Unmanned aerial vehicle accurate identification method and device, storage medium and product

Abstract

The invention discloses an unmanned aerial vehicle accurate identification method and device, a storage medium and a product, and the identification method comprises the steps: carrying out the Fourier transform of an image transmission signal of an unmanned aerial vehicle, and obtaining a time-frequency graph; preprocessing and segmenting the image transmission signal according to the time-frequency graph to obtain a plurality of baseband signal segments containing lead codes or pilot frequency sequences; determining a subcarrier of an OFDM symbol of a lead code or a pilot frequency sequence in each baseband signal segment and a subcarrier carrying data, and generating a local sequence of each baseband signal segment; calculating a normalized correlation sequence of each baseband signal segment and each local sequence thereof, and further determining a generation mode of a lead code or a pilot frequency sequence in each baseband signal segment; and realizing accurate identification of the unmanned aerial vehicle according to the generation modes of the lead codes or the pilot frequency sequences in all the baseband signal segments. According to the invention, accurate identification of the unmanned aerial vehicle is realized without acquiring the unique serial number of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention belongs to the technical field of drone detection, and in particular relates to a method, device, storage medium and product for accurately identifying drones. Background Art

[0002] At present, there are many spectrum detection devices on the market. These detection devices are generally passive detection devices. They detect and identify drones through relevant algorithms. However, when spectrum detection devices detect drones, they usually cannot directly determine whether the drone has been detected before. Since drones may appear many times within the spectrum range, and each drone signal may be affected by factors such as the environment, flight status, and interference, the accuracy and consistency of its identification cannot be guaranteed.

[0003] With the increasing number of drones in low-altitude airspace, the Civil Aviation Administration has introduced a management system to avoid conflicts between drones and other aircraft and ensure the safety of low-altitude airspace, requiring drones to register a unique serial number when they are produced, reporting the drone serial number and flight data, and restricting no-fly zones. Although drones have fixed identity information, not all organizations can obtain it, so when drones are flown indiscriminately, safety accidents or privacy leaks may still occur.

[0004] In order to accurately identify drones, some people are currently using the method of cracking the WiFi signal or broadcast signal to try to parse out its original serial number. However, there are many manufacturers of drones and many types of drones. Not all drones contain broadcast signals, WiFi signals and unique serial numbers, especially some lightweight drones. This makes it impossible to accurately identify drones without knowing their unique serial numbers. Summary of the invention

[0005] The purpose of the present invention is to provide a method, device, storage medium and product for accurately identifying a drone, so as to solve the problem that when the unique serial number of the drone cannot be known, it is impossible to accurately identify whether the drone has been detected by the detection equipment before.

[0006] The present invention solves the above technical problems through the following technical solutions: a method for accurately identifying drones, comprising:

[0007] Get the image transmission signal of the drone;

[0008] Performing Fourier transform on the image transmission signal to obtain a time-frequency diagram;

[0009] Preprocessing and segmenting the image transmission signal according to the time-frequency diagram to obtain a plurality of baseband signal segments including a preamble code or a pilot sequence;

[0010] Determine, according to the time-frequency diagram, the subcarriers of the OFDM symbols of the preamble or pilot sequence and the subcarriers carrying data in each baseband signal segment;

[0011] Generate a local sequence for each baseband signal segment according to the subcarriers of the OFDM symbol of the preamble or pilot sequence in each baseband signal segment, the subcarriers carrying data, and the carried data;

[0012] Calculating a normalized correlation sequence between each baseband signal segment and each of its local sequences;

[0013] Determine a generation method of a preamble or a pilot sequence in each baseband signal segment according to a normalized correlation sequence between each baseband signal segment and each local sequence thereof;

[0014] Accurate identification of drones is achieved based on the generation method of preamble codes or pilot sequences in all baseband signal segments.

