Unmanned aerial vehicle identification system and method based on radio technology

Abstract

The invention discloses an unmanned aerial vehicle identification system and method based on a radio technology. The system comprises a radio receiving module, a signal processing module, an unmanned aerial vehicle identification module, a data storage module, a display module and a control module. According to the unmanned aerial vehicle identification system and method based on the radio technology, the radio receiving module is used in combination with a directional antenna and an omnidirectional antenna, a wide frequency band range can be covered, the detection capacity for different types of unmanned aerial vehicles is remarkably enhanced, the signal processing module adopts a spectrum analysis technology, and the detection accuracy is improved. The method optimizes the recognition precision and robustness in a dynamic environment by using deep learning and reinforcement learning algorithms, improves the adaptability of the system in a complex and changing environment, ensures the efficient and accurate recognition of the type, position and other features of the unmanned aerial vehicle, can give out low, medium and high risk alarms according to the flight state and potential threat level of the unmanned aerial vehicle, and improves the safety of the unmanned aerial vehicle. And the safety of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drone identification, and in particular to a drone identification system and method based on radio technology. Background Art

[0002] With the rapid development of drone technology, drones are increasingly being used in military, logistics, aerial photography, and other fields. At the same time, the challenges posed by drones, such as airspace security and privacy protection, are becoming increasingly severe. Traditional drone detection methods, such as visual monitoring and radar monitoring, have limitations such as limited detection range and insensitivity to low-altitude, small drones.

[0003] In existing technologies, the recognition system has poor adaptability in complex environments, making it difficult to accurately identify drones of different types and locations, unable to update the latest feature information of drones in real time, and has limited cross-platform data interoperability and collaboration capabilities. The security and emergency response capabilities of the existing system are insufficient, and it is unable to conduct effective risk assessments and alerts based on the threat level of drones, and lacks the ability to quickly respond to emergencies. Summary of the Invention

[0004] The main purpose of the present invention is to provide a charging control method and system for a non-liquid-cooled high-current charging device, which can effectively solve the problems of existing drone identification systems such as poor adaptability in complex environments, limited data update and cross-platform collaboration capabilities, insufficient security and emergency response, and difficulty in effectively assessing and responding to potential threats.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a UAV identification system based on radio technology, the system comprising:

[0006] Radio receiving module: used to receive radio signals from the drone;

[0007] Signal processing module: used to perform time domain and frequency domain analysis on the received radio signal, remove environmental noise, and extract unique electromagnetic characteristic information of the UAV;

[0008] UAV identification module: used to identify the type, location, heading, flight altitude and identity characteristics of the UAV based on the radio signal characteristics using a machine learning algorithm;

[0009] Data storage module: used to store real-time recognition results, historical signal data and characteristic information of known drones;

[0010] Display module: used to display real-time recognition results and drone monitoring information on the user terminal, including flight trajectory, flight plan and safety alerts;

[0011] Control module: used to coordinate the operations of various modules.

[0012] Preferably, the radio receiving module covers a frequency band range based on a receiving unit, the frequency band includes low frequency, medium frequency, high frequency, microwave and different frequency bands, and the receiving unit includes a directional antenna and an omnidirectional antenna.

[0013] Preferably, the signal processing module includes a spectrum analysis unit, which is used to identify characteristic patterns of signals in a multi-dimensional signal space, and the drone identification module adopts a deep learning model and a reinforcement learning algorithm.

[0014] Preferably, the data storage module includes a dynamic database, which is used to update and store the latest feature information and behavior patterns of different types of drones in real time.

[0015] Preferably, the display module includes an alarm unit, and the alarm unit includes a low risk alarm, a medium risk alarm and a high risk alarm.

[0016] In addition, the present invention also provides a method for using the above-mentioned radio-based drone identification system, the method comprising:

[0017] Step S1: receiving a radio signal from a UAV through a radio receiving module;

[0018] Step S2: demodulating, denoising and extracting features of the received radio signal through the signal processing module;

[0019] Step S3: using the drone identification module to identify the type, location, heading, and identity characteristics of the drone based on the characteristic information;

[0020] Step S4: storing the recognition result in a data storage module;

[0021] Step S5: Display the recognition result through the display module and perform real-time monitoring.

[0022] Preferably, in step S1, the radio signal includes a remote control signal, a communication signal and a position positioning signal of the UAV.

[0023] Preferably, in step S2, the signal processing module extracts the time-frequency characteristics of the signal based on spectrum analysis technology.

[0024] Preferably, in step S3, the recognition module uses a pattern recognition algorithm to classify the drone type based on the characteristic pattern of the radio signal.

