CN112859071A - Hidden fatal injury detection method and detection system based on UWB (ultra wide band) biological radar - Google Patents
Hidden fatal injury detection method and detection system based on UWB (ultra wide band) biological radar Download PDFInfo
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Abstract
The invention discloses a hidden fatal flaw detection method and a detection system based on a UWB (ultra wide band) biological radar, belonging to the technical field of biological radars and comprising the following steps: collecting two groups of UWB biological radar echo data which are symmetrical left and right of a physiological anatomical structure of a target object to be detected; sequentially carrying out normalization processing, discrete cosine transform processing and threshold operation processing on the two groups of collected UWB biological radar echo data to obtain two groups of characteristic value data which can be used for linear discriminant analysis; and judging the injury after carrying out linear discrimination analysis on the two groups of obtained characteristic value data, judging no injury if the two groups of characteristic value data are linearly inseparable, and judging the injury if the two groups of characteristic value data are linearly inseparable. The invention utilizes the symmetrical characteristic of the human body planning and removing structure to measure, process and analyze data, realizes the automatic discrimination of injury information such as an air layer, a bleeding point and the like, and can quickly and conveniently realize the detection of fatal hidden injury.
Description
Technical Field
The invention belongs to the technical field of biological radars, and particularly relates to a hidden fatal flaw detection method and a detection system based on a UWB biological radar.
Background
The pre-hospital emergency treatment process is limited by personnel, time and conditions, and some hidden fatal injuries such as tension pneumothorax, internal craniocerebral hemorrhage and the like cannot be timely found and correctly diagnosed only by symptoms and physical signs of the injured person, so that diagnosis and treatment are delayed and casualties are caused. However, conventional clinical diagnostic techniques and equipment such as ultrasound, X-ray, CT, MRI, etc. are difficult to apply due to their combined limitations of volume, weight, cost, and use by professionals.
The biological radar is a new concept radar technology which mainly takes a human body as a detection object, takes electromagnetic waves as a detection medium, can penetrate through nonmetallic media such as ruins, walls, clothes, human tissues and the like, senses information such as respiration, heartbeat, body movement, images and the like of the human body, has the characteristics of non-contact, penetrability and multiple information, and has wide application prospect in the fields of military affairs, medicine, public safety and the like. Compared with the conventional clinical diagnosis technologies such as ultrasound, CT, MRI and the like, the method has the comprehensive advantages of low cost, small volume, light weight, high speed and no need of professional operation and result interpretation, and does not need to directly contact the skin of a human body or paint a coupling agent during detection, thereby being a most advantageous technical means for detecting the occult fatal injuries in the pre-hospital emergency environment at the present stage.
The existing biological radar adopts an imaging method when used for detecting human injuries, is difficult to be put into practical use in a short period and is also easily subjected to comprehensive limitations of volume, weight, cost and the like due to the need of complex multi-channel transceiving, antenna arrays and imaging algorithms, and is not a feasible way for realizing the detection of the hidden fatal injuries.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a hidden casualty detection method and a detection system based on UWB (ultra Wide band) biological radar, which can realize the rapid automatic detection of the hidden casualty.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
the invention discloses a hidden fatal flaw detection method based on a UWB (ultra Wide band) biological radar, which comprises the following steps:
collecting two groups of UWB biological radar echo data which are symmetrical left and right of a physiological anatomical structure of a target object to be detected;
sequentially carrying out normalization processing, discrete cosine transform processing and threshold operation processing on the two groups of collected UWB biological radar echo data to obtain two groups of characteristic value data which can be used for linear discriminant analysis;
and judging the injury after carrying out linear discrimination analysis on the two groups of obtained characteristic value data, judging no injury if the two groups of characteristic value data are linearly inseparable, and judging the injury if the two groups of characteristic value data are linearly inseparable.
Preferably, when UWB biological radar echo data are collected, the distance between the UWB biological radar echo data and the body surface of the target object to be measured is 1-2 cm or the UWB biological radar echo data directly contact the target object to be measured; setting the initial distance to be 0ns, and setting the time window to be 5-10 ns; each set of data acquisition times >10 s.
