A method, device and equipment for detecting unmanned aerial vehicles based on data enhancement

Abstract

The present application discloses a method, device and equipment for detecting drones based on data enhancement. The method includes data enhancement in the machine learning of drone signals, including: automatically collecting the original drone signals, performing data enhancement on the collected original signals according to data enhancement parameters to obtain enhanced drone signals, and sending the enhanced drone signals to a neural network for training to obtain a drone recognition model. In data enhancement, the original drone signals are subjected to channelization transformation, signal-to-interference ratio transformation, signal-to-noise ratio transformation and carrier frequency deviation transformation to obtain enhanced drone signals. The present application improves the diversity of drone training signals through data enhancement technology to cover various possible scenarios, reduces the complexity of drone signal acquisition, and shortens the development time to support drone detection.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence and signal processing, and specifically to a drone detection method, device and equipment based on data enhancement, including data enhancement in drone signal machine learning. Background Art

[0002] With the explosive development of the drone industry, more and more drones are being used in various scenarios, such as legal aerial photography, express delivery, drone performances, drone competitions, agricultural production, plant protection, disaster relief, wildlife observation, surveying and mapping, news reporting, power inspections, etc. In addition to these conventional and legal scenarios, there are also some illegal applications, such as illegal intrusion, sneak photography, throwing illegal objects, etc., and there are more and more public safety risks. Due to the low price and easy access to drones, more and more people can use drones. Although many countries are paying more and more attention to the safety of drones and have gradually formulated corresponding laws and regulations to regulate them, the above-mentioned illegal applications, as well as some irregular operations and drone out of control, cannot be effectively controlled by legal means alone. Therefore, it is necessary to use professional equipment to manage them, including detection and defense. Among them, drone detection is a basic requirement for drone management.

[0003] Drone detection means that when a drone appears within the defense range, it can be detected in various ways that the drone has appeared. Currently commonly used detection methods include physical detection based on radar reflection waves, visual detection based on cameras, auditory detection based on sound, and detection based on radio signals emitted by drones. Patent application CN202111236835.3 discloses a method for measuring target characteristics by identifying background types by analyzing echo information of background clutter. In its data processing step, data of different antenna angles of the same ground feature are regarded as different serial numbers in the same group, and then the data in the group are compared to obtain the reflection spectrum of this scene for different bands; when the antenna angle is the same, different test surface features are compared with each other to obtain the similarities and differences of multiple scenes; drawing is performed to intuitively analyze the ground feature spectrum characteristics of different scenes. The experimental results can be used for the compounding of targets and complex scenes, with good recognition effect. There are also methods and devices in the prior art that use infrared image detection technology to detect and identify small targets at low altitudes and long distances within the field of view, such as drones. For example, patent CN201810694988.4 achieves effective recognition in complex backgrounds through an improved top-hat background suppression method, fractional-order target enhancement, and spectral residual method for significant target extraction.

[0004] Machine learning mainly extracts the features of input signals by training neural networks. Figure 1This is a simple diagram of machine learning, where circles represent values ​​and arrows represent operations. The essence of the neural network training process is to find the optimal operation parameters (i.e. Figure 1 The present invention is based on machine learning and data enhancement technology, generates an optimal neural network by training the neural network, and then uses the network to detect and identify the drone.

[0005] In terms of detection of radio signals transmitted by drones, drone identification methods based on data enhancement have emerged. Patent CN201910930192.9 relates to a method for identifying drone remote control signals based on frequency domain features, which performs mixing, phase detection, sampling, and channelization processing on received drone signals to obtain frequency measurement outputs of multiple channels, thereby realizing detection and identification of drone remote control signals.