[0025] Preferably, in step S4, the recognition results include but are not limited to the real-time location, flight trajectory, heading information, flight time, recognition time and safety hazards of the drone, and the data storage module supports cross-platform storage and sharing.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. In this invention, the radio receiving module combines the use of directional antennas and omnidirectional antennas to cover a wide range of frequency bands, significantly enhancing the detection capabilities of different types of drones. The signal processing module adopts spectrum analysis technology and utilizes deep learning and reinforcement learning algorithms to optimize recognition accuracy and robustness in dynamic environments, improving the system's adaptability in complex and changing environments, and ensuring efficient and accurate identification of drone types, locations, and other characteristics.

[0028] 2. In the present invention, the data storage module adopts a dynamic database, which can update the latest feature information of different types of drones in real time, enhancing the ability to identify unknown or new drones. Through cross-platform storage and sharing functions, the system can realize data intercommunication and real-time updates between different devices and systems, improve the collaboration and availability of data, and ensure reliability and flexibility in long-term operation.

[0029] 3. In the present invention, the method uses the alarm unit of the display module, and the system can issue low, medium, and high risk alarms according to the flight status and potential threat level of the drone, helping users to timely evaluate and respond to different risk situations. This not only improves the security of the system, but also supports rapid decision-making, ensuring that a quick response can be made in the event of an emergency, thereby enhancing the practicality of monitoring and management and emergency response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0031] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0032] A UAV identification system based on radio technology, the system comprising:

[0033] Radio receiving module: used to receive radio signals from the drone;

[0034] Signal processing module: used to perform time domain and frequency domain analysis on the received radio signal, remove environmental noise, and extract unique electromagnetic characteristic information of the UAV;

[0035] UAV identification module: used to identify the type, location, heading, flight altitude and identity characteristics of the UAV based on the radio signal characteristics using a machine learning algorithm;

[0036] Data storage module: used to store real-time recognition results, historical signal data and characteristic information of known drones;

[0037] Display module: used to display real-time recognition results and drone monitoring information on the user terminal, including flight trajectory, flight plan and safety alerts;

[0038] Control module: used to coordinate the operations of various modules.

[0039] When the modules work together, the radio receiving module first receives the radio signal emitted by the drone. The signal processing module performs time domain and frequency domain analysis on the signal, removes noise and extracts feature information. Based on this feature information, the drone identification module uses a machine learning algorithm to identify the type, location, heading, flight altitude, and identity of the drone. The identification results and related data are stored in the data storage module for subsequent query and analysis. The display module displays the real-time identification results and drone monitoring information on the user terminal. The control module is responsible for coordinating the normal operation of each module.

[0040] The radio receiving module covers a frequency band range based on a receiving unit, the frequency band including low frequency, medium frequency, high frequency, microwave and different frequency bands, and the receiving unit includes a directional antenna and an omnidirectional antenna.

[0041] The detection capability of various types of drones has been improved. At the same time, the directional antenna can be used to accurately locate the direction of the drone, and the omnidirectional antenna can ensure that the signal is received in all directions, which improves the sensitivity and reliability of the system, thereby enhancing the accuracy and stability of drone identification.

[0042] The signal processing module includes a spectrum analysis unit, which is used to identify characteristic patterns of signals in a multi-dimensional signal space. The drone identification module adopts a deep learning model and a reinforcement learning algorithm.

[0043] The accuracy and robustness of signal analysis are improved, and the drone identification module adopts deep learning models and reinforcement learning algorithms, which can automatically optimize recognition capabilities based on rich training data, accurately identify the type, location, status and other information of the drone, and continuously improve the adaptability and decision-making efficiency in dynamic environments through reinforcement learning, significantly enhancing the system's recognition performance in complex and changing environments.

[0044] The data storage module includes a dynamic database, which is used to update and store the latest feature information and behavior patterns of different types of drones in real time.

[0045] This ensures that the system always has the latest drone data, which not only improves the accuracy of drone identification, but also promptly reflects the characteristics and behavioral changes of new drones, enhances the system's ability to identify unknown or changing types of drones, and thus improves the system's adaptability and long-term reliability.

[0046] The display module includes an alarm unit, and the alarm unit includes a low risk alarm, a medium risk alarm, and a high risk alarm.

[0047] By setting alarm units for low-risk, medium-risk, and high-risk alerts, the display module can issue different levels of alarms in real time according to the drone's flight status and potential threat level, helping users to promptly assess and respond to situations of different risk levels, ensuring that users can make quick decisions based on actual risks, and improving the safety and practicality of the system.

[0048] In addition, the present invention also provides a method for using the above-mentioned radio-based drone identification system, the method comprising:

[0049] Step S1: receiving a radio signal from a UAV through a radio receiving module;

[0050] Step S2: demodulating, denoising and extracting features of the received radio signal through the signal processing module;

[0051] Step S3: using the drone identification module to identify the type, location, heading, and identity characteristics of the drone based on the characteristic information;

[0052] Step S4: storing the recognition result in a data storage module;

[0053] Step S5: Display the recognition result through the display module and perform real-time monitoring.

