CN110764152A - Device and method for rapid detection and identification of unmanned aerial vehicle - Google Patents
Device and method for rapid detection and identification of unmanned aerial vehicle Download PDFInfo
- Publication number
- CN110764152A CN110764152A CN201911047640.7A CN201911047640A CN110764152A CN 110764152 A CN110764152 A CN 110764152A CN 201911047640 A CN201911047640 A CN 201911047640A CN 110764152 A CN110764152 A CN 110764152A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- frequency
- signal
- coordinate
- 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
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/12—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with electromagnetic waves
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Databases & Information Systems (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geophysics (AREA)
- Mobile Radio Communication Systems (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a method for rapidly detecting and identifying an unmanned aerial vehicle, which comprises the following steps: 1) signal acquisition; 2) time-frequency analysis; 3) inquiring the maximum value and the coordinate; 4) converting coordinate points; 5) judging a threshold value; 6) difference processing; 7) extracting characteristics; 8) establishing a database; 9) detecting and identifying single frequency band in the library; 10) detecting and identifying single frequency band outside the library; 11) and detecting and identifying the full frequency band. The invention also discloses a device for rapidly detecting and identifying the unmanned aerial vehicle. The device has low power consumption, no radio frequency pollution, low cost, convenient assembly and good practicability, and the method has simple operation, can finish the effective detection and identification of civil low, slow and small unmanned aerial vehicles, and provides a foundation for reasonably and normatively managing the unmanned aerial vehicles.
Description
Technical Field
The invention relates to the field of radio technology and signal processing, in particular to a device and a method for rapidly detecting and identifying an unmanned aerial vehicle.
Background
Along with the popularization of civil unmanned aerial vehicles, great convenience is brought to life of people, and meanwhile, a series of problems are brought, such as the phenomenon of 'black flying' of the unmanned aerial vehicles, the social security is affected, and therefore a plurality of researchers provide various methods for detecting and identifying the unmanned aerial vehicles. The radio frequency spectrum detection mode is an effective detection method, but the unmanned aerial vehicle signal often exists in a complex electromagnetic environment, so that the detection and identification of the unmanned aerial vehicle signal by adopting the radio frequency spectrum detection mode have certain difficulty.
At present, most methods for detecting and identifying the unmanned aerial vehicle by adopting frequency spectrum detection are complex and are not beneficial to the realization of actual products, and the method has less engineering practical value.
Disclosure of Invention
The invention aims to provide a device and a method for rapidly detecting and identifying an unmanned aerial vehicle, aiming at the defects of the prior art. The device has low power consumption, no radio frequency pollution, low cost, convenient assembly and good practicability, and the method has simple operation, can finish the effective detection and identification of civil low, slow and small unmanned aerial vehicles, and provides a foundation for reasonably and normatively managing the unmanned aerial vehicles.
The technical scheme for realizing the purpose of the invention is as follows:
the utility model provides a device of unmanned aerial vehicle short-term test and discernment, different with prior art, including FPGA module and the AD9361 radio frequency transceiver of being connected with the FPGA module, the FPGA module passes through the external PC terminal of serial ports, the inside unmanned aerial vehicle database that is equipped with of FPGA module, AD9361 radio frequency transceiver control module is used for the control to AD9361 radio frequency transceiver, unmanned aerial vehicle's signal preprocessing, unmanned aerial vehicle's signal characteristic draws and categorised, unmanned aerial vehicle signal's detection and discernment.
The FPGA module adopts a black gold development board AX7325, and is internally integrated with an FPGA of XC7K325TIFFG900, a DDR3 memory bank, a serial port and an FMC interface.
The chip of the AD9361 radio frequency transceiver is FMCOMMS3 of ADI company, and the AD9361 radio frequency transceiver is bidirectionally connected with the FPGA module through an FMC interface of AX 7325.