Preferably, the normalization process is to change the maximum value of each group of UWB biological radar echo data at different time into 1, and the UWB biological radar echo data contains two-dimensional information of distance m and time n, which is recorded as ri[m,n]Wherein, i is 1,2 represents two groups of left and right data, M is more than or equal to 1 and less than or equal to M represents distance, N is more than or equal to 1 and less than or equal to N represents time, M represents the number of points of echo data distance dimension, M is more than or equal to 512, N represents the number of points of echo data time dimension, N is more than or equal to data acquisition time multiplied by sampling rate, then the data after normalization processing is:
therein, maxmri[m,n]Taking the maximum value in the distance dimension.
Preferably, the discrete cosine transform processing refers to normalizing the processed data
Processing in the distance dimension, discrete cosine Ri[m,n]：
Wherein the content of the first and second substances,
or
Further preferably, the threshold operation processing is to select a data feature value after discrete cosine transform processing, and then there is a threshold R'i[m,n]：
R′i[m,n]＝Ri[m,n] 2≤m≤M′ (3)
Wherein M ' < M, M ' represents p-R 'i[m,n]The thresholding is performed on the distance dimension.
Preferably, the linear discriminant analysis includes:
designing a binary classification problem, and finding a group of projection coefficients w according to the principle that the intra-class variance is minimum and the inter-class variance is maximum after projection so as to maximize the generalized Rayleigh entropy of the two groups of processed characteristic value data:
wherein S iswFor classes of two sets of characteristic value dataInternal divergence matrix, SbAn inter-class divergence matrix for two sets of eigenvalue data [ ·]TIndicating transposition, then performing projective transformation on the two sets of characteristic value data by using w, and projecting the transformed data R ″i：
R″i＝wTR′i[m,n] (5)
And (3) measuring the distance between the two groups of characteristic value data after projection by adopting the Mahalanobis distance:
therein, sigma-1Is R1And R ″)2Represents [ · of]-1Inversion is carried out, and finally a threshold value is set for judgment, when D isMAnd when the value is more than or equal to 1, the two groups of characteristic value data are considered to be linearly separable, and the occurrence of injury is judged, otherwise, the judgment is normal.
The invention also discloses a detection system for realizing the detection method of the latent fatal injury based on the UWB biological radar, which comprises the following steps:
the UWB biological radar echo data acquisition module is used for acquiring two groups of UWB biological radar echo data which are symmetrical left and right of the physiological anatomical structure of the target object to be detected;
the normalization processing module is used for performing normalization processing on the two groups of collected UWB biological radar echo data;
the discrete cosine transform processing module is used for carrying out discrete cosine transform processing on the data after the normalization processing;
the threshold operation selection module is used for selecting threshold operation for the data after discrete cosine transform processing;
and the linear discriminant analysis module is used for performing linear discriminant analysis on the two groups of characteristic value data selected by the threshold operation.
Preferably, the UWB biological radar echo data acquisition module comprises a pulse generator, a transmitter, a transceiving antenna, a sequential logic unit, a control unit and a receiver; wherein:
the impulse generator generates impulse with a certain pulse repetition frequency, the impulse is sent to the transmitter for shaping and then radiated out through the transmitting and receiving antenna, meanwhile, the impulse generated by the impulse generator is sent to the time sequence logic unit, a distance gate with controllable delay time is generated under the control of the control unit, and the receiver is triggered to sample the echo signal of the UWB biological radar.
Preferably, the device also comprises an analog-to-digital conversion module and a control display unit;
the analog-to-digital conversion module is used for converting the collected UWB biological radar echo signals and then sending the converted UWB biological radar echo signals to the control display unit;
and the control display unit is used for setting system parameters of the UWB biological radar, periodically controlling the delay time of the range gate and realizing scanning detection in a set range.
Preferably, the set distance range is determined by two parameters, namely a starting distance and a time window, and corresponds to a fan-shaped area in a two-dimensional plane.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a hidden fatal damage detection method based on UWB (ultra wide band) biological radar, which comprises the steps of firstly collecting two symmetrical groups of UWB biological radar echo data of an anatomical structure of a target to be detected (human tissue), then sequentially carrying out normalization processing, discrete cosine transform processing and threshold operation processing on the two groups of echo data, and analyzing the two groups of processed data by adopting LDA (laser direct structuring) to judge the damage. The innovation of the invention is that the symmetrical characteristic of the human body debriding structure is utilized to measure, process and analyze data, and the automatic discrimination of injury information such as an air layer, a bleeding point and the like is realized, so that the tension pneumothorax and the internal craniocerebral bleeding can be detected only by a single-channel system, and the detection of fatal occult injury can be quickly and conveniently realized.