[0015] Further, according to the time-frequency diagram, the image transmission signal is preprocessed and segmented, specifically including:

[0016] Extracting key information of the image transmission signal from the time-frequency diagram; wherein the key information includes bandwidth, frequency band, center frequency, data starting point and data end point;

[0017] Down-converting the image transmission signal according to the frequency band and the center frequency;

[0018] Designing a filter according to the bandwidth, and using the filter to filter the down-converted image transmission signal;

[0019] The filtered image transmission signal is subjected to time domain extraction to obtain a baseband signal; wherein the extraction multiple is determined according to the sampling rate and bandwidth;

[0020] According to the data starting point and the extraction multiple, the baseband signal is evenly divided according to the time window to obtain multiple baseband signal segments containing preamble codes or pilot sequences; wherein the length of the time window is the interval between two pilot sequences in the baseband signal.

[0021] Furthermore, a pre-trained deep learning model is used to extract key information of the image transmission signal from the time-frequency diagram, and the training process of the deep learning model includes:

[0022] Constructing a sample data set; wherein the samples in the sample data set include a time-frequency graph and key information thereof;

[0023] Deploy a deep learning model and use the sample data set to train, verify and evaluate the deep learning model.

[0024] Further, according to the time-frequency diagram, determining the subcarriers of the OFDM symbols of the preamble or pilot sequence and the subcarriers carrying data in each baseband signal segment specifically includes:

[0025] Determine the position of the preamble or pilot sequence in each baseband signal segment according to the time-frequency diagram;

[0026] Extracting the preamble code or pilot sequence in each baseband signal segment based on the position of the preamble code or pilot sequence in each baseband signal segment;

[0027] Performing Fourier transform on the preamble code or pilot sequence in each baseband signal segment to obtain the subcarrier of the OFDM symbol of the preamble code or pilot sequence in each baseband signal segment;

[0028] The subcarriers carrying data are determined according to the subcarriers of the OFDM symbols of the preamble or pilot sequence in each baseband signal segment.

[0029] Furthermore, the specific calculation formula for the normalized correlation sequence between each baseband signal segment and each of its local sequences is:

[0030] ;

[0031] ;

[0032] in, Indicates the baseband signal segment With its local sequence The normalized correlation sequence of Indicates the baseband signal segment With its local sequence The related sequence of represents the baseband signal segment, represents the local sequence of baseband signal segments, Indicates delay, represents the index of discrete time, Indicates the baseband signal segment Length, Represents a local sequence Length.

[0033] Further, determining a generation method of a preamble or a pilot sequence in each baseband signal segment according to a normalized correlation sequence between each baseband signal segment and each local sequence thereof specifically includes:

[0034] For each baseband signal segment, take the maximum normalized correlation value in each normalized correlation sequence, and compare the maximum normalized correlation value with a set threshold; if the maximum normalized correlation value exceeds the set threshold, record the maximum normalized correlation value and the local sequence corresponding to the maximum normalized correlation value; otherwise, discard the normalized correlation sequence;

[0035] If multiple maximum normalized correlation values ​​and their corresponding local sequences are recorded, the largest one is selected from the multiple maximum normalized correlation values;

[0036] Determine the subcarriers of the OFDM symbols of the preamble or pilot sequence in the baseband signal segment, the subcarriers carrying data, and the data carried by the subcarriers according to the local sequence corresponding to the largest one;

[0037] The generation method of the preamble code or pilot sequence in the baseband signal segment is determined according to the subcarriers of the OFDM symbols of the preamble code or pilot sequence in the baseband signal segment, the subcarriers carrying data, and the data carried by the subcarriers.

[0038] Based on the same concept, the present invention also provides an electronic device, including a memory, a processor, and a computer program / instruction stored in the memory, wherein the processor executes the computer program / instruction to implement the above-mentioned method for accurately identifying a drone.

[0039] Based on the same concept, the present invention also provides a computer-readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the method for accurately identifying a drone as described above is implemented.

[0040] Based on the same concept, the present invention also provides a computer program product, including a computer program / instruction, which implements the above-mentioned method for accurately identifying drones when executed by a processor.

