CN113325008A - Non-contact material identification system and method based on WIFI equipment - Google Patents
Non-contact material identification system and method based on WIFI equipment Download PDFInfo
- Publication number
- CN113325008A CN113325008A CN202110612456.3A CN202110612456A CN113325008A CN 113325008 A CN113325008 A CN 113325008A CN 202110612456 A CN202110612456 A CN 202110612456A CN 113325008 A CN113325008 A CN 113325008A
- Authority
- CN
- China
- Prior art keywords
- signal
- identified
- csi
- wifi
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N22/00—Investigating or analysing materials by the use of microwaves or radio waves, i.e. electromagnetic waves with a wavelength of one millimetre or more
Landscapes
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Radar Systems Or Details Thereof (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention provides a non-contact material identification system based on WIFI equipment, wherein the WIFI equipment utilizes a transmitter to transmit a first signal through a transmitting antenna so as to construct a test field, and utilizes a receiver to receive the first signal after the first signal penetrates through a material to be identified through a first receiving antenna; the WIFI equipment also extracts amplitude parameters and phase parameters of the CSI based on changes caused by the first signal passing through the material to be identified and transmits the amplitude parameters and the phase parameters to the identification server; the identification server uses the CSI variation to determine a texture feature value unique to each texture type and independent of the target location, and identifies the material based on the feature value. The invention quantifies a characteristic value related to different signal attenuation caused by different materials, and can test the materials with low cost and high precision.
Description
Technical Field
The invention relates to the field of intelligent material inspection, in particular to a non-contact material identification system and method based on universal WIFI equipment.
Background
Material identification has wide application in commercial and industrial fields such as distinguishing water from salt water without tasting, analyzing explosion products after explosion, adjusting grip according to hardness of material, and the like. Conventional material identification methods include x-ray, ultrasound, and radio frequency. Generally, x-ray based methods and ultrasound based methods are similar in that they both require specialized hardware to transmit very high frequency signals. Despite their high recognition accuracy, the hardware required for these systems is expensive and heavy, making them difficult to fit in a commercial setting. In addition, the radioactivity of the x-rays can cause damage to the human body. Radio frequency based methods use signals such as RFID and UWB for material identification to achieve high accuracy. While they can overcome the large scale problem, it requires the use of expensive RFID reading equipment, which is often expensive to use in home and office environments. WiFi devices are increasingly being used because of their low cost and non-destructive advantages.
At present, there are identification solutions based on WiFi technology, and the main application scenarios can be divided into two categories: one is human and the other is for materials. In the former case, the main principle is that the signal will diffract when meeting human body, and non-contact behavior analysis is realized through signal processing and intelligent calculation. In the latter case, the key is that when the signal comes into contact with the material, it will be transmitted and discolored to varying degrees, such as the WiFi-based non-destructive and economical wheat moisture detection systems that have been proposed. However, the above method cannot accurately report the status of different objects and cannot be used for different body parts. Confusion may result due to its limited material quality on the surface of the object, and the analysis results are coarse-grained.
Therefore, how to realize material detection with lower cost, higher reliability and more intellectualization is a great problem which needs to be solved urgently at present.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a non-contact material identification system and a non-contact material identification method by using existing WiFi equipment, which can realize nondestructive passive material identification and can be applied to various use scenes needing material detection. The key implementation factor lies in that how the electromagnetic wave attenuates when penetrating different materials is described through a new theoretical model established by the application, so that the advantages of improving the accuracy, rich identification types, simple structure and the like of colleagues with low cost are realized.
In order to achieve the purpose of the invention, the invention provides a non-contact material identification system based on WIFI equipment, which is characterized in that:
a WIFI device based contactless material identification system, the system comprising one or more WIFI devices, and an identification server communicatively connected to the WIFI devices; the WIFI equipment transmits a first signal through a transmitting antenna by using a transmitter to construct a test field, and receives the first signal penetrating through a material to be identified through a first receiving antenna by using a receiver; wherein the material to be identified is located on a connection between the transmitting antenna (TX) and the first receiving antenna (RX1), the WIFI device further extracting amplitude parameters and phase parameters of the CSI based on a change caused by the first signal passing through the material to be identified, and transmitting to the identification server; the identification server determines a material characteristic value which is unique for each material type and is irrelevant to the target position by using the CSI change, and identifies the material according to the characteristic value;
the characteristic values are expressed as follows:
wherein the content of the first and second substances,
for amplitude attenuation during propagation of microwaves through a medium, KRD is the measured value of the phase change of the medium, D being equal to the material thickness.
