CN210487967U - Anti-unmanned aerial vehicle detection tracking interference system - Google Patents
Anti-unmanned aerial vehicle detection tracking interference system Download PDFInfo
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Abstract
The utility model provides an anti-unmanned aerial vehicle detection tracking interference system, which comprises a detection radar, a photoelectric tracking system, an unmanned aerial vehicle interference unit and a holder; the photoelectric tracking system comprises a motion detection algorithm module, a related filtering target tracking algorithm module, a deep learning target detection algorithm module and a deep learning target tracking algorithm module; the detection radar is in communication connection with the photoelectric tracking system; and the photoelectric tracking system is in communication connection with the holder. According to the anti-unmanned aerial vehicle detection tracking interference system, when the target distance is long, the target characteristics cannot be extracted by the deep learning target detection algorithm module, and the target detection is carried out by the motion detection algorithm module; when the target distance is long and the target features cannot be extracted by the deep learning target tracking algorithm module, the target is tracked by adopting a related filtering target tracking algorithm module; the problem that the deep learning target tracking algorithm module cannot provide confidence coefficient is solved by adopting data of the relevant filtering target tracking algorithm module.
Description
Technical Field
The utility model relates to a prevent unmanned aerial vehicle and trail technical field, in particular to prevent unmanned aerial vehicle and survey tracking interference system.
Background
The detection radar is responsible for searching and finding a target in the anti-unmanned aerial vehicle system, the photoelectric system controls the holder lens according to target angle and distance data provided by the detection radar, detection, locking and tracking tasks of the target are completed, and then the interference equipment is controlled to transmit an interference signal to the periphery of the target unmanned aerial vehicle until the unmanned aerial vehicle is driven away. Traditional anti-unmanned aerial vehicle's system is including surveying radar, photoelectric tracking device, cloud platform, satellite navigation and remote control signal's interfering device. The detection radar is responsible for finding the target of the unmanned aerial vehicle and sending the target angle and distance data to the photoelectric tracking device; the photoelectric tracking device mainly comprises a target detection part and a target tracking part, wherein a target detection module controls a holder to point a lens to a target area according to target angle data provided by a detection radar, then performs focusing imaging on the target area according to distance data, then performs target detection according to target characteristics of an unmanned aerial vehicle, automatically or manually locks a target after the target is found, and transmits information of the locked target to the target tracking module; the tracking module of the photoelectric tracking device extracts features in the area around the original position of the last frame of target, finds the highest position of the matching degree with the target features and takes the highest position as the new position of the target, then adjusts the new position of the holder and the lens, and then automatically or manually controls the unmanned aerial vehicle interference unit in linkage to transmit satellite navigation and remote control interference signals to the target area until the unmanned aerial vehicle is driven away. At present, an optoelectronic system adopting a traditional target detection and target tracking algorithm is easy to lose a target under the conditions of hovering, shielding and deformation of an unmanned aerial vehicle; the target detection and tracking algorithm based on the deep learning technology has good detection and tracking capabilities for a complete target in a simple scene, has strong robustness for scale change, deformation and the like, can solve the problems of hovering, shielding and deformation of the unmanned aerial vehicle, and has a detection and tracking effect to be improved under the conditions of long distance, small target and unobvious target characteristics. Therefore, the respective advantages of the traditional target detection tracking algorithm and the deep learning target detection tracking algorithm are necessarily combined, and a novel anti-unmanned aerial vehicle detection tracking interference system capable of automatically detecting, locking and tracking is designed.
SUMMERY OF THE UTILITY MODEL
In order to solve the problems existing in the background technology, the utility model provides an anti-unmanned aerial vehicle detection and tracking interference system, which comprises a detection radar, a photoelectric tracking system, an unmanned aerial vehicle interference device and a holder;
the photoelectric tracking system comprises a motion detection algorithm module, a related filtering target tracking algorithm module, a deep learning target detection algorithm module and a deep learning target tracking algorithm module;
the motion detection algorithm module is used for detecting the target of the long-distance motion unmanned aerial vehicle;
the relevant filtering target tracking algorithm module is used for tracking the target of the long-distance moving unmanned aerial vehicle;
the deep learning target detection algorithm module is used for target detection of the medium and short distance unmanned aerial vehicles;
the deep learning target tracking algorithm module is used for tracking targets of the medium-distance and short-distance unmanned aerial vehicles;
the detection radar is in communication connection with the photoelectric tracking system;
the photoelectric tracking system is in communication connection with the holder;
the unmanned aerial vehicle interference unit is in communication connection with the photoelectric tracking system.