The invention also discloses a detection system for realizing the detection method, which comprises a UWB biological radar echo data acquisition module, a data processing module (a normalization processing module, a discrete cosine transform processing module and a threshold operation selection module) and a linear discriminant analysis module, and can form a truly portable and low-cost intelligent detection device for detecting the occult fatal injury in pre-hospital first aid.
Furthermore, the UWB biological radar echo data acquisition module adopts an impulse system, the center frequency is controlled to be 3-5 GHz, the system bandwidth is more than 1GHz, the penetration capability to human tissues and the detection capability to injury information such as air layers and bleeding points can be ensured, the system is adopted to respectively acquire two groups of data at the symmetrical positions of the left side and the right side of a human body, the system antenna can be 1-2 cm away from the body surface of the human body or directly contact the human body during acquisition, and the operation is convenient and simple.
Drawings
FIG. 1 is a schematic block diagram of a UWB bio-radar system;
FIG. 2 is a schematic diagram of the symmetry of human anatomy at data acquisition;
FIG. 3 is a flow chart of a process for UWB biological radar echo data;
FIG. 4 LDA-based data analysis flow;
figure 5 is a top view of a phantom for a tension pneumothorax;
FIG. 6 phantom experimental results of a tension pneumothorax test; wherein, (a), (b) and (c) respectively correspond to two groups of data after LDA projection under the conditions of normal human body, left-side tension pneumothorax and double-side tension pneumothorax;
fig. 7 is a flow chart of a hidden fatal injury detection method based on UWB biological radar.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 7, the method for detecting occult fatal injury based on UWB biological radar of the present invention includes:
collecting two groups of UWB biological radar echo data which are symmetrical left and right of a physiological anatomical structure of a target object to be detected;
sequentially carrying out normalization processing, discrete cosine transform processing and threshold operation processing on the two groups of collected UWB biological radar echo data to obtain two groups of characteristic value data which can be used for linear discriminant analysis;
and judging the injury after carrying out linear discrimination analysis on the two groups of obtained characteristic value data, judging no injury if the two groups of characteristic value data are linearly inseparable, and judging the injury if the two groups of characteristic value data are linearly inseparable.
A detection device for realizing the detection method comprises the following steps:
the UWB biological radar echo data acquisition module is used for acquiring two groups of UWB biological radar echo data which are symmetrical left and right of the physiological anatomical structure of the target object to be detected;
the normalization processing module is used for performing normalization processing on the two groups of collected UWB biological radar echo data;
the discrete cosine transform processing module is used for carrying out discrete cosine transform processing on the data after the normalization processing;
the threshold operation selection module is used for selecting threshold operation for the data after discrete cosine transform processing;
and the linear discriminant analysis module is used for performing linear discriminant analysis on the two groups of characteristic value data selected by the threshold operation.
The detection system and the detection method of the present invention will be described in detail below with reference to the following specific drawings:
referring to fig. 1, a schematic block diagram of a UWB bio-radar system is shown. The system adopts an impulse pulse system, the central frequency is controlled to be 3-5 GHz, and the system bandwidth is more than 1 GHz. As shown in the figure, the pulse generator generates impulse with a certain pulse repetition frequency, and the impulse is sent to the transmitter for shaping and then radiated by the transceiving antenna. The receiving and transmitting antenna comprises 1 transmitting antenna and 1 receiving antenna, and butterfly dipole directional antennas are adopted. Meanwhile, the pulse generated by the pulse generator is sent to the sequential logic unit, and a distance gate with controllable delay time is generated under the control of the control unit, so that the receiver is triggered to sample the echo signal. The range gate is delayed relative to the transmitted pulse, i.e., the pulse travels in both passes between the system and the target. The radial distance of the target relative to the radar can be obtained by obtaining the travel time. Therefore, the UWB sodar echo signal is a two-dimensional echo signal containing time and distance information. The sampled echo signals are acquired at high speed by an ADC (Analog Digital Converter) and then sent to a control display unit. The control and display unit processes and analyzes the echo data, detects information such as an air layer and a bleeding point contained in the echo data, and judges the hiding fatal injury on the basis of the information. Meanwhile, the control display unit can set system parameters of the UWB biological radar, control the working state of the radar host, namely periodically control the delay time of the range gate, and realize scanning detection in a set range. The distance range is determined by two parameters of the starting distance and the time window and corresponds to a fan-shaped area in a two-dimensional plane.