Furthermore, the material to be identified is vertically placed on the transceiving antenna connecting line, so that the signal vertically penetrates through the surface of the material, and the distance from the material to be identified to the transmitting antenna is smaller than the distance from the material to the first receiving antenna.
In addition, the recognition server includes a data preprocessing module and a feature extraction module. The data pre-processing performed by the data pre-processing module includes outlier detection and substitution, noise cancellation to calibrate the captured CSI data.
Further, the feature extraction module comprises the following steps during feature extraction: carrying out grouping averaging on the amplitude and the phase difference of the preprocessed CSI data, and substituting the average value of each group into the characteristic value to calculate the known characteristic value in the group; and performing weighted calculation on each group of characteristic values to obtain final characteristic values.
The invention also provides a non-contact material identification method based on the WIFI equipment, which is characterized by comprising the following steps: the method comprises the following steps:
s1, the WIFI equipment transmits a first signal through a transmitting antenna by using a transmitter to construct a test field;
s2, the WIFI device receives the first signal penetrating through the material to be identified through a first receiving antenna by using a receiver, and receives the first signal bypassing the material to be identified through a second receiving antenna by using the receiver;
s3, extracting amplitude attenuation parameters and phase difference parameters of the CSI by the WIFI equipment based on changes caused by the first signal passing through the material to be identified, and transmitting the parameters to an identification server;
s4, the identification server determines a material characteristic value which is unique for each material type and is irrelevant to the target position by using CSI change, and identifies the material according to the characteristic value;
wherein the feature values are represented as follows:
wherein the content of the first and second substances,
for amplitude attenuation during propagation of microwaves through a medium, KRD is the measured value of the phase change of the medium, D being equal to the material thickness.
The invention has the following beneficial effects:
1. the WIFI signal channel information is used as a data source for identification, so that the WIFI signal channel information is harmless to a human body and has low requirements on equipment. The material can be rapidly tested, and the material can be tested in a wireless place even without being limited in a testing laboratory.
2. The invention creatively uses the WiFi equipment with low cost to realize the material identification independent of the position, mainly contributes to quantizing a characteristic value only related to different signal attenuation caused by different materials, and improves the identification precision by causing different signal attenuation depending on different materials.
And calculating characteristic values only related to the material according to the influence of the material on the amplitude and the phase of the signal, and realizing material identification by using the obtained characteristic values. The experimental result of the invention shows that the identification accuracy of the system to different places is 96.2%, and the system has robustness to different experimental environments.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a schematic diagram of an architecture of a verification system of a WIFI device-based contactless material identification system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the material types and corresponding 16 th sub-carrier data (at the middle position) according to an embodiment of the present invention;
fig. 3 is a schematic diagram of RX1 CSI data of all 30 subcarriers in the time domain according to an embodiment of the present invention, where (a) is an amplitude diagram and (b) is a phase difference diagram;
fig. 4 is a schematic system architecture diagram of a contactless material identification system based on a WIFI device according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the characteristic values of six materials at different locations according to one embodiment of the present invention;
fig. 6 is a schematic diagram of an antenna arrangement according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of the recognition accuracy of 6 materials at 5 positions according to an embodiment of the present invention
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
Before the present invention is proposed, the technical principle of the present invention is introduced, mainly as follows:
1. channel state information, CSI, for WIFI
The present invention is developed on the basis of WIFI devices, where WIFI standards such as 802.11n use orthogonal frequency division multiplexing for communication. Unlike RSS, which captures the superposition of multipath signals only, CSI displays fine-grained information such as different time delays, amplitude attenuations, and phases of multipath signals on each subcarrier when the signal is transmitted between each transmit and receive antenna pair. A number of (preferably 30) sub-carriers included in the CSI measurement value may be derived by the CSI tool. The channel frequency response can be generally expressed as:
H(f，t)＝Hl(f，t)+Hm(f，t). (1)
where H (f, t) represents the complex-valued channel frequency response of the CSI format, H1Is a signal transmitted over a line-of-sight (LOS) path, HmRepresenting the total effect of other multipath signals. H (f, t) can also be expressed by the following equation:
H(f，t)＝h(f，t)·ejθ (2)
wherein h is1(f, t) and θ represent the amplitude and phase of the transmission signal, respectively, so equation (1) can be rewritten as equation (3):
2. preliminary verification before the present invention is proposed
Fig. 1 shows the architecture of the authentication system of the present application, which consists of two commercially available Mini PCs and an Intel Network Interface Controller (NIC)5300, each with a 5300WiFi NIC. One PC is connected as a transmitter to one external antenna and the other PC is connected to two antennas as receivers. These antennas are mounted on a tripod. The sampling frequency was set to 1000 Hz. The test material for material identification consisted of a 100mm x 1mm piece of sheet, cardboard, nickel, wood chips, iron, titanium, as shown in fig. 2.