Further, the unmanned aerial vehicle interference unit comprises a positioning channel interference unit and a remote control channel interference unit.
And further, the motion detection algorithm module comprises algorithms of foreground extraction, edge extraction, foreground fusion and the like.
And further, the related filtering target tracking algorithm module comprises algorithms of feature extraction, template updating, frequency domain point multiplication and the like.
Further, the deep learning target detection algorithm module comprises a plurality of convolution layers and a plurality of full-connection layers, a convolution network is adopted to extract features, and then the full-connection layers are used to obtain a predicted value.
And furthermore, the deep learning target tracking algorithm module comprises a plurality of convolution layers and a plurality of full connection layers, the features of a target area and a search area are respectively extracted from a previous frame and a current frame by adopting two sets of convolution networks, and the full connection layers are used for comparing the features of the target area and the search area and outputting a new target position.
Compare with the anti-unmanned aerial vehicle system's of traditional tradition structure, the utility model has the characteristics of as follows:
1. the target detection is realized by adopting a mode of combining deep learning and motion detection, and the target can be found in a target hovering state;
2. the target tracking is realized by adopting a mode of combining deep learning and kernel-related filtering, and the target cannot be lost under the conditions of target shielding and deformation;
3. the automatic target locking function is added on the basis of the original manual locking, and unattended operation is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a schematic structural diagram of an anti-drone detecting and tracking jamming system provided by the present invention;
fig. 2 is a diagram illustrating the tracking process and hardware structure of the optoelectronic system according to the present invention.
The attached drawings are as follows:
10 detecting a radar;
20 a photoelectric tracking system;
21 a motion detection algorithm module;
22 a related filtering target tracking algorithm module;
23 deep learning target detection algorithm module;
24 a deep learning target tracking algorithm module;
30 unmanned aerial vehicle disturbers;
40 cloud platform.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by a person skilled in the art without creative efforts belong to the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for convenience of description and simplification of the description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
An anti-unmanned aerial vehicle detection tracking interference system comprises a detection radar 10, a photoelectric tracking system 20, an unmanned aerial vehicle interference device 30 and a holder 40;
the photoelectric tracking system 20 comprises a motion detection algorithm module 21, a related filtering target tracking algorithm module 22, a deep learning target detection algorithm module 23 and a deep learning target tracking algorithm module 24;
the motion detection algorithm module 21 is used for detecting the target of the long-distance motion unmanned aerial vehicle;
a related filtering target tracking algorithm module 22, which is used for tracking the target of the long-distance moving unmanned aerial vehicle;
the deep learning target detection algorithm module 23 is used for target detection of the medium-distance and short-distance unmanned aerial vehicles;
the deep learning target tracking algorithm module 24 is used for target tracking of the medium-distance and short-distance unmanned aerial vehicles;
the detection radar 10 is in communication connection with the photoelectric tracking system 20;
the photoelectric tracking system 20 is in communication connection with the holder 40;
the unmanned aerial vehicle interference unit 30 is in communication connection with the photoelectric tracking system 20.
In specific implementation, as shown in fig. 1, the anti-unmanned aerial vehicle detection and tracking interference system integrates a detection radar 10, a photoelectric tracking system 20, an unmanned aerial vehicle jammer 30, a cradle head 40 and other devices to form the anti-unmanned aerial vehicle detection and tracking interference system;
as shown in fig. 2, the photoelectric tracking system 20 is composed of a traditional target detection tracking board and an intelligent target detection tracking board, and the two circuit boards are connected with each other by adopting a CVBS, a HD-SDI video interface and an RS-422 serial port; the traditional target detection tracking plate provides analog and high-definition video signals to the intelligent target detection tracking plate through the CVBS and the HD-SDI interface respectively, the intelligent target detection tracking plate decodes the accessed video signals in real time, then performs target detection and tracking on decoded standard-definition or high-definition images, and feeds back an intelligent analysis result of the target detection and tracking to the traditional target detection tracking plate through the RS-422 interface; the photoelectric tracking system 20 comprises a motion detection algorithm module 21, a related filtering target tracking algorithm module 22, a deep learning target detection algorithm module 23 and a deep learning target tracking algorithm module 24;
the detection radar 10 is in communication connection with the photoelectric tracking system 20, and the detection radar 10 is used for transmitting a position signal of a target to the photoelectric tracking system 20; the photoelectric tracking system 20 analyzes the received position signal of the target;
the photoelectric tracking system 20 is in communication connection with the cradle head 40, and the photoelectric tracking system 20 can adjust the position of the cradle head 40 according to the analyzed target position.