The method for rapidly detecting and identifying the unmanned aerial vehicle by adopting the device for rapidly detecting and identifying the unmanned aerial vehicle comprises the following steps:
1) signal acquisition: the AD9361 radio frequency transceiver is controlled by the FPGA module to acquire signals of a common frequency band of the unmanned aerial vehicle, and acquired data are acquired at one time by 800 ten thousand points;
2) time-frequency analysis: carrying out short-time Fourier transform on each 1024 points of 800 ten thousand data acquired in the step 1) to finish signal time-frequency analysis:
wherein x (t) is the collected signal, the complex conjugate sign, and g (t) is a window function;
3) inquiring the maximum value and the coordinate: circularly inquiring each section of data, and solving the maximum value and the coordinate of the frequency spectrum:
in the formula, Max (m) is maximum value data of a frequency spectrum of each 1024 points, and m is a coordinate point of the maximum value of the frequency spectrum;
4) coordinate point conversion: performing frequency spectrum coordinate point conversion to obtain a real frequency spectrum:
in the formula, h is a coordinate point obtained after coordinate frequency spectrum conversion processing;
5) judging a threshold value: setting a threshold value for the maximum value of the frequency spectrum, storing the coordinate when the maximum value of the frequency spectrum is greater than the threshold value, and filtering partial noise signals by the method:
wherein theresold is a set maximum threshold of the frequency spectrum;
6) difference processing: carrying out difference processing on the coordinate points obtained in the step 5) to obtain difference coordinate values:
p(n)＝h(k)-h(k-1),k＝1,2......7813,n＝1,2......7812, (5)，
wherein p (n) is a differential coordinate value;
7) feature extraction: setting a bandwidth threshold according to the bandwidth characteristics of the frequency hopping signal of the unmanned aerial vehicle, performing threshold judgment on the difference coordinate values one by one, counting when the difference coordinate values are less than or equal to the threshold, and if the difference coordinate values are greater than the threshold, counting 0, wherein the counting data is the number of the duration time of the signal within the extracted bandwidth range;
8) establishing a database: collecting signals of a plurality of existing unmanned aerial vehicles, counting the frequency hopping signal duration of each unmanned aerial vehicle and the frequency hopping times in 800 ten thousand points of data, and establishing an unmanned aerial vehicle information database;
9) detecting and identifying single frequency band in library: comparing the number of the signal duration time obtained in the step 7) with the frequency hopping duration time of the unmanned aerial vehicles in the unmanned aerial vehicle information database, if the number of the signal duration time is consistent with the frequency hopping duration time of the unmanned aerial vehicles in the unmanned aerial vehicle information database, recording the model and the frequency of the unmanned aerial vehicles corresponding to the data, then comparing the number of the frequency hopping frequency with the frequency hopping frequency in the unmanned aerial vehicle information database, and if the number of the frequency hopping frequency is consistent with the frequency hopping frequency in the unmanned aerial vehicle information;
10) detecting and identifying single frequency band outside the library: when the first conditions in the step 9) are not consistent, the duration time of the lowest remote control signal of the known unmanned aerial vehicle is used as a threshold, if the number of the duration time of the signal is greater than or equal to the threshold, the signal is stored, then the number of the same number in the stored data is counted, the number is compared with the lowest occurrence frequency of the unmanned aerial vehicle in the database, and if the number is greater than the minimum occurrence frequency of the unmanned aerial vehicle, the suspected unknown unmanned aerial vehicle can be judged to be present;
11) full-band detection and identification: when the single frequency band detection is completed, namely the steps 1) to 10) are completed, the AD9361 detection frequency band is switched and the steps 1) to 10) are repeated, the switching is performed once every time the switching is completed, the switching is performed for 36 times totally, the number of times of suspected unmanned aerial vehicles appearing in the 36 times of switching is obtained, the threshold value of the number of times of the unmanned aerial vehicles appearing is set, when the threshold value is larger than the threshold value, the existence of the unmanned aerial vehicles can be determined, and the type number and the number of the unmanned aerial vehicles can be detected and identified by comparing the threshold value.
