-device’s non-linear response for recognition. Specifically, the probe transmits a continuous wave signal and processes/demodulates the response signal. Resulting data is sent to the processor (e.g ., the smartphone) for recognition.
  • the data may be small, for example, on the order of kilobyte (KB)-size data.
  • E-Eye Software Once the data is received by the processor, the processor (e.g., an e-device recognition module) first performs preprocessing and/or demodulation to filter interference and noise. Then, the effective features are extracted from the nonlinear responses via wavelet-based analysis. A fine-tuned classification algorithm is used to recognize the e- device type based on the extracted features. Result can then be displayed to the inspector using, for example, the display of the smartphone.
  • the processor e.g., an e-device recognition module
  • the exemplary system is capable of transmitting a 24 GHz carrier signal and receiving the returned nonlinear responses.
  • a schematic of the exemplary mmWave probe is shown in Figure 6. It comprises a radio frequency (RF) board and a baseband board.
  • the RF board includes a pair of array antennas (i.e., Tx and Rx), a voltage controlled oscillator (VCO), a pair of low noise amplifiers (LNA), and a six-port structure.
  • the baseband board contains baseband amplifiers (BA) and an on-board sawtooth voltage generator (SVG).
  • a six-port circuit is a simple structure, as a quadrature mixer, to down-convert RF signals into baseband, avoiding the use of expensive integrated mixer chips.
  • the six-port structure includes three quadrature couplers and one rat-race coupler. Ports 1 and 2 of the six-port structure are inputs for a local oscillator (LO) drive and the RF signal,
  • LO local oscillator
  • Coherence is used for the mmWave probe to obtain the effective information of the e-device. Rather than sharing synchronous clocks at the signal generation and acquisition stages, which increases the complexity and cost of the system, in the exemplary E- Eye embodiment, the coherence property of the mmWave probe is obtained by simultaneously sampling the reference signal and the baseband signal (further described below, under the heading“Signal Demodulation”). In order to control the VCO, the reference signal is phase- locked to the sawtooth voltage signal. In the synchronization procedure, the phase of each beat- signal period is aligned in the digital domain after sampling the reference signal and the baseband signal. Thus, in this method, synchronous clocks shared between generation and acquisition stages are not required, which simplifies the hardware design.
  • Performance of the mmWave probe may depend primarily on three factors: detection range, range resolution, and the
  • f s is the sampling frequency on the baseband board and f c is the center frequency, which is 24 GHz.
  • a larger detection range R d requires a longer frequency ramp repetition period T and smaller transmitted bandwidth B.
  • a higher range resolution A R requires a wider bandwidth B.
  • a faster non-ambiguous wireless signal velocity v max requires a shorter T.
  • the bandwidth of the transmitted signal (B) was 450 MHz with a center frequency (f c ) of 24 GHz, and the transmitted average power was around
  • the median filter was combined as it runs through the signal entry by entry, replacing each entry with the median of neighboring entries to remove the high frequency noise.
  • Other processing techniques will be apparent in light of the present disclosure and are within the present scope.
  • Wavelet-based Nonlinear Response Analysis Wavelet transform (WT) is an effective multi-resolution analysis tool for signal decomposition.
  • WT approach can overcome the shortcoming of Fourier analysis, which only works in the frequency domain, not in the time domain.
  • the signal can be decomposed into many groups of coefficients in different scales with WT through different scaled versions. After removing the DC component, y(t) becomes a signal with zero-mean and some variance and satisfies the following condition:
  • Equation (2) the wavelet-based analysis is achieved as Equation (2):
  • the analyzing wavelet should satisfy the admissibility condition, given in the following: are the Fourier transform of respectively. Also, and are constants for corresponding wavelets. Subsequently, we get the approximation signal as shown in Figure 9(a) and the detail signal in Figure 9(b). Finally, for comprehensive characterization of the nonlinear response, we also get the spectral approximation and detail signals by Fast Fourier Transform (FFT) for further feature extraction.
  • FFT Fast Fourier Transform
  • Electronics recognition can be treated as a classification problem.
  • E-Eye system uses supervised learning to classify e-device types, beginning with a training phase followed by testing, as illustrated in Program 1 ( Figure 21).
  • Program 1 Figure 21
  • some e-devices known as alien devices
  • a Classifier and a Decision maker were implemented to output a final recognition result.
  • Health Hazards Compared to other security screening techniques (e.g .,
  • E-Eye has a much smaller radiation factor, i.e., a 1.2 W power consumption and an 8 dBm radio transmission power. Considering that typical public WiFi spots have about 20 to 30 dBm of output power, E-Eye is a considerably safe screening tool, even for cardiac device patients.
  • Metal Intervention Metal has a stronger reflection on EM wave compared to other materials. We realize that a metal case shields a large portion of RF signals. By deploying an additional metal hidden material (e.g., an e-device inside a metal box), it is difficult for E-Eye to recognize the covered e-device. This limitation can be solved by detecting the existence of metal.
  • an additional metal hidden material e.g., an e-device inside a metal box
  • each feature vector has 26 dimensions data of size 0.2 KB around.
  • the template for each device seizes 14 KB size data in the experiment setup.
  • X-ray Imaging The X-ray baggage scanner operates based on the different X- radiation absorption rates of the penetrated objects and can accordingly produce the shape image of the objects. The typical cost of such a scanner can reach US $50,000. Besides the undesired privacy concerns raised by the image of personal belongings, x-radiation also has harmful effects on human.
  • Terahertz Imaging Terahertz (THz) imaging is also exploited in package
  • Electromagnetic Emission Sensing Studies find that e-devices transmit
  • E-Eye is the first mmWave sensing application to explore nonlinear effects for hidden electronics recognition.
  • Example 1 A system for detecting and recognizing one or more electronic device, comprising: a continuous-wave transmitter configured to transmit a probe signal; a receiver configured to receive a response signal; wherein the receiver is synchronized to the transmitter (: i.e ., coherent); and a processor configured to: demodulate and extract a feature vector of a nonlinear response component of the response signal; and analyze the feature vector to detect an electronic device.
  • Example 2 The system of Example 1, wherein the probe signal is a frequency- modulated continuous-wave signal.
  • Example 7 The system of Example 6, wherein the wavelet-based analysis comprises: removing a DC component of the nonlinear response signal; decomposing the nonlinear response signal using a wavelet transform to obtain an approximation signal and a detail signal; and extracting time domain features and frequency domain features of the approximation signal and detail signal as a feature vector.
  • Example 9 The system of Example 8, wherein the classifier is a support vector machine.
  • Example 15 The system of Example 10, wherein the system can recognize electronics devices at a distance from 1 cm to 20 meters.
  • Example 22 The system of Example 21, wherein the processor is configured to analyze the nonlinear response using a wavelet-based analysis.
  • Example 23 The system of Example 22, wherein the wavelet-based analysis comprises: removing a DC component of the nonlinear response; and decomposing the nonlinear response using a wavelet transform to obtain an approximation signal and a detail signal.
  • Example 24 The system of Example 23, wherein the processor is further configured to extract one or more time domain features and/or one or more frequency domain features of the approximation signal and the detail signal as a feature vector.
  • Example 25 The system of any one of Examples 16-24, wherein the processor is further configured to include a classifier to recognize the electronic device based on the feature.
  • Example 26 The system of Example 25, wherein the classifier is a support vector machine.
  • Example 27 A method for detecting an electronic device, comprising:
  • Example 28 The method of Example 27, wherein the probe signal is a frequency- modulated continuous-wave signal.
  • Example 29 The method of any one of Examples 27-28, wherein detecting an electronic device includes recognizing the electronic device.
  • Example 30 The method of any one of Examples 27-29, further comprising extracting the nonlinear response from the response signal.
  • Example 31 The method of Example 30, wherein extracting the nonlinear response comprises: locating each cycle of the response signal using a falling edge and/or adjacent rising edge of the reference signal; demodulating the response signal using the cycle location; and removing a baseband signal from the demodulated response signal to extract the nonlinear response from the demodulated response signal.
  • Example 32 The method of any one of Examples 27-31, further comprising analyzing the nonlinear response using a wavelet-based analysis.
  • Example 34 The method of Example 33, further comprising extracting one or more time domain features and/or one or more frequency domain features of the approximation signal and the detail signal as a feature vector.
  • Example 35 The method of any one of Examples 27-34, further comprising classifying the feature vector to recognize the electronic device.
  • Example 36 The method of Example 35, wherein the feature vector is classified using a support vector machine.