[0054] The drone signal is received through the radio receiving module. After demodulation, denoising and feature extraction by the signal processing module, the drone identification module is used to identify the type, location, heading and identity characteristics of the drone. The identification results are then stored in the data storage module and displayed in real time through the display module, realizing efficient monitoring and management of the drone.

[0055] In step S1, the radio signal includes the remote control signal, communication signal and position positioning signal of the drone.

[0056] It can obtain more comprehensive drone information and enhance the comprehensive monitoring capability of drone behavior. The multi-signal source reception method helps to improve the accuracy and reliability of identification, enabling the system to more accurately locate drones, track their flight status, and identify potential threats.

[0057] In step S2, the signal processing module extracts the time-frequency characteristics of the signal based on spectrum analysis technology.

[0058] The accuracy and robustness of signal processing have been improved, enabling the system to more accurately identify the behavior patterns and characteristics of drones, and enhancing the ability to process signals in complex environments, thereby improving the performance of the overall identification and monitoring system.

[0059] In step S3, the recognition module uses a pattern recognition algorithm to classify the drone type based on the characteristic pattern of the radio signal.

[0060] By utilizing the uniqueness of signal characteristics, various types of drones can be effectively distinguished, improving the accuracy and efficiency of identification. Especially in complex environments, the type of drone can be quickly and accurately determined, thereby providing reliable data support for subsequent monitoring and response.

[0061] In step S4, the recognition results include but are not limited to the real-time location, flight trajectory, heading information, flight time, recognition time and safety hazards of the drone, and the data storage module supports cross-platform storage and sharing.

[0062] The system can achieve efficient management and intercommunication of data. The cross-platform storage and sharing capabilities enable real-time access and update of identification data between different systems and devices, enhancing data availability and collaboration.

[0063] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A UAV identification system based on radio technology, characterized in that: The system includes: Radio receiving module: used to receive radio signals from the drone; Signal processing module: used to perform time domain and frequency domain analysis on the received radio signal, remove environmental noise, and extract unique electromagnetic characteristic information of the UAV; UAV identification module: used to identify the type, location, heading, flight altitude and identity characteristics of the UAV based on the radio signal characteristics using a machine learning algorithm; Data storage module: used to store real-time recognition results, historical signal data and characteristic information of known drones; Display module: used to display real-time recognition results and drone monitoring information on the user terminal, including flight trajectory, flight plan and safety alerts; Control module: used to coordinate the operations of various modules.

2. The UAV identification system based on radio technology according to claim 1, characterized in that: The radio receiving module covers a frequency band range based on a receiving unit, the frequency band including low frequency, medium frequency, high frequency, microwave and different frequency bands, and the receiving unit includes a directional antenna and an omnidirectional antenna.

3. The UAV identification system based on radio technology according to claim 1, characterized in that: The signal processing module includes a spectrum analysis unit, which is used to identify characteristic patterns of signals in a multi-dimensional signal space. The drone identification module adopts a deep learning model and a reinforcement learning algorithm.

4. The UAV identification system based on radio technology according to claim 1, characterized in that: The data storage module includes a dynamic database, which is used to update and store the latest feature information and behavior patterns of different types of drones in real time.

5. The UAV identification system based on radio technology according to claim 1, characterized in that: The display module includes an alarm unit, and the alarm unit includes a low risk alarm, a medium risk alarm, and a high risk alarm.

6. A method for using the radio technology-based drone identification system according to any one of claims 1 to 5, characterized in that: The method includes: Step S1: receiving a radio signal from a UAV through a radio receiving module; Step S2: demodulating, denoising and extracting features of the received radio signal through the signal processing module; Step S3: using the drone identification module to identify the type, location, heading, and identity characteristics of the drone based on the characteristic information; Step S4: storing the recognition result in a data storage module; Step S5: Display the recognition result through the display module and perform real-time monitoring.

7. The method for identifying drones based on radio technology according to claim 6, characterized in that: In step S1, the radio signal includes the remote control signal, communication signal and position positioning signal of the drone.

8. The method for identifying drones based on radio technology according to claim 6, characterized in that: In step S2, the signal processing module extracts the time-frequency characteristics of the signal based on spectrum analysis technology.

9. The method for identifying drones based on radio technology according to claim 6, characterized in that: In step S3, the recognition module uses a pattern recognition algorithm to classify the drone type based on the characteristic pattern of the radio signal.

10. The method for identifying drones based on radio technology according to claim 6, characterized in that: In step S4, the recognition results include but are not limited to the real-time location, flight trajectory, heading information, flight time, recognition time and safety hazards of the drone, and the data storage module supports cross-platform storage and sharing.

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