The device has low power consumption, no radio frequency pollution, low cost, convenient assembly and good practicability, and the method has simple operation, can finish the effective detection and identification of civil low, slow and small unmanned aerial vehicles, and provides a foundation for reasonably and normatively managing the unmanned aerial vehicles.
Drawings
FIG. 1 is a schematic structural view of the apparatus in the example;
FIG. 2 is a schematic flow chart of the method in the example.
Detailed Description
The invention will be further illustrated, but not limited, by the following description of the embodiments with reference to the accompanying drawings.
Example (b):
referring to fig. 1, a device of unmanned aerial vehicle short-term test and discernment, AD9361 radio frequency transceiver including FPGA module and being connected with the FPGA module, the FPGA module passes through the external PC terminal of serial ports, the inside unmanned aerial vehicle database module that is equipped with of FPGA module, AD9361 radio frequency transceiver control module is used for the control to AD9361 radio frequency transceiver, unmanned aerial vehicle's signal preprocessing module, unmanned aerial vehicle's signal characteristic draws and classification module, unmanned aerial vehicle signal's detection and identification module.
The FPGA module adopts a black gold development board AX7325, and is internally integrated with an FPGA of XC7K325TIFFG900, an FMC interface, a DDR3 memory bank, a serial port and an FMC interface.
The chip of the AD9361 radio frequency transceiver is FMCOMMS3 of ADI company, and the AD9361 radio frequency transceiver is bidirectionally connected with the FPGA module through an FMC interface of AX 7325.
The method for rapidly detecting and identifying the unmanned aerial vehicle by adopting the device for rapidly detecting and identifying the unmanned aerial vehicle comprises the following steps as shown in figure 2:
1) signal acquisition: the AD9361 radio frequency transceiver is controlled by the FPGA module to acquire signals of a common frequency band of the unmanned aerial vehicle, and acquired data are acquired at one time by 800 ten thousand points;
2) time-frequency analysis: carrying out short-time Fourier transform on each 1024 points of 800 ten thousand data acquired in the step 1) to finish signal time-frequency analysis:
wherein x (t) is the collected signal, the complex conjugate sign, and g (t) is a window function;
3) inquiring the maximum value and the coordinate: circularly inquiring each section of data, and solving the maximum value and the coordinate of the frequency spectrum:
in the formula, Max (m) is maximum value data of a frequency spectrum of each 1024 points, and m is a coordinate point of the maximum value of the frequency spectrum;
4) coordinate point conversion: performing frequency spectrum coordinate point conversion to obtain a real frequency spectrum:
in the formula, h is a coordinate point obtained after coordinate frequency spectrum conversion processing;
5) judging a threshold value: setting a threshold value for the maximum value of the frequency spectrum, storing the coordinate when the maximum value of the frequency spectrum is greater than the threshold value, and filtering partial noise signals by the method:
wherein theresold is a set maximum threshold of the frequency spectrum;
6) difference processing: carrying out difference processing on the coordinate points obtained in the step 5) to obtain difference coordinate values:
p(n)＝h(k)-h(k-1),k＝1,2......7813,n＝1,2......7812, (5)，
wherein p (n) is a differential coordinate value;
7) feature extraction: setting a bandwidth threshold according to the bandwidth characteristics of the frequency hopping signal of the unmanned aerial vehicle, carrying out threshold judgment on the difference coordinate values one by one, counting if the difference coordinate values are less than or equal to the threshold, and if the difference coordinate values are greater than the threshold, counting 0, wherein the counting data is the number of the signal duration time within the extracted bandwidth range;
8) establishing a database: collecting signals of a plurality of existing unmanned aerial vehicles, counting the frequency hopping signal duration of each unmanned aerial vehicle and the frequency hopping times in 800 ten thousand points of data, and establishing an unmanned aerial vehicle information database;