[0006] However, in the neural network training process based on drone radio signals, the collection scenarios of the collected drone signals are often limited. The model obtained after training with drone signals collected in limited scenarios is difficult to accurately identify drone signals in more other scenarios, that is, the generalization ability of the obtained training model is poor. For example, a model trained with drone signals collected in scene A can correctly identify drone signals in scene A, but may not be able to identify the same drone signal in scene B. At present, most solutions are to manually collect drone signals in as many scenarios as possible through scene selection and setting. This greatly prolongs the research and development cycle of drone identification devices and equipment, and cannot solve the problem of low recognition success rate in new scenarios. Summary of the invention

[0007] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method, device and equipment for detecting drones based on data enhancement, which does not require the actual provision of drone signals in many scenarios, but only requires the collection of drone signals in some conventional scenarios, and then obtains drone signals in a variety of possible scenarios through signal transformation enhancement, and sends them to a neural network for training to obtain a drone recognition model. The advantage of the present invention is that it avoids the manual collection of signals of various scenarios, reduces the complexity of signal collection, and improves the generalization ability of the model.

[0008] Machine learning mainly extracts the features of input signals by training neural networks. The present invention uses data enhancement technology to obtain drone signals in various scenarios based on drone signals in conventional scenarios, generates an optimized neural network by training the neural network, and then detects and identifies drones through the network.

[0009] The specific technical solutions of the present invention are as follows:

[0010] In a first aspect, the present application provides a drone detection method based on data enhancement, the method comprising:

[0011] Step 1: Use the drone signal acquisition device to automatically collect the original signal of the drone.

[0012] Step 2: Use the data enhancement module to transform and enhance the collected original drone signal according to the data enhancement parameters, and use the transformed and enhanced data as the input for neural network training.

[0013] Step 3: Use the neural network module to train the transformed and enhanced drone signals to obtain a drone recognition model, and send the trained drone recognition model to the drone recognition module.

[0014] Step 4: Use the drone identification module to identify the collected signal to be identified, determine whether there is a drone signal in the signal, and output the signal identification result.

[0015] In the step 1, the original signal of the drone collected by the drone signal collection device includes the communication signal used when the drone communicates with the ground and the control end, and also includes the environmental signal.

[0016] In the step 1, after the drone signal is collected, the collected drone signal is added to the signal library.

[0017] In the step 2, data enhancement is performed on the collected drone signal, including channelization transformation, signal-to-interference ratio transformation, signal-to-noise ratio transformation, and carrier frequency deviation transformation performed in sequence according to data enhancement parameters.

[0018] A wireless channel refers to a data signal transmission channel that uses wireless signals as the transmission medium. A drone usually has multiple wireless channels. Channelization conversion is to convert the wireless signal on a certain channel into the wireless signal on any other channel.

[0019] The channelization transformation is as follows: Assume that the original signal of the drone is {x[0], x[1], x[2], ..., x[N-1]}, where N is the length of the wireless signal. Assume that the channel frequency of the original signal of the drone is f0, and the set of all channel frequencies of the signal is {f0, f1, ..., f M}, where M is the total number of channels. Assume that the signal frequency point to be transformed in the data enhancement parameter is f m , the sampling frequency is f s , then the original signal of the drone is transformed into:

[0020] y1[n]=x[n]*e-j2*πn* (f m -f0) / f s

[0021] The signal-to-interference ratio transformation is to simulate the actual situation that the receiving end in the wireless communication system receives both the useful signal and the interference signal when receiving the drone signal. The signal-to-interference ratio transformation is specifically:

[0022] Assume that the required signal-to-interference ratio is α, and the interference signal is {I[0], I[1], I[2], ..., I[N-1]}, then

[0023]

[0024] in:

[0025] Then, the drone signal is transformed into:

[0026]

[0027] The signal-to-noise ratio transformation is to simulate the attenuation effect of the wireless signal transmission from the drone reaching the receiving end due to the different degrees of attenuation due to the transmission distance. The signal-to-noise ratio transformation is specifically:

[0028] Assume the noise power is P noise , the required signal-to-noise ratio is β, then:

[0029]

[0030] in:

[0031] Then, the drone signal is transformed into:

[0032]

[0033] in, The power is P noise is a cyclically symmetric zero-mean complex Gaussian random variable.