[0041] Compared with the prior art, the advantages of the present invention are:

[0042] The present invention analyzes the image transmission signal, determines the generation method of the preamble code or pilot sequence in each baseband signal segment, and then determines the type of generation method of the preamble code or pilot sequence in the entire baseband signal. Combined with the characteristics that the types of generation methods of the preamble code or pilot sequence in the baseband signals of different drones are not completely the same, it can accurately identify whether the drone has been detected by the detection equipment before. The method of the present invention cooperates with the spectrum detection equipment to accurately identify the drone and whether the drone has been detected before, ensuring the accuracy and uniqueness of the identification result, and realizing the accurate identification of the drone without knowing the unique serial number of the drone.

[0043] The present invention achieves accurate identification of drones by analyzing the image transmission signal to obtain the type of generation method of the preamble code or pilot sequence in the baseband signal, thereby increasing the distance of drone identification and achieving accurate identification of long-distance drones. The identification process of the present invention is relatively simple, with a small amount of calculation, and can be processed in parallel with the spectrum detection equipment to identify the drone, thereby shortening the identification time. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 1 is a flow chart of a method for accurately identifying a drone in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following is a clear and complete description of the technical solutions in the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0048] Embodiment 1

[0049] In order to solve the problem that it is impossible to accurately identify whether a drone has been detected by a detection device before when the unique serial number of the drone cannot be known, the present invention provides a method for accurately identifying drones, which analyzes image transmission signals to determine the generation method of a preamble code or a pilot sequence in each baseband signal segment, and based on the characteristic that the types of generation methods of preamble codes or pilot sequences in baseband signals of different drones are not exactly the same, accurately identify whether the drone has been detected by a detection device before.

[0050] Figure 1 FIG. 2 shows a flow chart of a method for accurately identifying a drone in an embodiment of the present invention. Figure 1 As shown, the method for accurately identifying a drone includes the following steps:

[0051] Step S1: Obtain the image transmission signal of the drone.

[0052] The drone communication link is divided into an uplink communication link and a downlink communication link. The uplink communication link is used for communication from the ground to the drone, mainly transmitting drone flight control data and sensor control data; the downlink communication link is used for communication from the drone to the ground, mainly transmitting the drone's path, speed, distance and other navigation information. The image transmission signal of the drone in this embodiment belongs to the downlink communication link signal.

[0053] When the drone is running, the drone detection device will perform passive detection. When a drone is detected, the drone detection device saves the image transmission signal. When saving, the real part and the imaginary part of the image transmission signal are saved separately; when applied, the real part and the imaginary part are combined into a complex signal. The image transmission signal of this embodiment is a complex signal.

[0054] Step S2: Perform Fourier transform on the image transmission signal to obtain a time-frequency diagram.

[0055] When performing Fourier transform, the number of Fourier transform points (i.e., the number of FFT points) can be selected. Usually, in order to improve calculation efficiency, the number of FFT points can be selected as a power of 2. In this embodiment, 1024 data points are selected as the number of points for a Fourier transform, i.e., the number of FFT points is 1024.

[0056] Step S3: Preprocess and segment the image transmission signal according to the time-frequency diagram to obtain multiple baseband signal segments containing preamble codes or pilot sequences.

[0057] In a specific implementation of the present invention, the image transmission signal is preprocessed and segmented according to the time-frequency diagram, specifically including:

[0058] Step S3.1: extracting key information of the image transmission signal from the time-frequency diagram; wherein the key information includes the bandwidth, frequency band, center frequency, data starting point and data end point of the image transmission signal.

[0059] In this embodiment, a pre-trained deep learning model is used to extract key information of the image transmission signal from the time-frequency diagram. The training process of the deep learning model includes:

[0060] Step S3.11: Construct a sample dataset.

[0061] Collect the time-frequency diagrams of the image transmission signals of different types of drones, including but not limited to the time-frequency diagrams of the image transmission signals of drones of different manufacturers, different models, different frequency bands, different modulation methods, and different power levels. Manually annotate the collected time-frequency diagrams, including the bandwidth, frequency band, center frequency, data start point, and data end point. Construct a sample data set based on the time-frequency diagram and its annotations. Each sample in the sample data set includes the time-frequency diagram and its bandwidth, frequency band, center frequency, data start point, and data end point.