9) detecting and identifying single frequency band in library: comparing the number of the signal duration time obtained in the step 7) with the frequency hopping duration time of the unmanned aerial vehicles in the unmanned aerial vehicle information database, if the number of the signal duration time is consistent with the frequency hopping duration time of the unmanned aerial vehicles in the unmanned aerial vehicle information database, recording the model and the frequency of the unmanned aerial vehicles corresponding to the data, then comparing the number of the frequency hopping frequency with the frequency hopping frequency in the unmanned aerial vehicle information database, and if the number of the frequency hopping frequency is consistent with the frequency hopping frequency in the unmanned aerial vehicle information;
10) detecting and identifying single frequency band outside the library: when the first conditions in the step 9) are not consistent, the duration time of the lowest remote control signal of the known unmanned aerial vehicle is used as a threshold, if the number of the duration time of the signal is greater than or equal to the threshold, the signal is stored, then the number of the same number in the stored data is counted, the number is compared with the lowest occurrence frequency of the unmanned aerial vehicle in the database, and if the number is greater than the minimum occurrence frequency of the unmanned aerial vehicle, the suspected unknown unmanned aerial vehicle can be judged to be present;
11) full-band detection and identification: when the single frequency band detection is completed, namely the steps 1) to 10) are completed, the AD9361 detection frequency band is switched and the steps 1) to 10) are repeated, the switching is performed once every time the switching is completed, the switching is performed for 36 times totally, the number of times of suspected unmanned aerial vehicles appearing in the 36 times of switching is obtained, the threshold value of the number of times of the unmanned aerial vehicles appearing is set, when the threshold value is larger than the threshold value, the existence of the unmanned aerial vehicles can be determined, and the type number and the number of the unmanned aerial vehicles can be detected and identified by comparing the threshold value.
Claims (4)
1. The utility model provides a device of unmanned aerial vehicle short-term test and discernment, characterized by, including FPGA module and the AD9361 radio frequency transceiver of being connected with the FPGA module, the FPGA module passes through the external PC terminal of serial ports, the inside unmanned aerial vehicle database that is equipped with of FPGA module, AD9361 radio frequency transceiver control module is used for the control to AD9361 radio frequency transceiver, unmanned aerial vehicle's signal preprocessing, unmanned aerial vehicle's signal characteristic draws and categorised, unmanned aerial vehicle signal's detection and discernment.
2. The device for rapid detection and identification of unmanned aerial vehicle of claim 1, wherein the FPGA module adopts a black gold development board AX7325, and is internally integrated with an FPGA of XC7K325TIFFG900, a DDR3 memory bank, a serial port and an FMC interface.
3. The device of claim 1, wherein the chip of the AD9361 rf transceiver is FMCOMMS3 from ADI, and the AD9361 rf transceiver is bidirectionally coupled to the FPGA module through an FMC interface of AX 7325.
4. A method for rapidly detecting and identifying an unmanned aerial vehicle is characterized by comprising a device for rapidly detecting and identifying the unmanned aerial vehicle according to any one of the right 1-3, and the method comprises the following steps:
1) signal acquisition: the AD9361 radio frequency transceiver is controlled by the FPGA module to acquire signals of a common frequency band of the unmanned aerial vehicle, and acquired data are acquired at one time by 800 ten thousand points;
2) time-frequency analysis: carrying out short-time Fourier transform on each 1024 points of 800 ten thousand data acquired in the step 1) to finish signal time-frequency analysis:
wherein x (t) is the collected signal, the complex conjugate sign, and g (t) is a window function;