Preliminary verification experiments were conducted in a 8.3m x 10m lobby, as shown in fig. 1, with chairs, bookshelves, computer desks, sofas and other furniture. During the preliminary experiments, only experimenters were present in the room. Various materials were placed in the above environment and placed on a non-metallic platform between TX and RX 1. The platform was a 20cm x 20cm rack with a 20 second duration for each experiment. An example of the amplitude of the 16 th subcarrier of 8000 packets measured by different materials at an intermediate position between TX and RX1 is shown in fig. 2. From the rendering of the heat map it is clear that different materials have different effects on the CSI.
Further, the material was placed in the center of TX and RX1 (distance between TX and RX1 was 20 cm). Fig. 3 shows an example of the amplitude and phase difference of all 30 carrier carriers of RX1 in the time domain. It can be seen from fig. 3 that the amplitude and phase difference of the first 5 seconds fluctuate greatly. This is caused when the experimenter leaves the experimental environment after the experiment begins. After 5 seconds, the signal gradually settles and for each subcarrier, there is still some random noise in the data, and we use time averaging technique to smooth the noise. To ensure that the signal is stable and free from artifacts, the experiment uses CSI data for between 10 and 20 seconds. Preliminary studies have demonstrated that different materials have different effects on channel response data.
On the basis of the research, the invention further deepens and solves the problem of identification accuracy through model optimization. Finally, the system of the non-contact material identification system based on the WIFI equipment is designed and realized, and the schematic diagram of the system architecture is shown in fig. 4.
The system comprises one or more WIFI devices and an identification server in communication connection with the WIFI devices; the WIFI equipment utilizes a transmitter to transmit a first signal through a transmitting antenna so as to construct a test field, and utilizes a receiver to receive the first signal penetrating through a material to be identified through a first receiving antenna. The connection between the transmitting antenna TX and the first receiving antenna RX1, in which the material to be identified is located, may preferably be placed vertically on the transceiver antenna connection so that the signal is transmitted vertically through the surface of the material. The WIFI device also extracts the phase and amplitude of the CSI based on the change caused by the first signal passing through the material to be identified, and transmits the CSI to the identification server.
In order to obtain the change, optionally, the receiver receives the first signal without the identification material inserted through the first receiving antenna RX 1; or the receiver also receives said first signal not passing through the material to be identified through a second receiving antenna RX2 (see fig. 6). The transmitting antenna and the receiving antenna may belong to the same device, or may be respectively disposed on different devices.
When a target appears on the service level link between the transmitter and the receiver, the phase and amplitude of the CSI will change, and different materials can be identified through model training by using the change. To identify different textures, the identification server uses the CSI variation to create texture feature values that are unique to each texture type and independent of the target location.
The characteristic values are specified as follows:
the amplitude of the microwaves decreases as they propagate through the dissipative medium
At the same time, the phase change of the medium is measured as KRD, where D is the propagation distance, equal to the material thickness, KI, KR are calculated as follows:
where ω denotes the angular frequency of the wave, and μ 0 and ∈ 0 denote the permeability and permittivity, respectively, under vacuum. Epsilonr and sigma are the relative permittivity of the medium and the conductivity of the medium, respectively. KI and KR are only relevant to these materials.
When a material to be identified is placed on the service level path between TX and RX1, (3) may be rewritten as follows:
wherein, h'l(f, t) and θ'1Representing a signal that is not affected by the material.