[0062] The sample data set is divided into a training set and a validation set. The training set is used to train the deep learning model, and the validation set is used to validate the deep learning model.

[0063] Step S3.12: Deploy the deep learning model and use the sample data set to train, verify and evaluate the deep learning model.

[0064] According to the characteristics of sample data and the effect of key information extraction, select a suitable deep learning model, such as a convolutional neural network and a long short-term memory hybrid network, or a convolutional neural network and a recurrent neural network. Use the time-frequency graph as the input of the deep learning model, and the corresponding annotation as the expected output of the deep learning model to train the deep learning model. During the training process, use the mean square error loss function to calculate the loss error, and adjust the parameters of the deep learning model according to the loss error to achieve the training of the deep learning model. If the loss error does not decrease significantly in multiple consecutive training cycles, or the set number of training times is reached, stop training to avoid overfitting.

[0065] Evaluate the trained and verified deep learning model, and determine whether it is necessary to increase the sample data set and continue training based on the evaluation results until the performance evaluation requirements are met.

[0066] Step S3.2: Down-convert the image transmission signal according to the frequency band and the center frequency.

[0067] In order to observe the down-conversion process more clearly in the frequency domain, the down-converted image transmission signal is subjected to Fourier transform to obtain the corresponding time-frequency diagram.

[0068] Step S3.3: Design a filter according to the bandwidth, and use the filter to filter the down-converted image transmission signal.

[0069] Different filter coefficients are set according to the bandwidth, and then the corresponding filter is designed. Then the filter is used to perform low-pass filtering on the down-converted image transmission signal to filter out the interference signal outside the band. In order to better observe the filtering effect, the filtered image transmission signal is Fourier transformed to obtain the corresponding time-frequency diagram.

[0070] Step S3.4: Perform time domain extraction on the filtered image transmission signal to obtain a baseband signal.

[0071] The decimation multiple is determined according to the sampling rate and bandwidth, and the filtered image transmission signal is decimated in the time domain to reduce the calculation amount of the related sequence. In order to better observe the time-frequency decimation effect, the baseband signal is Fourier transformed to obtain the corresponding time-frequency diagram.

[0072] Step S3.5: According to the data starting point and the decimation multiple, the baseband signal is evenly divided according to the time window to obtain multiple baseband signal segments containing preamble codes or pilot sequences.

[0073] The length of the time window is the interval between two pilot sequences in the baseband signal, and the length of the baseband signal segment is equal to the length of the time window. For example, assuming that the data starting point of the image transmission signal output by the deep learning model is 1000, the interval of the pilot sequence in the baseband signal is 2000, and the decimation multiple is 2, then the data starting point of the baseband signal is 500, and starting from the 500th data point, the baseband signal is evenly divided into 2000 data points per segment to obtain each baseband signal segment.

[0074] Step S4: According to the time-frequency diagram, the subcarriers of the OFDM (Orthogonal Frequency Division Multiplexing) symbols of the preamble code or pilot sequence and the subcarriers carrying data in each baseband signal segment are determined.

[0075] In a specific implementation of the present invention, determining the subcarriers of the OFDM symbols of the preamble or pilot sequence and the subcarriers carrying data in each baseband signal segment according to the time-frequency diagram specifically includes:

[0076] Step S4.1: Determine the position of the preamble or pilot sequence in each baseband signal segment according to the time-frequency diagram;

[0077] Step S4.2: extracting the preamble code or pilot sequence in each baseband signal segment based on the position of the preamble code or pilot sequence in each baseband signal segment;

[0078] Step S4.3: Perform Fourier transform on the preamble code or pilot sequence in each baseband signal segment to obtain the subcarrier of the OFDM symbol of the preamble code or pilot sequence in each baseband signal segment;

[0079] Step S4.4: Determine the subcarrier carrying data according to the subcarrier of the OFDM symbol of the preamble or pilot sequence in each baseband signal segment.