3) inquiring the maximum value and the coordinate: circularly inquiring each section of data, and solving the maximum value and the coordinate of the frequency spectrum:
in the formula, Max (m) is maximum value data of a frequency spectrum of each 1024 points, and m is a coordinate point of the maximum value of the frequency spectrum;
4) coordinate point conversion: performing frequency spectrum coordinate point conversion to obtain a real frequency spectrum:
in the formula, h is a coordinate point obtained after coordinate frequency spectrum conversion processing;
5) judging a threshold value: setting a threshold value for the maximum value of the frequency spectrum, and storing the coordinate when the maximum value of the frequency spectrum is greater than the threshold value:
wherein theresold is a set maximum threshold of the frequency spectrum;
6) difference processing: carrying out difference processing on the coordinate points obtained in the step 5) to obtain difference coordinate values:
p(n)＝h(k)-h(k-1),k＝1,2......7813,n＝1,2......7812, (5)，
wherein p (n) is a differential coordinate value;
7) feature extraction: setting a bandwidth threshold according to the bandwidth characteristics of the frequency hopping signal of the unmanned aerial vehicle, performing threshold judgment on the difference coordinate values one by one, counting when the difference coordinate values are less than or equal to the threshold, and if the difference coordinate values are greater than the threshold, counting 0, wherein the counting data is the number of the duration time of the signal within the extracted bandwidth range;
8) establishing a database: collecting signals of a plurality of existing unmanned aerial vehicles, counting the frequency hopping signal duration of each unmanned aerial vehicle and the frequency hopping times in 800 ten thousand points of data, and establishing an unmanned aerial vehicle information database;
9) detecting and identifying single frequency band in library: comparing the number of the signal duration time obtained in the step 7) with the frequency hopping duration time of the unmanned aerial vehicles in the unmanned aerial vehicle information database, if the number of the signal duration time is consistent with the frequency hopping duration time of the unmanned aerial vehicles in the unmanned aerial vehicle information database, recording the model and the frequency of the unmanned aerial vehicles corresponding to the data, then comparing the number of the frequency hopping frequency with the frequency hopping frequency in the unmanned aerial vehicle information database, and if the number of the frequency hopping frequency is consistent with the frequency hopping frequency in the unmanned aerial vehicle information;
10) detecting and identifying single frequency band outside the library: when the first conditions in the step 9) are not consistent, the duration time of the lowest remote control signal of the known unmanned aerial vehicle is used as a threshold, if the number of the duration time of the signal is greater than or equal to the threshold, the signal is stored, then the number of the same number in the stored data is counted, the number is compared with the lowest occurrence frequency of the unmanned aerial vehicle in the database, and if the number is greater than the minimum occurrence frequency of the unmanned aerial vehicle, the suspected unknown unmanned aerial vehicle can be judged to be present;
11) full-band detection and identification: when the single frequency band detection is completed, namely the steps 1) to 10) are completed, the AD9361 detection frequency band is switched and the steps 1) to 10) are repeated, the switching is performed once every time the switching is completed, the switching is performed for 36 times totally, the number of times of suspected unmanned aerial vehicles appearing in the 36 times of switching is obtained, the threshold value of the number of times of the unmanned aerial vehicles appearing is set, when the threshold value is larger than the threshold value, the existence of the unmanned aerial vehicles can be determined, and the type number and the number of the unmanned aerial vehicles can be detected and identified by comparing the threshold value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911047640.7A CN110764152B (en) | 2019-10-30 | 2019-10-30 | Device and method for rapid detection and identification of unmanned aerial vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911047640.7A CN110764152B (en) | 2019-10-30 | 2019-10-30 | Device and method for rapid detection and identification of unmanned aerial vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110764152A true CN110764152A (en) | 2020-02-07 |