The unmanned aerial vehicle interference unit 30 is in communication connection with the photoelectric tracking system 20; the drone interferer 30 sends out an interfering signal to evict the target.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention.
Claims (6)
1. The utility model provides an anti unmanned aerial vehicle surveys and tracks interference system which characterized in that: the system comprises a detection radar, a photoelectric tracking system, an unmanned aerial vehicle interference unit and a holder;
the photoelectric tracking system comprises a motion detection algorithm module, a related filtering target tracking algorithm module, a deep learning target detection algorithm module and a deep learning target tracking algorithm module;
the motion detection algorithm module is used for detecting the target of the long-distance motion unmanned aerial vehicle;
the relevant filtering target tracking algorithm module is used for tracking the target of the long-distance moving unmanned aerial vehicle;
the deep learning target detection algorithm module is used for target detection of the medium and short distance unmanned aerial vehicles;
the deep learning target tracking algorithm module is used for tracking targets of the medium-distance and short-distance unmanned aerial vehicles;
the detection radar is in communication connection with the photoelectric tracking system;
the photoelectric tracking system is in communication connection with the holder;
the unmanned aerial vehicle interference unit is in communication connection with the photoelectric tracking system.
2. The anti-drone detection tracking jamming system according to claim 1, characterized in that: the unmanned aerial vehicle interference unit is a radio signal transmitting device with serial port communication control and comprises a positioning channel interference unit and a remote control channel interference unit.
3. The anti-drone detection tracking jamming system according to claim 1, characterized in that: and the motion detection algorithm module comprises a foreground extraction unit, an edge extraction unit and a foreground fusion unit.
4. The anti-drone detection tracking jamming system according to claim 1, characterized in that: and the related filtering target tracking algorithm module comprises a feature extraction unit, a template updating unit and a frequency domain point multiplication unit.
5. The anti-drone detection tracking jamming system according to claim 1, characterized in that: the deep learning target detection algorithm module comprises a plurality of convolution layers and a plurality of full-connection layers, a convolution network is adopted to extract features, and then the full-connection layers are used to obtain a predicted value.
6. The anti-drone detection tracking jamming system according to claim 1, characterized in that: the deep learning target tracking algorithm module comprises a plurality of convolution layers and a plurality of full connection layers, wherein two sets of convolution networks are adopted to respectively extract the characteristics of a target area and a search area from a previous frame and a current frame, and the full connection layers are used for comparing the characteristics of the target area and the search area and outputting a new target position.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110398720A (en) * | 2019-08-21 | 2019-11-01 | 深圳耐杰电子技术有限公司 | A kind of anti-unmanned plane detection tracking interference system and photoelectric follow-up working method |
| CN112485781A (en) * | 2020-11-18 | 2021-03-12 | 济南和普威视光电技术有限公司 | Anti-unmanned aerial vehicle unattended system and method based on deep learning |
| CN115355764A (en) * | 2022-09-02 | 2022-11-18 | 中交遥感载荷(江苏)科技有限公司 | Unmanned aerial vehicle confrontation method based on vision for identifying enemy and my targets |
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2019
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110398720A (en) * | 2019-08-21 | 2019-11-01 | 深圳耐杰电子技术有限公司 | A kind of anti-unmanned plane detection tracking interference system and photoelectric follow-up working method |
| CN110398720B (en) * | 2019-08-21 | 2024-05-03 | 深圳耐杰电子技术有限公司 | Anti-unmanned aerial vehicle detection tracking interference system and working method of photoelectric tracking system |
| CN112485781A (en) * | 2020-11-18 | 2021-03-12 | 济南和普威视光电技术有限公司 | Anti-unmanned aerial vehicle unattended system and method based on deep learning |
| CN112485781B (en) * | 2020-11-18 | 2022-10-28 | 济南和普威视光电技术有限公司 | Anti-unmanned aerial vehicle unmanned system and method based on deep learning |
| CN115355764A (en) * | 2022-09-02 | 2022-11-18 | 中交遥感载荷(江苏)科技有限公司 | Unmanned aerial vehicle confrontation method based on vision for identifying enemy and my targets |
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