[0080] Subcarriers carrying data refer to which subcarriers can carry data. Exemplarily, the number of subcarriers in an OFDM symbol is 1024, of which 150 subcarriers carry data.

[0081] Step S5: Generate a local sequence for each baseband signal segment according to the subcarriers of the OFDM symbols of the preamble code or pilot sequence in each baseband signal segment, the subcarriers carrying data, and the carried data.

[0082] The data carried is a type of sequence that optimizes the autocorrelation characteristics through mathematical construction. The specific data is multiple and definite, and the length and type of the data are the same as the number of subcarriers carrying the data.

[0083] All possible data carrying modes are defined, and each data carrying mode corresponds to a group of subcarriers carrying data. The subcarriers of the OFDM symbol are combined with different data carrying modes and the carried data to generate different local sequences.

[0084] For example, the number of subcarriers of the OFDM symbol of the pilot sequence is 1024, and the number of subcarriers carrying data is 150. Then, 150 possible subcarriers carrying data are selected from the 1024 subcarriers, and each group (i.e., 150 subcarriers carrying data) is selected to obtain a data carrying mode. The 1024 subcarriers are combined with a group of subcarriers and a type of data carried to obtain a local sequence. Therefore, the number of local sequences is equal to the product of the number of data carrying modes and the type of data carried.

[0085] Step S6: Calculate the normalized correlation sequence between each baseband signal segment and each of its local sequences.

[0086] In this embodiment, the calculation formula for the correlation sequence between each baseband signal segment and each local sequence thereof is:

[0087] (1)

[0088] in, Indicates the baseband signal segment With its local sequence The related sequence of Represents the baseband signal segment, represents the local sequence of baseband signal segments, Indicates delay, represents the index of discrete time, Indicates the baseband signal segment Length, Represents a local sequence Length.

[0089] In order to eliminate the influence of signal length and signal amplitude on the correlation value, the correlation sequence is normalized to obtain a normalized correlation sequence; wherein, the normalization processing formula is:

[0090] (2)

[0091] in, Indicates the baseband signal segment With its local sequence The normalized correlation sequence of .

[0092] Step S7: Determine the generation method of the preamble code or pilot sequence in each baseband signal segment according to the normalized correlation sequence between each baseband signal segment and each local sequence thereof.

[0093] In a specific embodiment of the present invention, the generation method of the preamble code or pilot sequence in each baseband signal segment is determined according to the normalized correlation sequence between each baseband signal segment and each local sequence thereof, specifically including:

[0094] Step S7.1: For each baseband signal segment, take each normalized correlation sequence The maximum normalized correlation value in ,in, Represents the normalized correlation sequence between the baseband signal segment and its i-th local sequence The maximum normalized correlation value in .

[0095] Step S7.2: The maximum normalized correlation value Compare with the set threshold;

[0096] If the maximum normalized correlation value If the set threshold is exceeded, the maximum normalized correlation value is recorded. and the maximum normalized correlation value The corresponding local sequence ; otherwise, discard the normalized correlation sequence .

[0097] For the baseband signal segment For each normalized correlation sequence, repeat steps S7.1 and S7.2 to obtain all the maximum normalized correlation values ​​recorded. and the local sequence corresponding to the maximum normalized correlation value The threshold is set based on experience, and the threshold in this embodiment is set to 0.2.

[0098] Step S7.3: If there are multiple maximum normalized correlation values and its corresponding local sequence are recorded, then the largest one is selected from multiple maximum normalized correlation values, that is, the maximum value is max (all the maximum normalized correlation values ​​recorded ).

[0099] Step S7.4: Determine the subcarriers of the OFDM symbols of the preamble or pilot sequence in the baseband signal segment, the subcarriers carrying data, and the data carried by the subcarriers according to the local sequence corresponding to the largest one.

[0100] The local sequence corresponding to the largest one (i.e. the determined local sequence) has the highest similarity with the preamble or pilot sequence in the baseband signal segment. Therefore, the subcarriers of the OFDM symbol of the preamble or pilot sequence in the baseband signal segment, the subcarriers carrying data, and the data carried by the subcarriers can be determined based on the local sequence corresponding to the largest one.