In WiMate, the clocks of the transmitter and receiver are not synchronized. However, the sampling clocks of the different antennas at the receiving end are the same. To solve the problem of transmitter and receiver clock asynchronism, all channel response data is multiplied by a coefficient
This is equivalent to phase data, making the phase of R2 zero. After the phases are over, all phases are synchronized to R1. Phase difference
Indicating a phase change effected by the material. Therefore, equation (6) can be rewritten as follows:
wherein the content of the first and second substances,
multiplying the transmitted signal using the channel frequency response in the frequency domain may also represent the received signal:
R(f)＝S(f)·H(f) (8)
thus, it can be seen that:
in conjunction with equations (9) (11), equation (6) can be rewritten as:
the left side of the yielding program is ζ,
it is clear that ζ is a variable that is related only to the material itself, i.e. the characteristic value can be obtained.
An example of the calculation of the feature value is as follows:
1) first, a correlation coefficient is obtained:
in formula (13)
And
a fixed value on the premise that the environment remains unchanged.
May be extracted from the measured CSI data; for the
And
fitting known KI and KR materials by substituting them into (13).
As an example, with alcohol as a known material, the relative dielectric constant and conductivity data for various concentrations of aqueous alcohol solutions are shown in Table 1, and these data are measured by professional hardware at a frequency of 5.32 GHz. The test material comprises a 210mm x 148mm x 2mm storage tank and a 0% -90% concentration 10% interval aqueous alcohol solution. A storage tank filled with alcohol/water solution with different concentrations is placed in the center of TX/RX 1. The present application uses only the average amplitude and average phase of the 16 th subcarrier difference because the CSI data of the 16 th subcarrier is closer to the center frequency of 5.32 GHz. The present application then performs a least squares fit on the ten sets of data to calculate the correlation coefficients.
TABLE 1
2) Obtaining characteristic values of different materials:
the identification server comprises a data preprocessing module and a feature extraction module. Wherein the data pre-processing module includes outlier detection and noise cancellation to calibrate the captured CSI data.
Outlier detection includes: there are typically some outliers in the captured CSI amplitude and phase difference tracking, and anomaly detection is performed to detect bad data values that should be replaced from the original CSI data. The noise cancellation includes: the raw channel data may contain anomalous samples caused by background noise or hardware faults, and therefore, a filter, such as a butterworth filter, may be selected to further remove ambient noise before applying the feature extraction technique.
Preferably, the sampling rate of the CSI is Fs 1000 samples/s, and the cut-off frequency is
Since there is an unstable situation in the initial stage of reception (for example, within the first 5 seconds of reception), the data used for the data preprocessing and the feature extraction does not include the data in the initial stage of reception.
The feature extraction module comprises the following steps during feature extraction: dividing amplitude and phase difference data of the preprocessed CSI data into 1000 data packets in each group, and calculating an average value in each group as an amplitude and phase difference value in each group; secondly, substituting the average value in the group into a formula (14) to calculate the characteristic value in the group; and thirdly, weighting and calculating each group of characteristic values to obtain a comprehensive characteristic value.
The invention further adopts six materials in the preliminary verification experiment to verify the result. Six of them are placed at distances of 4 cm, 8 cm, 10 cm, 12 cm and 16cm from TX, respectively, and the calculation results are shown in the figure. As can be seen from fig. 5, the characteristic values of different materials are different, and the characteristic values of the same material are slightly different at different positions. In fig. 5, solid lines and broken lines represent non-metallic materials and metallic materials, respectively. The difference between these two types is very significant. Can know through the experiment, the eigenvalue that this application provided can better distinguish the material of different grade type, can also further promote the accuracy if control position variable is fixed.
Further, as an example, fig. 6 shows an antenna arrangement diagram of the present invention. This is achieved by using off-the-shelf WIFI devices, two WIFI devices being used as transmitting and receiving devices, respectively, the transmitter having one antenna TX and the receiver having two antennas RX1, RX 2. The distance between the transmitting antenna and the receiving antenna was 20cm, and they were both horizontally placed above the floor. The material to be tested is placed in the service level path of TX and RX1, with the path of TX1 and RX2 bypassing the material to be tested.