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Abstract

L'invention concerne un système de détection d'un dispositif électronique qui comprend un émetteur configuré pour émettre un signal de sonde et un récepteur cohérent configuré pour recevoir un signal de réponse, le récepteur étant synchronisé avec l'émetteur. Un processeur est configuré pour extraire un vecteur de caractéristiques d'une réponse non linéaire du signal de réponse et pour détecter un dispositif électronique sur la base du vecteur de caractéristiques.
PCT/US2019/059735 2018-11-03 2019-11-04 Systèmes et procédés d'identification de dispositifs électroniques WO2020093068A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113644920A (zh) * 2021-06-16 2021-11-12 北京协同创新研究院 一种用于脑部成像的微波多路收发系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1996002990A2 (fr) * 1994-07-13 1996-02-01 Hd-Divine Procede et dispositif de synchronisation d'un emetteur et d'un recepteur dans un systeme numerique
US20040260169A1 (en) * 2001-09-21 2004-12-23 Karsten Sternnickel Nonlinear noise reduction for magnetocardiograms using wavelet transforms
US7464005B1 (en) * 2007-06-29 2008-12-09 The Curators Of The University Of Missouri Electromagnetic emissions stimulation and detection system
US9721173B2 (en) * 2014-04-04 2017-08-01 Conduent Business Services, Llc Machine learning approach for detecting mobile phone usage by a driver
US10067221B2 (en) * 2015-04-06 2018-09-04 Texas Instruments Incorporated Interference detection in a frequency modulated continuous wave (FMCW) radar system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1996002990A2 (fr) * 1994-07-13 1996-02-01 Hd-Divine Procede et dispositif de synchronisation d'un emetteur et d'un recepteur dans un systeme numerique
US20040260169A1 (en) * 2001-09-21 2004-12-23 Karsten Sternnickel Nonlinear noise reduction for magnetocardiograms using wavelet transforms
US7464005B1 (en) * 2007-06-29 2008-12-09 The Curators Of The University Of Missouri Electromagnetic emissions stimulation and detection system
US9721173B2 (en) * 2014-04-04 2017-08-01 Conduent Business Services, Llc Machine learning approach for detecting mobile phone usage by a driver
US10067221B2 (en) * 2015-04-06 2018-09-04 Texas Instruments Incorporated Interference detection in a frequency modulated continuous wave (FMCW) radar system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113644920A (zh) * 2021-06-16 2021-11-12 北京协同创新研究院 一种用于脑部成像的微波多路收发系统

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