[0101] Step S7.5: Determine the method for generating the preamble code or pilot sequence in the baseband signal segment according to the subcarriers of the OFDM symbols of the preamble code or pilot sequence in the baseband signal segment, the subcarriers carrying data, and the data carried by the subcarriers.

[0102] The preamble code or pilot sequence in the baseband signal segment is generated according to the subcarriers of the OFDM symbol of the preamble code or pilot sequence, the subcarriers carrying data, and the data carried by the subcarriers.

[0103] Step S8: Accurately identify the drone based on the generation method of the preamble code or pilot sequence in all baseband signal segments.

[0104] For each baseband signal segment, the generation method of its preamble or pilot sequence can be determined, and then the type of the generation method of the preamble or pilot sequence in the entire baseband signal can be determined. The types of the generation methods of the preamble or pilot sequence of the baseband signals of different drones are not exactly the same, that is, the types of the generation methods of the preamble or pilot sequence of the baseband signals of different drones may be partially the same, partially different, or completely different, but the types of the generation methods of the preamble or pilot sequence of the baseband signals of different drones cannot be exactly the same.

[0105] Based on the fact that the types of generation methods of preamble codes or pilot sequences in baseband signals of different drones are not exactly the same, it is possible to determine whether the drone has been detected by the detection equipment before according to the type of generation method of the preamble code or pilot sequence in the entire baseband signal. This enables accurate identification of drones when the unique serial number of the drone cannot be known, and can assist detection equipment in drone identification, avoiding the problem that traditional detection equipment cannot directly determine whether the drone has been detected before when detecting a drone.

[0106] Embodiment 2

[0107] An embodiment of the present invention further provides an electronic device, which includes: a memory, a processor, and a computer program / instructions stored in the memory, and the processor executes the computer program / instructions to implement the drone accurate identification method in the embodiment of the present application.

[0108] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes according to the programs and / or data stored in the read-only memory (ROM) or the programs and / or data loaded from the storage part into the random access memory (RAM). The processor can be a multi-core processor or can include multiple processors. In some embodiments, the processor can include a general-purpose main processor and one or more special coprocessors, such as a central processing unit, a graphics processing unit (GPU), a neural network processor (NPU), a digital signal processor (DSP), etc. In RAM, various programs and data required for device operation are also stored. The processor, ROM, and RAM are connected to each other via a bus. The input / output (I / O) interface is also connected to the bus.

[0109] The processor and memory are used together to execute the program / instructions stored in the memory. When the program / instructions are executed by the computer, the methods, steps or functions described in the above embodiments can be implemented.

[0110] Although not shown, an embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the method for accurately identifying a drone in an embodiment of the present application.

[0111] Readable storage media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0112] Although not shown, an embodiment of the present invention further provides a computer program product, including: a computer program / instruction, which, when executed by a processor, implements the method for accurately identifying a drone in an embodiment of the present application.

[0113] What is disclosed above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or modifications within the technical scope disclosed in the present invention, which should be covered within the protection scope of the present invention.

Claims

1. A method for accurately identifying drones, characterized in that: The identification method comprises: Get the image transmission signal of the drone; Performing Fourier transform on the image transmission signal to obtain a time-frequency diagram; Preprocessing and segmenting the image transmission signal according to the time-frequency diagram to obtain a plurality of baseband signal segments including a preamble code or a pilot sequence; Determine, according to the time-frequency diagram, the subcarriers of the OFDM symbols of the preamble or pilot sequence and the subcarriers carrying data in each baseband signal segment; Generate a local sequence for each baseband signal segment according to the subcarriers of the OFDM symbol of the preamble or pilot sequence in each baseband signal segment, the subcarriers carrying data, and the carried data; Calculating a normalized correlation sequence between each baseband signal segment and each of its local sequences; Determine a generation method of a preamble or a pilot sequence in each baseband signal segment according to a normalized correlation sequence between each baseband signal segment and each local sequence thereof; Accurate identification of drones is achieved based on the generation method of preamble codes or pilot sequences in all baseband signal segments.