[0034] The carrier frequency deviation transformation is to simulate the carrier frequency deviation δf between the receiving end and the transmitting end due to the deviation of the local oscillator and the Doppler frequency shift in the wireless communication system. The carrier frequency deviation has a range [δf min ,δf max ]. The carrier frequency deviation transformation is specifically as follows:

[0035] A deviation δf is randomly selected within the deviation range, and then the drone signal is transformed according to the variation as follows:

[0036]

[0037] In step 4, the drone identification module stores a drone identification model that can be used to identify drone signals in multiple scenarios, that is, the drone identification model trained in step 3. When a drone communication signal is received, the drone identification module will identify the received drone communication signal through the stored drone identification model and output the drone communication signal identification result.

[0038] In a second aspect, the present application provides a drone detection device based on data enhancement, the device comprising:

[0039] The UAV signal acquisition module automatically collects the original signals of the UAV.

[0040] The signal library is connected to the UAV signal acquisition module and the data enhancement module, and is used to store the collected original UAV signals and send the stored UAV signals to the data enhancement module.

[0041] The data enhancement module transforms and enhances the collected signals, and uses the transformed and enhanced data as the input for neural network training.

[0042] The neural network module is connected to the data enhancement module and is used to train the transformed and enhanced drone signals to obtain a drone recognition model.

[0043] The drone identification module is used to identify the drone. The neural network module also sends the trained drone identification model to the drone identification module to enable the drone identification module to have the ability to identify the drone. The drone identification module stores the drone identification model trained by the neural network module, and uses the drone identification model to identify the collected signals to be distinguished.

[0044] The original signal of the drone collected by the drone signal collection module includes the communication signal used by the drone to communicate with the ground and the control end, and also includes the environmental signal. The method and steps of the automatic collection are the same as those mentioned above and will not be repeated here.

[0045] The data enhancement module performs data enhancement on the collected original signal of the drone, and includes a channelization transformation module, a signal-to-interference ratio transformation module, a signal-to-noise ratio transformation module, and a carrier frequency deviation transformation module which are arranged in sequence.

[0046] The channelization transformation module, signal-to-interference ratio transformation module, signal-to-noise ratio transformation module, and carrier frequency deviation transformation module respectively implement the channelization transformation, signal-to-interference ratio transformation, signal-to-noise ratio transformation, and carrier frequency deviation transformation as described above according to the data enhancement parameters, and will not be repeated here.

[0047] The drone identification module stores a drone identification model that can be used to identify drone signals in various scenarios. When a drone communication signal is received, the drone identification module will identify the received drone communication signal through the stored drone identification model and output the drone communication signal identification result.

[0048] In a third aspect, the present application also provides a drone detection device based on data enhancement, wherein the device includes a memory and a processor.

[0049] The memory is used to store program codes and transmit the program codes to the processor.

[0050] The processor is used to execute the drone detection method based on data enhancement described in the first aspect according to the instructions in the program code.

[0051] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the drone detection method based on data enhancement described in the first aspect.

[0052] The beneficial effects of this application are:

[0053] 1. This application can identify the original signals of drones in various scenarios, avoids the manual collection of signals in various scenarios, reduces the complexity of signal collection, improves the generalization ability of the model, greatly improves the success rate of drone identification in new scenarios, and shortens the development time of supporting drone detection.

[0054] 2. In the data enhancement link, the present application sequentially performs channelization transformation, signal-to-interference ratio transformation, signal-to-noise ratio transformation, and carrier frequency deviation transformation on the original signal. The drone recognition model obtained by training the data after channelization transformation can accurately identify drone signals with continuously changing communication channels. The signal-to-interference ratio transformation is used for data enhancement, so that the drone recognition model can recognize drone signals under various interferences. The signal-to-noise ratio transformation is used for data enhancement, so that the drone recognition model can recognize drone signals after different degrees of attenuation. The carrier frequency deviation transformation is used for data enhancement, so that the drone recognition model can recognize drone signals under different carrier frequency deviations. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0056] Figure 1 It is a structural diagram of a neural network model in a drone detection device based on data enhancement;

[0057] Figure 2 A flowchart of a drone detection method based on data enhancement provided in an embodiment of the present application;

[0058] Figure 3 A schematic diagram of the structure of a data enhancement device for drone signal machine learning provided in an embodiment of the present application;

[0059] Figure 4 A schematic diagram of the structure of a drone detection device based on data enhancement provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0061] In the current neural network training process based on drone signals, the actual scenarios in the signal collection process are often limited. The model obtained after training with drone signals collected in limited scenarios is difficult to accurately identify drone signals in more scenarios, and the generalization ability of the obtained training model is poor. The existing solution is to manually collect drone signals in as many scenarios as possible through scene selection and setting. This greatly prolongs the research and development cycle of drone identification devices and equipment, and cannot solve the problem of low recognition success rate in new scenarios.