| CN110764152B CN110764152B (en) | 2021-06-11 |
Family
ID=69333407
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911047640.7A Active CN110764152B (en) | 2019-10-30 | 2019-10-30 | Device and method for rapid detection and identification of unmanned aerial vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110764152B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111652183A (en) * | 2020-06-19 | 2020-09-11 | 桂林电子科技大学 | Unmanned aerial vehicle detection and identification method based on image-transmission signal multi-feature fusion |
| CN111884735A (en) * | 2020-07-30 | 2020-11-03 | 北京环境特性研究所 | Frequency spectrum-based unmanned aerial vehicle detection method and detection system thereof |
| CN112598934A (en) * | 2020-12-25 | 2021-04-02 | 桂林电子科技大学 | Unmanned aerial vehicle detection system and detection method based on Beidou positioning and wireless networking |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015132772A2 (en) * | 2014-03-07 | 2015-09-11 | Elettronica S.P.A. | Photonic-assisted rf spectrum scanner for ultra-wide band receivers |
| US20170045638A1 (en) * | 2014-04-23 | 2017-02-16 | Cgg Services Sa | Systems and methods for multiple bandwidth electromagnetic geophysical exploration |
| CN108233985A (en) * | 2017-12-28 | 2018-06-29 | 厦门安胜网络科技有限公司 | A kind of unmanned plane management method, terminal device and storage medium |
| CN108333584A (en) * | 2017-12-28 | 2018-07-27 | 陕西弘毅军民融合智能科技有限公司 | A kind of remote unmanned plane detection system of low altitude small target and detection method |
| CN108880929A (en) * | 2018-05-24 | 2018-11-23 | 浙江大学 | A kind of communication protocol extracting method towards the civilian no-manned machine distant control chain circuit of microminiature |
| CN109034088A (en) * | 2018-08-06 | 2018-12-18 | 北京邮电大学 | A kind of unmanned plane signal detection method and device |
| CN109270510A (en) * | 2018-11-21 | 2019-01-25 | 北京理工大学 | A kind of small accurate extracting method of structure target scattering center model of complexity |
| KR101948852B1 (en) * | 2017-10-25 | 2019-05-29 | 세종대학교산학협력단 | Hybrid image scanning method and apparatus for noncontact crack evaluation |
| CN110161902A (en) * | 2019-04-30 | 2019-08-23 | 桂林电子科技大学 | A kind of unmanned plane figure passes and the Universal-purpose quick acquisition device and acquisition method of remote signal |
| US10775786B2 (en) * | 2016-01-19 | 2020-09-15 | Percepto Robotics Ltd | Method and system for emulating modular agnostic control of commercial unmanned aerial vehicles (UAVS) |
-
2019
- 2019-10-30 CN CN201911047640.7A patent/CN110764152B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015132772A2 (en) * | 2014-03-07 | 2015-09-11 | Elettronica S.P.A. | Photonic-assisted rf spectrum scanner for ultra-wide band receivers |
| US20170045638A1 (en) * | 2014-04-23 | 2017-02-16 | Cgg Services Sa | Systems and methods for multiple bandwidth electromagnetic geophysical exploration |
| US10775786B2 (en) * | 2016-01-19 | 2020-09-15 | Percepto Robotics Ltd | Method and system for emulating modular agnostic control of commercial unmanned aerial vehicles (UAVS) |
| KR101948852B1 (en) * | 2017-10-25 | 2019-05-29 | 세종대학교산학협력단 | Hybrid image scanning method and apparatus for noncontact crack evaluation |
| CN108233985A (en) * | 2017-12-28 | 2018-06-29 | 厦门安胜网络科技有限公司 | A kind of unmanned plane management method, terminal device and storage medium |
| CN108333584A (en) * | 2017-12-28 | 2018-07-27 | 陕西弘毅军民融合智能科技有限公司 | A kind of remote unmanned plane detection system of low altitude small target and detection method |
| CN108880929A (en) * | 2018-05-24 | 2018-11-23 | 浙江大学 | A kind of communication protocol extracting method towards the civilian no-manned machine distant control chain circuit of microminiature |
| CN109034088A (en) * | 2018-08-06 | 2018-12-18 | 北京邮电大学 | A kind of unmanned plane signal detection method and device |
| CN109270510A (en) * | 2018-11-21 | 2019-01-25 | 北京理工大学 | A kind of small accurate extracting method of structure target scattering center model of complexity |
| CN110161902A (en) * | 2019-04-30 | 2019-08-23 | 桂林电子科技大学 | A kind of unmanned plane figure passes and the Universal-purpose quick acquisition device and acquisition method of remote signal |