Referring to fig. 2, a diagram of the symmetry of human anatomy at data acquisition is shown. As shown, the body is divided into left and right sides by a vertically extending line of the midline of the sternum of the body. According to the division, tissues such as human skin, fat, muscle, bone and the like and parts of organs such as brain, lung and the like are bilaterally symmetrical. When the UWB biological radar echo data acquisition module is used for data acquisition, a group of data is respectively acquired at symmetrical positions on the left side and the right side (such as temples of the head, nipples of the chest and the like). The receiving and transmitting antenna can be 1-2 cm away from the body surface of a human body or directly contact the human body during collection; setting the initial distance to be 0ns, and setting the time window to be 5-10 ns; each set of data acquisition times >10s during which the antenna is held stationary.
Referring to fig. 3, a flow chart of UWB biological radar echo data processing is shown. As shown in the figure, first, two groups of UWB biological radar echo data are normalized, that is, the maximum value of each group of UWB biological radar echo data at different time is changed to 1. As mentioned above, UWB biological radar echo data includes two-dimensional distance and time information, which is recorded as ri[m,n]Where i ≦ 1,2 denotes left and right side data, 1 ≦ M denotes a distance, and 1 ≦ N denotes a time. The normalized data becomes:
therein, maxmri[m,n]Taking the maximum value in the distance dimension. Then the normalized data is compared
DCT (Discrete Cosine Transform) is performed in the distance dimension:
wherein the content of the first and second substances,
or
Because the energy of the data after DCT is mainly concentrated in the low-frequency part, the data is selected as the characteristic value of the subsequent LDA analysis by adopting threshold operation, namely:
R′i[m,n]＝Ri[m,n] 2≤m≤M′ (3)
wherein M' < M.
The LDA-based data analysis method is explained as follows:
fig. 4 is a flow of LDA-based data analysis. As shown in the figure, firstly, a classification problem is designed, and a group of projection coefficients w is found according to the principle that the intra-class variance is minimum and the inter-class variance is maximum after projection so that the generalized rayleigh entropy of the two groups of processed UWB biological radar echo data is maximized:
wherein S iswAn intra-class divergence matrix, S, for two sets of databAn inter-class divergence matrix for two sets of data [ ·]TIndicating transposition. Then, two sets of data are projectively transformed using w:
R″i＝wTR′i[m,n] (5)
next, Mahalanobis Distance (Mahalanobis Distance) was used to determine the "Distance" between the two sets of data after projection:
therein, sigma-1Is R1And R ″)2Represents [ · of]-1And (6) inversion. Finally setting a threshold value for judgment, and judging when D isMWhen the data is more than or equal to 1, the two groups of data are considered to be linearly separable, so that the judgment is madeAnd if the condition is broken, the condition is normal.
Experiments were designed to evaluate the efficacy of the method of the invention:
figure 5 is a phantom top view of a tension pneumothorax used in the experiment. As shown in the figure, the phantom is made of polymethyl methacrylate material, an elliptic cylindrical container with the outer part of 33 multiplied by 25cm simulates the chest of a human body, two symmetrical cylindrical containers with the diameter of 11cm simulate the double lungs of the human body are arranged in the middle, the wall thickness of each container is 5mm, and the height of each container is 30 cm. Two groups of data are respectively collected for processing and analysis according to the following three conditions in the experimental process: 1) the mixed solution of purified water and ethanol, namely a normal human body, is injected into the three containers; 2) a small balloon is pasted on the outer side of the left cylindrical container to introduce air, namely the left tensile pneumothorax; 3) the small balloons are adhered to the outer sides of the cylindrical containers at the left side and the right side to introduce air, namely the bilateral tension pneumothorax. During data acquisition, the UWB biological radar antenna is over against the horizontal center line of the phantom and the focus outside the elliptic cylinder container, and the height is 15cm of the phantom.