[0062] To this end, this application provides a data enhancement method for drone signal machine learning, which only needs to collect drone signals in some common scenarios, and then obtain drone signals in multiple possible scenarios through signal transformation enhancement, so that the drone recognition model obtained after training can recognize drone signals in complex scenarios.

[0063] The following specifically introduces an embodiment of a data enhancement method for drone signal machine learning and a drone detection method provided by the present application, with reference to Figure 2 , is a flow chart of a method provided in an embodiment of the present application, the method specifically includes:

[0064] S101: Automatically collect the original signal of the drone using the drone signal collection device.

[0065] In the embodiment of the present application, the original signal of the drone collected by the drone signal collection device includes the communication signal used by the drone to communicate with the ground and the control end, and also includes the environmental signal. In addition, after the signal is collected, the collected signal is added to the signal library.

[0066] S102: Using the data enhancement module, transform and enhance the collected original signal of the drone according to the data enhancement parameters, and use the transformed and enhanced data as the input of the neural network training.

[0067] S103: Using a neural network module to train the transformed and enhanced drone signal to obtain a drone recognition model, and sending the trained drone recognition model to a drone recognition module.

[0068] S104: Using the drone identification module, identify the collected signal to be identified, determine whether there is a drone signal in the signal, and output the identification result.

[0069] In S102, data enhancement is performed on the collected signal, including channelization transformation, signal-to-interference ratio transformation, signal-to-noise ratio transformation, and carrier frequency deviation transformation performed in sequence according to data enhancement parameters.

[0070] A wireless channel refers to a data signal transmission channel that uses wireless signals as the transmission medium. A drone usually has multiple wireless channels. Channelization conversion is to convert the wireless signal on a certain channel into the signal on any other channel.

[0071] The channelization transformation is as follows: Assume that the original signal of the drone is {x[0], x[1], x[2], ..., x[N-1]}, where N is the length of the wireless signal. Assume that the channel frequency of the original signal is f0, and the set of all channel frequencies of the signal is {f0, f1, ..., f M}, where M is the total number of channels. Assume that the signal frequency point to be transformed in the data enhancement parameter is f m , the sampling frequency is f s , then the original signal of the drone is transformed into:

[0072] y1[n]=x[n]*e -j2*πn* (f m -f0) / f s

[0073] The signal-to-interference ratio transformation is to simulate the actual situation that the receiving end in the wireless communication system receives both the useful signal and the interference signal when receiving the drone signal. The signal-to-interference ratio transformation is specifically:

[0074] Assume that the required signal-to-interference ratio is α, and the interference signal is {I[0], I[1], I[2], ..., I[N-1]}, then

[0075]

[0076] in:

[0077] Then, the drone signal is transformed into:

[0078]

[0079] The signal-to-noise ratio conversion is to simulate the attenuation effect of different degrees of attenuation due to the transmission distance when the wireless signal reaches the receiving end. The signal-to-noise ratio conversion is specifically:

[0080] Assume the noise power is P noise , the required signal-to-noise ratio is β, then:

[0081]

[0082] in:

[0083] Then, the drone signal is transformed into:

[0084]

[0085] in, The power is P noise is a cyclically symmetric zero-mean complex Gaussian random variable.