The present invention also evaluates the performance of the system by setting the transmission speed of the transmission device to 1000 packets per second, performing 30 second experiments on 6 different signature materials (same as the previous settings) at 5 different locations, while the first 5 seconds of the 30 seconds will not be used on the experimental data. Since the experimental material was static, the eigenvalues of the material were chosen to be calculated as a group for every 1000 data packets.
The method collects 6 (material quantity) multiplied by 5 (position number) multiplied by 25 (experimental group) data files, then uses a Support Vector Machine (SVM) to classify the processed CSI data, randomly divides the data into two groups to train and test, and observes the classification effect at different positions. Preferably, using 750 sets of experimental data collected, 80% are randomly selected as the training set and 20% as the test set. The final result is shown in fig. 7. Fig. 7 shows the recognition accuracy of 6 materials at 5 locations, with the X-axis representing the distance from the material to TX and the Y-axis representing the accuracy. The experimental results show that an average resolution of 96.2% is obtained over 6 materials at 5 locations, where the recognition accuracy of the material is the lowest, only 90%, at 16cm, but as high as 100% elsewhere. Therefore, the material to be measured cannot be too far from the transmitting antenna. In theory, the system of the present invention can accurately identify the material at any position of the service level, but in order to overcome the environmental deviation, it is preferable to set the distance of the material to be measured from the transmitting antenna to be smaller than the distance of the material to be measured from the receiving antenna.
The method creatively sets the characteristic value for the material, can realize the nondestructive detection of the material based on mature and cheap wireless WIFI equipment, improves the accuracy of an analysis result, and solves the defects in the prior art. The method has high feasibility, detects the signal attenuation caused by the material by using the CSI extracted from the WiFi physical layer, calculates a characteristic value which is only related to the material and is not related to the position, finally realizes the identification of different materials by using the WiFi signal, and has accurate material identification performance.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the scope of the present invention should be determined by the following claims.
Claims (10)
1. A non-contact material identification system based on WIFI equipment is characterized in that: the system comprises one or more WIFI devices and an identification server in communication connection with the WIFI devices;
the WIFI equipment transmits a first signal through a transmitting antenna by using a transmitter to construct a test field, and receives the first signal penetrating through a material to be identified through a first receiving antenna by using a receiver;
wherein the material to be identified is located on a connection between the transmitting antenna (TX) and the first receiving antenna (RX1), the WIFI device further extracting amplitude parameters and phase parameters of the CSI based on a change caused by the first signal passing through the material to be identified, and transmitting to the identification server;
the identification server determines a material characteristic value which is unique for each material type and is irrelevant to the target position by using the CSI change, and identifies the material according to the characteristic value;
the characteristic values are expressed as follows:
wherein the content of the first and second substances,
for amplitude attenuation during propagation of microwaves through a medium, KRD is the measured value of the phase change of the medium, D being equal to the material thickness.
2. The system of claim 1, wherein the material to be identified is placed vertically on the transceiver antenna wire such that the signal is transmitted vertically through the surface of the material, and wherein the distance of the material to be identified from the transmitting antenna is less than the distance of the material to the first receiving antenna.
3. The system of claim 1, wherein the recognition server comprises a data pre-processing module and a feature extraction module.
4. The system of claim 3, wherein the data pre-processing performed by the data pre-processing module includes outlier detection and substitution, noise cancellation to calibrate the captured CSI data.
5. The system of claim 3, wherein the feature extraction module comprises the following steps in feature extraction: carrying out grouping averaging on the amplitude and the phase difference of the preprocessed CSI data, and substituting the average value of each group into the characteristic value to calculate the known characteristic value in the group; and performing weighted calculation on each group of characteristic values to obtain final characteristic values.
6. A non-contact material identification method based on WIFI equipment is characterized by comprising the following steps: the method comprises the following steps:
s1, the WIFI equipment transmits a first signal through a transmitting antenna by using a transmitter to construct a test field;
s2, the WIFI device receives the first signal penetrating through the material to be identified through a first receiving antenna by using a receiver, and receives the first signal bypassing the material to be identified through a second receiving antenna by using the receiver;
s3, extracting amplitude attenuation parameters and phase difference parameters of the CSI by the WIFI equipment based on changes caused by the first signal passing through the material to be identified, and transmitting the parameters to an identification server;
s4, the identification server determines a material characteristic value which is unique for each material type and is irrelevant to the target position by using CSI change, and identifies the material according to the characteristic value;
wherein the feature values are represented as follows:
wherein the content of the first and second substances,
for amplitude attenuation during propagation of microwaves through a medium, KRD is the measured value of the phase change of the medium, D being equal to the material thickness.