FIG. 6 shows the phantom experimental results of the tension pneumothorax detection, in which (a), (b) and (c) correspond to two sets of data R' after LDA projection under the conditions of normal human body, left tension pneumothorax and double tension pneumothorax respectivelyi. As shown, the two sets of data are linearly inseparable in the normal human case, whereas in the left and double-sided tension pneumothorax cases, the introduction of air breaks the symmetry of the phantom resulting in the two sets of data being linearly separable. And finally, respectively calculating the Mahalanobis distances of 0.3020, 1.4475 and 1.0694 in three cases, and judging that the obtained detection result is consistent with the actual detection result according to the set threshold value.
In summary, the invention mainly utilizes the symmetry that the occurrence of injuries such as tension pneumothorax, internal craniocerebral hemorrhage and the like can damage the human body structure to realize the rapid automatic detection of fatal hidden injuries, the method comprises three steps of data acquisition, data processing and data analysis, and finally, the results of whether injuries exist or not are output: the data acquisition uses the UWB biological radar of impulse system, divide the human body into the left and right sides with the vertical extension line of the median line of human sternum, then collect a series of data in the symmetrical position of both sides (such as temple of the head, nipple of the chest, etc.); the data processing carries out operations such as normalization, DCT, threshold and the like on the collected data to generate a characteristic value for analysis; the data analysis is mainly based on LDA to calculate the distance between the projections after secondary classification, and threshold value judgment is carried out to output the result of whether the condition is damaged or not.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (10)
1. A hidden fatal injury detection method based on UWB (ultra Wide band) biological radar is characterized by comprising the following steps:
collecting two groups of UWB biological radar echo data which are symmetrical left and right of a physiological anatomical structure of a target object to be detected;
sequentially carrying out normalization processing, discrete cosine transform processing and threshold operation processing on the two groups of collected UWB biological radar echo data to obtain two groups of characteristic value data which can be used for linear discriminant analysis;
and judging the injury after carrying out linear discrimination analysis on the two groups of obtained characteristic value data, judging no injury if the two groups of characteristic value data are linearly inseparable, and judging the injury if the two groups of characteristic value data are linearly inseparable.
2. The UWB biological radar-based hiding fatal flaw detection method according to claim 1, wherein when UWB biological radar echo data are collected, the UWB biological radar echo data are 1-2 cm away from the body surface of a target object to be detected or directly contact the target object to be detected; setting the initial distance to be 0ns, and setting the time window to be 5-10 ns; the data acquisition time of each group is more than 10 s.
3. The method as claimed in claim 1, wherein the normalization process is performed to change the maximum value of each group of UWB biological radar echo data at different times to 1, and the UWB biological radar echo data includes two-dimensional information of distance m and time nIs denoted by ri[m，n]Wherein, i is 1,2 represents two groups of left and right data, M is more than or equal to 1 and less than or equal to M represents distance, N is more than or equal to 1 and less than or equal to N represents time, M represents the number of points of echo data distance dimension, M is more than or equal to 512, N represents the number of points of echo data time dimension, N is more than or equal to data acquisition time multiplied by sampling rate, then the data after normalization processing is:
therein, maxmri[m，n]Taking the maximum value in the distance dimension.
4. The UWB biological radar-based covert fatal lesion detection method of claim 3, wherein the discrete cosine transform processing refers to normalization processed data
Processing in the distance dimension, discrete cosine Ri[m，n]：
Wherein the content of the first and second substances,
or
5. The UWB biological radar-based covert fatal lesion detection method of claim 4, wherein the threshold operation processing is to select a data feature value after discrete cosine transform processing, and then has a threshold R'i[m，n]：
R′i[m，n]＝Ri[m，n] 2≤m≤M′ (3)
Wherein M ' < M, M ' represents p-R 'i[m，n]The thresholding is performed on the distance dimension.
6. The UWB bio-radar-based covert fatal lesion detection method of claim 5, wherein the linear discriminant analysis comprises:
designing a binary classification problem, and finding a group of projection coefficients w according to the principle that the intra-class variance is minimum and the inter-class variance is maximum after projection so as to maximize the generalized Rayleigh entropy of the two groups of processed characteristic value data:
wherein S iswAn intra-class divergence matrix, S, for two sets of eigenvalue databAn inter-class divergence matrix for two sets of eigenvalue data [ ·]TRepresenting the transposition, then projectively transforming the two sets of characteristic value data using w, the projectively transformed data R "i：
R"i＝wTR′i[m，n] (5)
And (3) measuring the distance between the two groups of characteristic value data after projection by adopting the Mahalanobis distance:
therein, sigma-1Is R1And R ″)2Represents [ · of]-1Inversion is carried out, and finally a threshold value is set for judgment, when D isMAnd when the value is more than or equal to 1, the two groups of characteristic value data are considered to be linearly separable, and the occurrence of injury is judged, otherwise, the judgment is normal.