[0086] The carrier frequency deviation transformation is to simulate the carrier frequency deviation δf between the receiving end and the transmitting end due to the deviation of the local oscillator and the Doppler frequency shift in the wireless communication system. The carrier frequency deviation has a range [δf min ,δf max ]. The carrier frequency deviation transformation is specifically as follows:

[0087] A deviation δf is randomly selected within the deviation range, and then the drone signal is transformed according to the variation as follows:

[0088]

[0089] In an embodiment of the present application, the drone identification module stores a drone identification model that can be used to identify drone signals in multiple scenarios, that is, the drone identification model obtained by training in S103. When a drone communication signal is received, the drone identification module will identify the received drone communication signal through the stored drone identification model and output the drone communication signal identification result.

[0090] Corresponding to the above method embodiments, the present application also provides a data enhancement device in drone signal machine learning, the device comprising:

[0091] The UAV signal acquisition module automatically collects the original signals of the UAV.

[0092] The signal library is connected to the UAV signal acquisition module and the data enhancement module, and is used to store the collected original UAV signals and send the stored UAV signals to the data enhancement module.

[0093] The data enhancement module transforms and enhances the collected signals, and uses the transformed and enhanced data as the input for neural network training.

[0094] The neural network module is connected to the data enhancement module and is used to train the transformed and enhanced drone signals to obtain a drone recognition model.

[0095] The UAV identification module is used to identify the UAV. The neural network module also sends the trained UAV identification model to the UAV identification module so that the UAV identification module has the ability to identify the UAV.

[0096] The drone identification module stores a drone identification model obtained by training the neural network module, and the collected signals to be distinguished are identified through the drone identification model.

[0097] The original signal of the drone collected by the drone signal collection module includes the communication signal used by the drone to communicate with the ground and the control end, and also includes the environmental signal. The method and steps of the automatic collection are the same as those mentioned above and will not be repeated here.

[0098] The data enhancement module performs data enhancement on the collected signal, including a channelization conversion module, a signal-to-interference ratio conversion module, a signal-to-noise ratio conversion module, and a carrier frequency deviation conversion module which are arranged in sequence. Figure 3 described.

[0099] The channelization transformation module, signal-to-interference ratio transformation module, signal-to-noise ratio transformation module, and carrier frequency deviation transformation module respectively implement the channelization transformation, signal-to-interference ratio transformation, signal-to-noise ratio transformation, and carrier frequency deviation transformation as described above based on the data enhancement parameters, which will not be repeated here.

[0100] The drone identification module stores a drone identification model that can be used to identify drone signals in various scenarios. When a drone communication signal is received, the drone identification module will identify the received drone communication signal through the stored drone identification model and output the drone communication signal identification result.

[0101] The embodiment of the present invention also provides a drone detection device based on data enhancement, see Figure 4 As shown, it may include:

[0102] Processor 401, memory 402, input device 403 and output device 404. The number of processors 401 in the drone detection device based on data enhancement can be one or more. Figure 4 In some embodiments of the present invention, the processor 401, the memory 402, the input device 403 and the output device 404 may be connected via a bus or other means, wherein: Figure 4 The example of connecting through bus is taken in the following.

[0103] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing of the drone detection device based on data enhancement by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. The input device 403 may be used to receive input digital or character information, and to generate signal input related to user settings and function control of the drone detection device based on data enhancement.

[0104] Specifically in this embodiment, the processor 401 will load the executable files corresponding to the processes of one or more applications into the memory 402 according to the following instructions, and the processor 401 will run the applications stored in the memory 402, thereby realizing various functions in the above-mentioned drone detection method based on data enhancement.

[0105] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0106] It should be noted that, in this article, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or equipment that includes a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment.