Non-Patent Citations (1)
| Title |
|---|
| 杨亚南: "无人机信号的侦收与识别方法研究", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111652183A (en) * | 2020-06-19 | 2020-09-11 | 桂林电子科技大学 | Unmanned aerial vehicle detection and identification method based on image-transmission signal multi-feature fusion |
| CN111652183B (en) * | 2020-06-19 | 2022-03-29 | 桂林电子科技大学 | Unmanned aerial vehicle detection and identification method based on image-transmission signal multi-feature fusion |
| CN111884735A (en) * | 2020-07-30 | 2020-11-03 | 北京环境特性研究所 | Frequency spectrum-based unmanned aerial vehicle detection method and detection system thereof |
| CN111884735B (en) * | 2020-07-30 | 2022-04-01 | 北京环境特性研究所 | Frequency spectrum-based unmanned aerial vehicle detection method and detection system thereof |
| CN112598934A (en) * | 2020-12-25 | 2021-04-02 | 桂林电子科技大学 | Unmanned aerial vehicle detection system and detection method based on Beidou positioning and wireless networking |
| CN112598934B (en) * | 2020-12-25 | 2022-02-18 | 桂林电子科技大学 | Unmanned aerial vehicle detection system and detection method based on Beidou positioning and wireless networking |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110764152B (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110764152B (en) | Device and method for rapid detection and identification of unmanned aerial vehicle | |
| CN105678273B (en) | The starting point detection algorithm of radio-frequency fingerprint identification technology transient signal | |
| CN111175718B (en) | Automatic target recognition method and system for ground radar combining time-frequency domains | |
| CN111310700B (en) | Intermediate frequency sampling sequence processing method for radiation source fingerprint feature recognition | |
| CN108280395B (en) | Efficient identification method for flight control signals of low-small-slow unmanned aerial vehicle | |
| CN103941254A (en) | Soil physical property classification recognition method and device based on geological radar | |
| CN104569694B (en) | Electric signal feature extraction and recognition system oriented to aircraft flying process | |
| CN111913156A (en) | Radar radiation source individual identification method based on deep learning model and feature combination | |
| CN103743980A (en) | Power quality disturbance recognition and classification method based on PSO (Particle Swarm Optimization) for SVM (Support Vector Machine) | |
| CN108768562A (en) | A kind of illegal unmanned plane recognition methods based on remote control with figure communication number | |
| CN106385288B (en) | A kind of across the channel fusion detection device of radar pulse and its detection method | |
| CN108197581B (en) | Unmanned aerial vehicle signal identification detection method based on improved AC-WGANs | |
| CN108680796A (en) | Electromagnetic information leakage detecting system and method for computer display | |
| CN108549078A (en) | A kind of radar pulse signal is across combining channel and detection method | |
| CN105429719A (en) | Strong interference signal detection method based on power spectrum and multiple dimensioned wavelet transformation analysis | |
| CN109359523A (en) | A kind of satellite navigation interference type recognition methods based on SVM multi-classification algorithm | |
| CN105550702B (en) | A kind of GNSS Deceiving interference recognition methods based on SVM | |
| CN115438708A (en) | Classification and identification method based on convolutional neural network and multi-mode fusion | |
| CN112929141A (en) | Unmanned aerial vehicle detection and identification method and system based on graph-borne signal matching | |
| CN111830321A (en) | Unmanned aerial vehicle detection and identification method based on radio frequency fingerprint | |
| CN106483513A (en) | A kind of aircraft class target micro-doppler texture characteristic extracting method | |
| CN205812023U (en) | Radio monitoring station signal recognition system | |
| CN102546116A (en) | Compressive-sensing-based frequency hopping signal blind-detection method | |
| CN104125026A (en) | Ultrahigh-frequency RFID (radio frequency identification) label protocol conformance testing method | |
| Wang et al. | Research on UAV Remote Control Signal Recognition Based on Time-frequency Map |
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 |