7. A detection system for realizing the UWB biological radar-based hiding fatal flaw detection method according to any one of claims 1 to 6, comprising:
the UWB biological radar echo data acquisition module is used for acquiring two groups of UWB biological radar echo data which are symmetrical left and right of the physiological anatomical structure of the target object to be detected;
the normalization processing module is used for performing normalization processing on the two groups of collected UWB biological radar echo data;
the discrete cosine transform processing module is used for carrying out discrete cosine transform processing on the data after the normalization processing;
the threshold operation selection module is used for selecting threshold operation for the data after discrete cosine transform processing;
and the linear discriminant analysis module is used for performing linear discriminant analysis on the two groups of characteristic value data selected by the threshold operation.
8. The detection system according to claim 7, wherein the UWB biological radar echo data acquisition module comprises a pulse generator, a transmitter, a transceiver antenna, a sequential logic unit, a control unit and a receiver; wherein:
the impulse generator generates impulse with a certain pulse repetition frequency, the impulse is sent to the transmitter for shaping and then radiated out through the transmitting and receiving antenna, meanwhile, the impulse generated by the impulse generator is sent to the time sequence logic unit, a distance gate with controllable delay time is generated under the control of the control unit, and the receiver is triggered to sample the echo signal of the UWB biological radar.
9. The detection system according to claim 7 or 8, further comprising an analog-to-digital conversion module and a control display unit;
the analog-to-digital conversion module is used for converting the collected UWB biological radar echo signals and then sending the converted UWB biological radar echo signals to the control display unit;
and the control display unit is used for setting system parameters of the UWB biological radar, periodically controlling the delay time of the range gate and realizing scanning detection in a set range.
10. The detection system of claim 9, wherein the set distance range is determined by two parameters, a starting distance and a time window, corresponding to a sector area in a two-dimensional plane.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103454691A (en) * | 2013-08-22 | 2013-12-18 | 中国人民解放军第四军医大学 | Scanning probing method and system based on UWB biological radar |
| CN103616729A (en) * | 2013-11-06 | 2014-03-05 | 中国人民解放军第四军医大学 | UWB bio-radar-based multiple-human body object estimation method and system |
| US20150054670A1 (en) * | 2010-10-27 | 2015-02-26 | Jianqi Wang | Multichannel UWB-based radar life detector and positioning method thereof |
| CN109507653A (en) * | 2018-10-22 | 2019-03-22 | 中国人民解放军第四军医大学 | A method of multi-information perception bioradar system and its acquisition target information based on UWB |
| CN110335513A (en) * | 2019-04-28 | 2019-10-15 | 中国人民解放军联勤保障部队第九二〇医院 | A kind of interactive timeliness self and mutual medical aid training and examination model at sham fight scene |
-
2021
- 2021-01-20 CN CN202110076254.1A patent/CN112859071B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150054670A1 (en) * | 2010-10-27 | 2015-02-26 | Jianqi Wang | Multichannel UWB-based radar life detector and positioning method thereof |
| CN103454691A (en) * | 2013-08-22 | 2013-12-18 | 中国人民解放军第四军医大学 | Scanning probing method and system based on UWB biological radar |
| CN103616729A (en) * | 2013-11-06 | 2014-03-05 | 中国人民解放军第四军医大学 | UWB bio-radar-based multiple-human body object estimation method and system |
| CN109507653A (en) * | 2018-10-22 | 2019-03-22 | 中国人民解放军第四军医大学 | A method of multi-information perception bioradar system and its acquisition target information based on UWB |
| CN110335513A (en) * | 2019-04-28 | 2019-10-15 | 中国人民解放军联勤保障部队第九二〇医院 | A kind of interactive timeliness self and mutual medical aid training and examination model at sham fight scene |
Non-Patent Citations (1)
| Title |
|---|
| 张芫蓓;王帅杰;王昊;祁富贵;王健琪;李钊;: "基于双源IR-UWB生物雷达的强反射杂波抑制", 中国医疗设备, no. 11 * |
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