[0107] The above is a detailed introduction to a drone detection and identification method, device and equipment based on data enhancement provided in the embodiments of the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A drone detection method based on data enhancement, characterized in that: The method comprises: Automatically collect the original signal of the drone; Perform data enhancement on the collected original signals of the drone, and use the enhanced data as the input for neural network training; The data enhancement specifically includes: performing channelization transformation, signal-to-interference ratio transformation, signal-to-noise ratio transformation, and carrier frequency deviation transformation in sequence according to the data enhancement parameters; Sending the enhanced data to a neural network for training to obtain a drone recognition model, and sending the trained drone recognition model to a drone recognition module; and The collected signal to be identified is sent to the drone identification module to determine whether there is a drone signal in the signal and output the identification result; in, The channelization transformation comprises: The original signal collected is {x[0],x[1],x[2],...,x[N-1]}, where N is the signal length, the channel frequency of the original signal is f0, and the set of all channel frequencies of the signal is {f0,f1,...,f M }, where M is the total number of channels, and the signal frequency point that needs to be transformed in the data enhancement parameter is f m , the sampling frequency is f s , the channelization transform transforms the original signal x[n] into y1[n]: The signal-to-interference ratio transformation includes: The channelization transformation obtains y1[n], the target signal-to-interference ratio of the signal-to-interference ratio transformation is α, and the interference signal is {I[0], I[1], I[2], ..., I[N-1]}. The signal-to-interference ratio transformation transforms the signal y1[n] into y2[n]: in, The signal-to-noise ratio transformation comprises: The signal-to-interference ratio is transformed to obtain the signal y2[n], and the power of the noise is P noise , the target signal-to-noise ratio is β, and the signal-to-noise ratio transformation transforms the signal y2[n] into y3[n]: in, The power is P noise A cyclically symmetric zero-mean complex Gaussian random variable; The carrier frequency deviation transformation includes: The signal-to-noise ratio transformation obtains the signal y3[n], and the carrier frequency deviation is δf. The carrier frequency deviation transformation transforms the signal y3[n] into y4[n]: Among them, f s is the sampling frequency.

2. The method according to claim 1, characterized in that The original signal of the UAV includes the communication signal used by the UAV to communicate with the ground and the control end.

3. A drone detection device based on data enhancement, characterized in that: The device comprises: The UAV signal acquisition module automatically collects the original signal of the UAV; The signal library is connected to the drone signal acquisition module and the data enhancement module, and is used to store the collected drone original signals and send the stored drone signals to the data enhancement module; The data enhancement module transforms and enhances the collected original signals of the drone, and uses the transformed and enhanced data as the input for neural network training; The data enhancement specifically includes: performing channelization transformation, signal-to-interference ratio transformation, signal-to-noise ratio transformation, and carrier frequency deviation transformation in sequence according to the data enhancement parameters; A neural network module, connected to the data enhancement module, for training the transformed and enhanced drone signal to obtain the drone recognition model; and UAV identification module, used to identify the UAV; in, The channelization transformation comprises: The original signal collected is {x[0],x[1],x[2],...,x[N-1]}, where N is the signal length, the channel frequency of the original signal is f0, and the set of all channel frequencies of the signal is {f0,f1,...,f M }, where M is the total number of channels, and the signal frequency point that needs to be transformed in the data enhancement parameter is f m , the sampling frequency is f s , the channelization transform transforms the original signal x[n] into y1[n]: The signal-to-interference ratio transformation includes: The channelization transformation obtains y1[n], the target signal-to-interference ratio of the signal-to-interference ratio transformation is α, and the interference signal is {I[0], I[1], I[2], ..., I[N-1]}. The signal-to-interference ratio transformation transforms the signal y1[n] into y2[n]: in, The signal-to-noise ratio transformation comprises: The signal-to-interference ratio is transformed to obtain the signal y2[n], and the power of the noise is P noise , the target signal-to-noise ratio is β, and the signal-to-noise ratio transformation transforms the signal y2[n] into y3[n]: in, The power is P noise A cyclically symmetric zero-mean complex Gaussian random variable; The carrier frequency deviation transformation includes: The signal-to-noise ratio transformation obtains the signal y3[n], and the carrier frequency deviation is δf. The carrier frequency deviation transformation transforms the signal y3[n] into y4[n]: Among them, f s is the sampling frequency.

4. The device according to claim 3, characterized in that The drone identification module stores a drone identification model obtained by training the neural network module, and the collected signals to be distinguished are identified through the drone identification model.

5. A drone detection device based on data enhancement, characterized in that: The device comprises a memory and a processor; The memory is used to store program codes and transmit the program codes to the processor; The processor is configured to execute the method according to any one of claims 1 to 2 according to the instructions in the program code.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 2 are implemented.

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