7. The method of claim 6, wherein the material to be identified is placed vertically on the transceiver antenna wire such that the signal is transmitted vertically through the surface of the material, and wherein the distance of the material to be identified from the transmitting antenna is less than the distance of the material to the first receiving antenna.
8. The method of claim 6, wherein the recognition server comprises a data pre-processing module and a feature extraction module.
9. The method of claim 8, wherein data pre-processing performed by the data pre-processing module includes outlier detection and substitution, noise cancellation to calibrate the captured CSI data.
10. The method of claim 8, wherein the feature extraction module comprises the following steps in the feature extraction: carrying out grouping averaging on the amplitude and the phase difference of the preprocessed CSI data, and substituting the average value of each group into the characteristic value to calculate the known characteristic value in the group; and performing weighted calculation on each group of characteristic values to obtain final characteristic values.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110612456.3A CN113325008B (en) | 2021-06-02 | 2021-06-02 | Non-contact material identification system and method based on WIFI equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110612456.3A CN113325008B (en) | 2021-06-02 | 2021-06-02 | Non-contact material identification system and method based on WIFI equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113325008A true CN113325008A (en) | 2021-08-31 |
| CN113325008B CN113325008B (en) | 2022-09-20 |
Family
ID=77423168
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110612456.3A Active CN113325008B (en) | 2021-06-02 | 2021-06-02 | Non-contact material identification system and method based on WIFI equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113325008B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2388453A1 (en) * | 2002-05-31 | 2003-11-30 | Precarn Incorporated | Method and apparatus to evaluate dielectrically-anisotropic materials using analysis of multiple microwave signals in different planes of polarization |
| WO2011050959A1 (en) * | 2009-10-27 | 2011-05-05 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Method and device for identifying a material |
| CN204008502U (en) * | 2014-04-09 | 2014-12-10 | 北京辰安测控科技有限公司 | Measure the device of material moisture |
| CN105469079A (en) * | 2015-12-31 | 2016-04-06 | 中国科学院上海高等研究院 | Object material identification method based on multi-sensor information fusion |
| CN105527303A (en) * | 2015-12-04 | 2016-04-27 | 深圳大学 | Method and system for detecting material by using millimeter waves |
| US20160178537A1 (en) * | 2014-12-22 | 2016-06-23 | Texas Instruments Incorporated | Material determination by sweeping a range of frequencies |
| CN107238611A (en) * | 2017-06-05 | 2017-10-10 | 默斯测控技术（长沙）有限公司 | Analysis of moisture content method, device and drimeter and measuring method |
| CN108903951A (en) * | 2018-05-28 | 2018-11-30 | 合肥工业大学 | A method of micro- deformation real-time monitoring assessment based on WiFi signal |
| US20200110170A1 (en) * | 2018-10-08 | 2020-04-09 | Microsoft Technology Licensing, Llc | Soil measurement system using wireless signals |
| GB202100037D0 (en) * | 2020-03-06 | 2021-02-17 | Johnson Matthey Plc | Level measurement apparatus |
| US20210063322A1 (en) * | 2019-09-03 | 2021-03-04 | Henan University Of Technology | Grain mildew detection method and device based on wifi apparatus |
-
2021
- 2021-06-02 CN CN202110612456.3A patent/CN113325008B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2388453A1 (en) * | 2002-05-31 | 2003-11-30 | Precarn Incorporated | Method and apparatus to evaluate dielectrically-anisotropic materials using analysis of multiple microwave signals in different planes of polarization |
| WO2011050959A1 (en) * | 2009-10-27 | 2011-05-05 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Method and device for identifying a material |
| CN204008502U (en) * | 2014-04-09 | 2014-12-10 | 北京辰安测控科技有限公司 | Measure the device of material moisture |
| US20160178537A1 (en) * | 2014-12-22 | 2016-06-23 | Texas Instruments Incorporated | Material determination by sweeping a range of frequencies |