2. The method for accurately identifying drones according to claim 1, characterized in that: According to the time-frequency diagram, the image transmission signal is preprocessed and segmented, specifically including: Extracting key information of the image transmission signal from the time-frequency diagram; wherein the key information includes bandwidth, frequency band, center frequency, data starting point and data end point; Down-converting the image transmission signal according to the frequency band and the center frequency; Designing a filter according to the bandwidth, and using the filter to filter the down-converted image transmission signal; The filtered image transmission signal is subjected to time domain extraction to obtain a baseband signal; wherein the extraction multiple is determined according to the sampling rate and bandwidth; According to the data starting point and the extraction multiple, the baseband signal is evenly divided according to the time window to obtain multiple baseband signal segments containing preamble codes or pilot sequences; wherein the length of the time window is the interval between two pilot sequences in the baseband signal.

3. The method for accurately identifying drones according to claim 2, characterized in that: The key information of the image transmission signal is extracted from the time-frequency diagram using a pre-trained deep learning model. The training process of the deep learning model includes: Constructing a sample data set; wherein the samples in the sample data set include a time-frequency graph and key information thereof; Deploy a deep learning model and use the sample data set to train, verify and evaluate the deep learning model.

4. The method for accurately identifying drones according to claim 1, characterized in that: Determining, according to the time-frequency diagram, the subcarriers of the OFDM symbols of the preamble or pilot sequence and the subcarriers carrying data in each baseband signal segment, specifically includes: Determine the position of the preamble or pilot sequence in each baseband signal segment according to the time-frequency diagram; Extracting the preamble code or pilot sequence in each baseband signal segment based on the position of the preamble code or pilot sequence in each baseband signal segment; Performing Fourier transform on the preamble code or pilot sequence in each baseband signal segment to obtain the subcarrier of the OFDM symbol of the preamble code or pilot sequence in each baseband signal segment; The subcarriers carrying data are determined according to the subcarriers of the OFDM symbols of the preamble or pilot sequence in each baseband signal segment.

5. The method for accurately identifying drones according to claim 1, characterized in that: The specific calculation formula for the normalized correlation sequence between each baseband signal segment and each of its local sequences is: ； ； in, Indicates the baseband signal segment With its local sequence The normalized correlation sequence of Indicates the baseband signal segment With its local sequence The related sequence of Represents the baseband signal segment, represents the local sequence of baseband signal segments, Indicates delay, represents the index of discrete time, Indicates the baseband signal segment Length, Represents a local sequence Length.

6. The method for accurately identifying a drone according to any one of claims 1 to 5, characterized in that: Determining a generation method of a preamble or a pilot sequence in each baseband signal segment according to a normalized correlation sequence between each baseband signal segment and each local sequence thereof, specifically comprising: For each baseband signal segment, take the maximum normalized correlation value in each normalized correlation sequence, and compare the maximum normalized correlation value with a set threshold; if the maximum normalized correlation value exceeds the set threshold, record the maximum normalized correlation value and the local sequence corresponding to the maximum normalized correlation value; otherwise, discard the normalized correlation sequence; If multiple maximum normalized correlation values ​​and their corresponding local sequences are recorded, the largest one is selected from the multiple maximum normalized correlation values; Determine the subcarriers of the OFDM symbols of the preamble or pilot sequence in the baseband signal segment, the subcarriers carrying data, and the data carried by the subcarriers according to the local sequence corresponding to the largest one; The generation method of the preamble code or pilot sequence in the baseband signal segment is determined according to the subcarriers of the OFDM symbols of the preamble code or pilot sequence in the baseband signal segment, the subcarriers carrying data, and the data carried by the subcarriers.

7. An electronic device comprising a memory, a processor, and a computer program / instruction stored in the memory, characterized in that: The processor executes the computer program / instructions to implement the method for accurately identifying a drone as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the method for accurately identifying a drone as described in any one of claims 1 to 6 is implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the method for accurately identifying a drone as described in any one of claims 1 to 6 is implemented.

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