| CN105527303A (en) * | 2015-12-04 | 2016-04-27 | 深圳大学 | Method and system for detecting material by using millimeter waves |
| CN105469079A (en) * | 2015-12-31 | 2016-04-06 | 中国科学院上海高等研究院 | Object material identification method based on multi-sensor information fusion |
| CN107238611A (en) * | 2017-06-05 | 2017-10-10 | 默斯测控技术（长沙）有限公司 | Analysis of moisture content method, device and drimeter and measuring method |
| CN108903951A (en) * | 2018-05-28 | 2018-11-30 | 合肥工业大学 | A method of micro- deformation real-time monitoring assessment based on WiFi signal |
| US20200110170A1 (en) * | 2018-10-08 | 2020-04-09 | Microsoft Technology Licensing, Llc | Soil measurement system using wireless signals |
| US20210063322A1 (en) * | 2019-09-03 | 2021-03-04 | Henan University Of Technology | Grain mildew detection method and device based on wifi apparatus |
| GB202100037D0 (en) * | 2020-03-06 | 2021-02-17 | Johnson Matthey Plc | Level measurement apparatus |
Non-Patent Citations (4)
| Title |
|---|
| BAO ZHOU 等: "Detection of Suspicious Objects Concealed by Walking Pedestrians Using WiFi", 《2020 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)》 * |
| CHEN WANG 等: "Towards In-baggage Suspicious Object Detection Using Commodity WiFi", 《2018 IEEE CONFERENCE ON COMMUNICATIONS AND NETWORK SECURITY (CNS)》 * |
| 朱海等: "基于信道状态信息的WiFi环境感知技术", 《南京邮电大学学报(自然科学版)》 * |
| 郑国军等: "基于无线背景信号的人体行为识别研究", 《蚌埠学院学报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113325008B (en) | 2022-09-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105527303B (en) | Utilize the method and system of millimeter wave detection material | |
| CN103596266B (en) | A kind of human testing and method, the apparatus and system of positioning | |
| Feng et al. | WiMi: Target material identification with commodity Wi-Fi devices | |
| CN104883732B (en) | A kind of enhanced indoor passive passive human body localization method | |
| CN107241698B (en) | Non-contact perception tracking method | |
| CN105652273B (en) | A kind of sparse imaging algorithm of MIMO radar based on mixing matching pursuit algorithm | |
| CN106019214B (en) | Wide-band coherent signal source DOA estimation method | |
| CN106443604B (en) | The method of calibration of the blind handling result of LFM/BPSK Hybrid-modulated Signals | |
| CN109738861A (en) | A kind of three-dimensional combined estimation method based on Wi-Fi channel state information | |
| CN110475221A (en) | A kind of personnel's action recognition and location estimation method based on channel state information | |
| WO2019154992A1 (en) | Position estimation device and communication device | |
| CN104243065B (en) | The method and apparatus of standing-wave ratio detection | |
| CN112040400B (en) | Single-site indoor fingerprint positioning method based on MIMO-CSI, storage medium and equipment | |
| JP2019148428A (en) | Human detection device and human detection method | |
| CN108363038A (en) | Alignment sensor, sensor and method | |
| WO2014206260A1 (en) | Blocking index test method, device and system | |
| CN109581303B (en) | Interference suppression method based on Wi-Fi through-wall radar | |
| CN113325008B (en) | Non-contact material identification system and method based on WIFI equipment | |
| CN111007456A (en) | Robust non-line-of-sight deviation elimination positioning method capable of realizing time domain combination | |
| CN109839543B (en) | System and method for testing amplitude-phase consistency of antenna | |
| CN112315452B (en) | Human body respiration tracking method, device and system based on multipath phase cancellation | |
| Boudlal et al. | A Monitoring System for Elderly People Using WiFi Sensing with Channel State Information. | |
| CN109829416A (en) | A kind of method and system detecting ultrahigh frequency partial discharge signal from random noise | |
| Wang et al. | Indoor fingerprinting localization based on fine-grained CSI using principal component analysis | |
| Hiron et al. | Investigation of Wireless Communication from Under Seawater to Open Air with Xbee Pro S2B Based on IEEE 802.15. 4 (Case Study: West Java Pangandaran Offshore Indonesia) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |