Unmanned aerial vehicle frequency band scanning detection method, unmanned aerial vehicle detection equipment and storage medium

Abstract

The invention relates to the field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle frequency band scanning detection method, unmanned aerial vehicle detection equipment and a storage medium. The target frequency is any first frequency selected from a first preset frequency set according to a polling rule; receiving a radio frequency signal of the unmanned aerial vehicle; performing frequency mixing processing on the radio frequency signal based on the target frequency to obtain a first signal after frequency mixing; obtaining an original signal corresponding to the radio frequency signal according to the first signal after frequency mixing; the original signal is detected and calculated, a detection result is obtained, and the detection result is whether the unmanned aerial vehicle is detected or not. According to the method, the radio frequency signal of the unmanned aerial vehicle can be efficiently received and scanned, the original signal is accurately restored through frequency mixing processing according to the target frequency of the unmanned aerial vehicle detection equipment, real-time monitoring is achieved, the false alarm rate is reduced, and the detection efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a frequency band scanning detection method for unmanned aerial vehicles, unmanned aerial vehicle detection equipment and a storage medium. Background Art

[0002] With the rapid development of drone technology, drones are increasingly used in military, commercial and civilian fields. The signals emitted by drones usually operate within a specific frequency band, which may vary depending on the model of the drone and the time of emission. In the electronic jamming and monitoring technology of drones, drone detection equipment accurately detects and identifies the frequency bands used by the signals emitted by drones.

[0003] In order to effectively detect drone signals, drone detection equipment usually adopts a rotation detection strategy. However, the existing technology has problems such as low frequency band detection efficiency, poor real-time performance, high equipment complexity and difficulty in interference identification. Summary of the invention

[0004] One purpose of an embodiment of the present invention is to provide a drone frequency band scanning detection method, a drone detection device and a storage medium, which are used to solve the technical problems of low frequency band detection efficiency, poor real-time performance, high equipment complexity and difficulty in interference identification in existing detections.

[0005] In a first aspect, an embodiment of the present invention provides a method for detecting a frequency band scan of a drone, which is applied to a drone detection device. The method includes:

[0006] Acquire a target frequency of the drone detection device, where the target frequency is any first frequency selected from a first preset frequency set according to a polling rule;

[0007] Receive the RF signal from the drone;

[0008] Performing a mixing process on the radio frequency signal based on the target frequency to obtain a mixed first signal;

[0009] Obtaining an original signal corresponding to the radio frequency signal according to the mixed first signal;

[0010] Perform detection calculation on the original signal to obtain a detection result, where the detection result is whether the drone is detected.

[0011] In combination with the first aspect, in a possible implementation manner, obtaining the original signal corresponding to the radio frequency signal according to the first signal after mixing includes: performing zero-IF processing on the first signal after mixing to obtain a baseband signal, where the baseband signal is an analog signal; performing analog-to-digital conversion sampling processing on the baseband signal to obtain a target sampling signal, where the target sampling signal is a digital signal; performing preset sampling processing on the target sampling signal to obtain a second signal, where the second signal is a signal obtained by performing downsampling on the target sampling signal after low-pass filtering; and performing preset frequency band processing on the second signal to obtain the original signal corresponding to the radio frequency signal.

[0012] It can be seen that in this embodiment, through multiple steps such as the conversion, filtering, and sampling of analog signals and digital signals, the restored original signal has high quality and accuracy, facilitating subsequent signal processing and analysis.

[0013] In combination with the first aspect, in a possible implementation manner, performing preset frequency band processing on the second signal to obtain the original signal corresponding to the radio frequency signal includes: mixing the second signal with each second frequency in a second preset frequency set in sequence to obtain multiple third signals after mixing, where the second frequencies corresponding to the multiple third signals are all different; and respectively performing filtering processing on the multiple third signals to obtain multiple fourth signals after filtering, where the multiple fourth signals are all the original signals corresponding to the radio frequency signal.

[0014] It can be seen that in this embodiment, the fourth signals after filtering are all the original signals corresponding to the radio frequency signal, and each fourth signal after filtering has undergone mixing processing and narrowband filtering processing at a specific frequency, ensuring the accuracy and effectiveness of the signal.

[0015] In combination with the first aspect, in a possible implementation manner, the original signal includes multiple fourth signals, and obtaining a detection result by performing detection calculation on the original signal, where the detection result is whether the drone is detected, includes: respectively performing correlation calculation on the multiple fourth signals to obtain a correlation coefficient set corresponding to each fourth signal in the multiple fourth signals, where the correlation coefficient set includes a first correlation coefficient and a second correlation coefficient; extracting the peak value of each correlation coefficient in the correlation coefficient set corresponding to each signal to obtain a peak coefficient corresponding to each fourth signal; determining whether the peak coefficient corresponding to each fourth signal is greater than a preset peak threshold; if it is greater than the preset peak threshold, determining the peak coefficient greater than the preset peak threshold as the target peak coefficient; and obtaining the detection result as detecting the drone according to the target peak coefficient.

[0016] It can be seen that in this embodiment, by performing correlation calculations, peak extraction, and judgment on multiple fourth signals, the detection result of whether a drone is detected is finally determined.

[0017] Combined with the first aspect, in a possible implementation manner, the step of determining whether the peak coefficient corresponding to each fourth signal is greater than a preset peak threshold includes: if it is not greater than the preset peak threshold, then the detection result is obtained as that the drone is not detected.

[0018] It can be seen that in this embodiment, according to the preset peak threshold, it is possible to accurately determine whether it is really a drone.

[0019] Combined with the first aspect, in a possible implementation manner, the step of respectively performing correlation calculations on the multiple fourth signals to obtain a set of correlation coefficients corresponding to each fourth signal in the multiple fourth signals, where the set of correlation coefficients includes a first correlation coefficient and a second correlation coefficient, includes: obtaining a preset first correlation symbol number and a preset second correlation symbol number; performing correlation calculations on each fourth signal and the preset first correlation symbol number to obtain a first correlation coefficient corresponding to each fourth signal; performing correlation calculations on each fourth signal and the preset second correlation symbol number to obtain a second correlation coefficient corresponding to each fourth signal; and obtaining a set of correlation coefficients corresponding to each fourth signal according to the first correlation coefficient corresponding to each fourth signal and the second correlation coefficient corresponding to each fourth signal.

[0020] It can be seen that in this embodiment, by obtaining the preset first and second correlation symbol numbers and performing correlation calculations on them with each fourth signal, the first and second correlation coefficients corresponding to each fourth signal can be obtained, which can more accurately identify and analyze the radio frequency signal of the drone, thereby improving the reliability and efficiency of detection.

[0021] Combined with the first aspect, in a possible implementation manner, after determining that the detection result is that the drone is detected according to the target peak coefficient, the method further includes: calculating the peak position of the target peak coefficient to obtain the peak position corresponding to the target peak coefficient, where the detection result further includes the peak position corresponding to the target peak coefficient.

[0022] It can be seen that in this embodiment, by calculating the peak position of the target peak coefficient, the peak position corresponding to the target peak coefficient can be obtained. The peak position and the peak coefficient together constitute the detection result, which is used for further signal analysis and processing.

[0023] In combination with the first aspect, in a possible implementation manner, after detecting and calculating the original signal to obtain a detection result, the method further includes: polling all the first frequencies in the first preset frequency set in sequence according to the polling rule to complete the detection of the radio frequency signal and obtain a plurality of detection results, where the detection result is whether the drone is detected.

[0024] It can be seen that in this embodiment, by polling all the frequencies in the first preset frequency set, the radio frequency signal detection of the drone is completed, and a plurality of detection results are obtained to determine the presence of the drone.

[0025] In the second aspect, an embodiment of the present invention provides a drone frequency band scanning detection device, which is applied to a drone detection device. The device includes:

[0026] An acquisition unit, configured to acquire a target frequency of the drone detection device, where the target frequency is a first frequency arbitrarily selected from the first preset frequency set according to the polling rule;

[0027] A receiving unit, configured to receive a radio frequency signal of the drone;

[0028] A processing unit, configured to perform mixing processing on the radio frequency signal based on the target frequency to obtain a first mixed signal;

[0029] The processing unit is further configured to obtain an original signal corresponding to the radio frequency signal according to the first mixed signal;

[0030] The processing unit is further configured to perform detection calculation on the original signal to obtain a detection result, where the detection result is whether the drone is detected.

[0031] In the third aspect, a drone detection device is provided. The drone detection device includes a memory and a processor. The memory is connected to the processor. The processor is configured to execute one or more computer programs stored in the memory. When the processor executes the one or more computer programs, the drone detection device implements the drone frequency band scanning detection method as described in the first aspect.

[0032] In the fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the drone frequency band scanning detection method as described in the first aspect.

[0033] In the embodiments implemented by the above-mentioned UAV frequency band scanning detection method, device, UAV detection equipment, and computer-readable storage medium, first, the target frequency of the UAV detection equipment is obtained. The target frequency is the first frequency randomly selected from the first preset frequency set according to the polling rule. Secondly, the radio frequency signal of the UAV is received, and the received radio frequency signal is subjected to mixing processing based on the target frequency to obtain the first mixed signal. Then, according to the first mixed signal, the original signal corresponding to the radio frequency signal is obtained. Finally, the original signal is subjected to detection calculation to obtain the detection result, where the detection result is whether the UAV is detected. In this embodiment, by receiving the radio frequency signal of the UAV and obtaining the target frequency of the UAV detection equipment, subsequent processing is carried out in a targeted manner, reducing unnecessary signal analysis time and improving the detection efficiency. Further, by performing mixing processing on the radio frequency signal, the first mixed signal can be obtained, and then the original signal corresponding to the radio frequency signal can be accurately restored, effectively reducing signal distortion and interference during transmission and improving the detection accuracy. Then, by detecting and calculating the original signal, it is possible to more accurately determine whether the UAV is detected, reducing false alarms caused by signal interference or other factors and improving the detection reliability. Therefore, this method can perform mixing processing for different target frequencies and has good adaptability to UAV signals in different frequency bands, without the need to design independent UAV detection equipment for signals in different frequency bands. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments of the present invention. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 is a schematic structural diagram of a UAV detection system in an embodiment of the present invention;

[0036] Figure 2 is a schematic flowchart of a UAV frequency band scanning detection method in an embodiment of the present invention;

[0037] Figure 3 is a schematic flowchart of a detection calculation in an embodiment of the present invention;

[0038] Figure 4 is a schematic structural diagram of a UAV frequency band scanning detection device in an embodiment of the present invention;

[0039] Figure 5 is a schematic structural diagram of a UAV detection equipment in an embodiment of the present invention. Detailed implementation manners

[0040] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts fall within the protection scope of the present invention.

[0041] It should be noted that if there is no conflict, the various features in the embodiments of the present invention can be combined with each other and are all within the protection scope of the present invention. In addition, although functional module division is carried out in the device schematic diagram and the logical sequence is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the sequence in the flowchart. Furthermore, the terms "first", "second", "third", etc. adopted by the present invention do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and roles.

[0042] Please refer to Figure 1 , Figure 1 which is a schematic structural diagram of a drone detection system. In Figure 1 , the drone detection system 10 includes a drone detection device 20, a control unit 30, an alarm unit 40, a positioning unit 50, etc.

[0043] Among them, the drone detection device 20 is mainly responsible for receiving and processing the radio frequency signals of drones. The specific steps of the drone detection device include: obtaining the target frequency of the drone detection device, where the target frequency is a first frequency randomly selected from a first preset frequency set according to a polling rule; receiving the radio frequency signal of the drone; performing mixing processing on the radio frequency signal based on the target frequency to obtain a first mixed signal; recovering the original signal corresponding to the radio frequency signal according to the first mixed signal; performing detection calculation on the original signal to obtain a detection result and determining whether a drone is detected.

[0044] Among them, the control unit 30 is responsible for coordinating and managing the working process of the entire drone detection system. It receives the processing results from the drone detection device 20 and further comprehensively analyzes and processes these results. The control unit 30 includes a central processing unit (CPU), a memory, an interface module, etc., and can execute complex algorithms and data processing tasks.

[0045] When the alarm unit 40 detects an unauthorized drone, it issues an alarm to notify the operator. The alarm method may include sound and light alarm, SMS notification, email, etc. The alarm unit 40 includes a speaker, a warning light, a communication module, etc. to ensure that the alarm information can be conveyed to relevant personnel in a timely manner.

[0046] The positioning unit 50 determines the specific location of the drone by analyzing the received signal. The positioning unit 50 uses multi-point receiving technology or antenna array to locate the direction and measure the distance of the drone signal source. It usually includes multiple receiving antennas, positioning algorithm modules and calculation units, which can accurately measure the location and movement trajectory of the drone.

[0047] Optionally, the drone detection system 10 may further include an interference and countermeasure unit, which sends out an interference signal to block the communication between the drone and the remote controller, forcing the drone to land or return. The interference and countermeasure unit includes an interference signal transmitter, control software, etc., which can effectively interfere with the normal operation of the drone.

[0048] Through the collaborative work of the above components and functional modules, the drone detection system 10 can efficiently detect, locate and respond to drones, providing a comprehensive solution for security protection.

[0049] In existing drone frequency band detection, drone detection equipment usually adopts a rotation detection strategy. However, the existing technology has problems such as low frequency band detection efficiency, poor real-time performance, high equipment complexity and difficulty in interference identification.

[0050] In view of this, the present application proposes a UAV frequency band scanning detection method to solve the above problems, which is described in detail below.

[0051] See also Figure 2 , Figure 2 A flowchart of a drone frequency band scanning detection method provided by an embodiment of the present invention is applied to a drone detection device. The method includes the following steps:

[0052] S10. Obtain a target frequency of the drone detection device, where the target frequency is any first frequency selected from a first preset frequency set according to a polling rule.

[0053] Among them, the polling rule is to select a frequency from a group of frequencies in a certain order or pattern, and the selected frequency is used for subsequent S30-S50. The purpose of the polling rule is to find the frequency point where the RF signal emitted by the drone is located among multiple frequencies.

[0054] Among them, the polling rule can be sequential polling, detecting each frequency in a preset order; random polling, randomly selecting a frequency for detection each time; polling based on historical data, selecting the frequency most likely to detect the drone according to historical data; priority polling, selecting according to the importance or priority of the frequency.

[0055] Specifically, in S20 - S50, the drone detection device will sequentially tune to each frequency point within the first preset frequency set, stay briefly for a certain period of time to detect whether there is a signal, and the setting of the time period is not limited.

[0056] Among them, the first preset frequency set is a set containing multiple first frequencies. The first frequencies are preset, and the setting rules can be selected according to common drone communication frequency bands, radio specifications, or specific application requirements, and there is no unique limitation here.

[0057] Optionally, the first preset frequency set can be four frequencies, 2422 MHz, 2452 MHz, 5766.5 MHz, 5806.5 MHz. Or, the first preset frequency set can be two frequencies, 2437 MHz and 5786.5 MHz.

[0058] In a specific implementation, the detection device selects the first first frequency (such as 2422 MHz) from the first preset frequency set, mixes the radio frequency signal with this first frequency to obtain the first mixed signal; according to the first mixed signal, obtains the original signal corresponding to the radio frequency signal; performs detection calculation on the original signal to obtain a detection result, and the detection result is whether the drone is detected. Repeat the above steps, sequentially selecting each first frequency in the first preset frequency set until a detection result is obtained for each first frequency in the first preset frequency set when it is mixed with the radio frequency signal, and then stop the detection.

[0059] Among them, the target frequency is a frequency in the first preset frequency set.

[0060] It can be seen that in this embodiment, the drone may use multiple frequencies for communication. Therefore, the detection device needs to continuously adjust its detection frequency to effectively capture the drone's signal. By polling among multiple frequencies, the detection device can increase the probability of detecting the drone signal.

[0061] S20. Receive the radio frequency signal of the drone.

[0062] Among them, the radio frequency (RF) signal is a type of radio wave, and its frequency range is usually between 3 kHz and 300 GHz. When the drone is flying and performing tasks, it will send RF signals through the radio transmitter carried on it. The RF signals are used for control, navigation, communication, or other functions. The RF signals include various types, such as digital signals, analog signals, or a combination of both. The RF signals contain data such as the working state, position information, and flight path of the drone.

[0063] Among them, the drone detection device receives the RF signals of the drone. The drone detection device has high sensitivity and wideband receiving capabilities to ensure that it can capture drone signals in various frequency ranges.

[0064] S30. Mix the RF signal based on the target frequency to obtain a first mixed signal.

[0065] Among them, the mixing process (also known as the down-conversion process) mixes the received high-frequency RF signal with the reference signal generated by the local oscillator (LO, Local Oscillator) to generate new frequency components. The new frequency components include the sum frequency and difference frequency of the original signal frequency and the local oscillator frequency.

[0066] Among them, the local oscillator is used to generate a reference signal that matches the target frequency. The frequency of this reference signal is usually very close to the target frequency so that a lower-frequency signal can be obtained after mixing.

[0067] Among them, the first signal is the difference frequency component (i.e., the difference between the target frequency and the local oscillator frequency). The first signal contains all the information of the original RF signal, but has a lower frequency, which is convenient for subsequent signal processing.

[0068] It can be seen that in this embodiment, the high-frequency signal processing is complex and costly. By mixing the signal down to a lower frequency, a simpler and more economical signal processing circuit can be used.

[0069] S40. Obtain the original signal corresponding to the RF signal according to the first mixed signal.

[0070] Among them, the process of obtaining the original signal corresponding to the RF signal according to the first mixed signal refers to restoring the down-converted low-frequency signal to the original high-frequency RF signal data through a series of signal processing steps. The process usually includes steps such as filtering, demodulation, and decoding.

[0071] Among them, the original signal is the original radio frequency signal data, and the original radio frequency signal data includes the control instructions, navigation information, image transmission, etc. of the unmanned aerial vehicle. That is, through the restored original signal, the monitoring, control, and analysis of the activities of the unmanned aerial vehicle can be realized.

[0072] It can be seen that in this embodiment, by restoring the original radio frequency signal from the first signal after mixing, the unmanned aerial vehicle detection device can further analyze the communication content, control instructions, or other important information of the unmanned aerial vehicle.

[0073] S50. Perform detection calculations on the original signal to obtain a detection result, where the detection result is whether the unmanned aerial vehicle is detected.

[0074] Among them, the process of detection calculation can be to process the original signal, which usually includes steps such as amplification, filtering, and down-conversion, so as to convert the original signal into a format suitable for analysis; extract features from the processed signal, and the features may be the frequency, amplitude, phase, modulation mode, etc. of the signal. The purpose of feature extraction is to identify the essential attributes of the signal; use a specific algorithm to analyze the extracted features to determine whether the signal exists, and the algorithm may include energy detection, cyclostationarity detection, matched filtering, etc.

[0075] Among them, during the detection process, a threshold is set, and the extracted features are compared with this threshold. If the feature value exceeds the threshold, it is considered that the unmanned aerial vehicle is detected; if it does not exceed, it is considered that the unmanned aerial vehicle is not detected.

[0076] It can be seen that in this embodiment, the signal of the unmanned aerial vehicle can be identified from a complex radio frequency environment, so as to realize the effective monitoring and management of the unmanned aerial vehicle.

[0077] In one embodiment, after performing detection calculations on the original signal to obtain a detection result, the method further includes: polling all the first frequencies in the first preset frequency set in sequence according to the polling rule, completing the detection of the radio frequency signal, and obtaining multiple detection results, where the detection result is whether the unmanned aerial vehicle is detected.

[0078] Specifically, the detection device selects each frequency one by one according to the polling rule to detect with the radio frequency signal, ensuring that each frequency in the set has undergone the process of S30 - S50 with the radio frequency signal to obtain multiple detection results.

[0079] It can be seen that in this embodiment, by polling all the frequencies in the first preset frequency set, the detection of the radio frequency signal of the unmanned aerial vehicle is completed, and multiple detection results are obtained to determine the presence of the unmanned aerial vehicle.

[0080] In this embodiment, by receiving the radio frequency signal of the drone and obtaining the target frequency of the drone detection device, the target frequency corresponding to the radio frequency signal can be quickly determined, so as to perform subsequent processing in a targeted manner, reducing unnecessary signal analysis time and improving the detection efficiency. Further, by performing mixing processing on the radio frequency signal, the first signal after mixing can be obtained, and then the original signal corresponding to the radio frequency signal can be accurately restored, thereby effectively reducing the distortion and interference of the signal during transmission and improving the detection accuracy. Then, by detecting the calculated original signal, it is possible to more accurately determine whether a drone is detected, reducing false alarms caused by signal interference or other factors and improving the detection reliability. Therefore, this method can perform mixing processing for different target frequencies and has good adaptability to drone signals in different frequency bands, without the need to design independent drone detection devices for signals in different frequency bands.

[0081] In one embodiment, obtaining the original signal corresponding to the radio frequency signal according to the first signal after mixing includes: performing zero-IF processing on the first signal after mixing to obtain a baseband signal, where the baseband signal is an analog signal; performing analog-to-digital conversion sampling processing on the baseband signal to obtain a target sampling signal, where the target sampling signal is a digital signal; performing preset sampling processing on the target sampling signal to obtain a second signal, where the second signal is a signal obtained by performing low-pass filtering on the target sampling signal and then performing downsampling; and performing preset frequency band processing on the second signal to obtain the original signal corresponding to the radio frequency signal.

[0082] Among them, zero-IF processing (Zero-IF Processing) converts the mixed signal to the baseband, that is, the frequency band with a frequency of zero. Zero-IF processing passes the mixed signal through a low-pass filter to remove unnecessary high-frequency components and obtain a baseband signal.

[0083] Among them, the baseband signal is an analog signal, which contains all the information of the original radio frequency signal, but has a lower frequency and is easier to process.

[0084] Among them, analog-to-digital conversion (ADC) is the process of converting an analog signal into a digital signal, that is, through sampling, a continuous analog signal is converted into a discrete digital signal. In a specific implementation, analog-to-digital conversion sampling processing is performed on the baseband signal to obtain a target sampling signal.

[0085] Among them, the target sampling signal is a digital signal, which is convenient for subsequent digital signal processing.

[0086] Among them, the preset sampling process is to perform low-pass filtering and downsampling on the target sampling signal. It involves a low-pass filter, which is used to remove high-frequency noise and unnecessary high-frequency components in the sampling signal, and downsampling is to reduce the sampling rate of the signal, thereby reducing the complexity of data processing and storage requirements. The second signal obtained in this step is still a digital signal, but with a smaller amount of data and is easier to process.

[0087] Among them, the second signal is the signal after the target sampling signal has undergone low-pass filtering and downsampling.

[0088] Among them, the preset frequency band processing may include but is not limited to operations such as filtering and frequency transformation to recover the original radio frequency signal. The preset frequency band processing ensures that the original signal recovered from the mixed signal is as close as possible to the original radio frequency signal transmitted by the drone.

[0089] For example, mix a 2.4 GHz radio frequency signal with a local oscillator signal (assuming a frequency of 2.4 GHz) to obtain a first mixed signal, perform zero-IF processing on the first mixed signal to obtain a baseband signal. Use an ADC to sample the baseband signal, assuming a sampling rate of 10 MHz, to obtain a target sampling signal, perform low-pass filtering on the target sampling signal to remove high-frequency components and retain effective signal components, perform downsampling on the filtered signal, assuming a downsampling factor of 2, that is, take one sample point for every two sample points, to reduce the data rate and improve processing efficiency, obtain a second signal, perform frequency band processing on the second signal to remove unnecessary frequency components, and obtain the original signal corresponding to the radio frequency signal.

[0090] It can be seen that in this embodiment, through multiple steps such as the conversion, filtering, and sampling of analog signals and digital signals, it is ensured that the restored original signal has high quality and accuracy, facilitating subsequent signal processing and analysis.

[0091] In one embodiment, the performing preset frequency band processing on the second signal to obtain the original signal corresponding to the radio frequency signal includes: sequentially mixing the second signal with each second frequency in the second preset frequency set to obtain a plurality of third mixed signals, and the corresponding second frequencies among the plurality of third mixed signals are all different; respectively performing filtering processing on the plurality of third mixed signals to obtain a plurality of fourth filtered signals, and the plurality of fourth filtered signals are all the original signals corresponding to the radio frequency signal.

[0092] Among them, when the first preset frequency set is two frequencies, 2437 MHZ and 5786.5 MHZ, the plurality of second frequencies in the second preset frequency set can be up-conversion of 7.5 MHz or 10 MHz, up-conversion of 22.5 / 30 MHZ, up-conversion of 7.5 MHz or 10 MHz, down-conversion of 22.5 / 30 MHZ.

[0093] Among them, when the first preset frequency set is four frequencies, 2422 MHz, 2452 MHz, 5766.5 MHz, and 5806.5 MHz, the multiple second frequencies in the second preset frequency set can be up-converted by 7.5 MHz or down-converted by 7.5 MHz, up-converted by 10 MHz or down-converted by 10 MHz. Specifically, the radio frequency signal frequencies corresponding to up-conversion by 7.5 MHz or down-conversion by 7.5 MHz are 2422 MHz and 2452 MHz; the radio frequency signal frequencies corresponding to up-conversion by 10 MHz or down-conversion by 10 MHz are 5766.5 MHz and 5806.5 MHz.

[0094] For example, the second signal is mixed with +7.5 MHz to obtain the first third signal after mixing; the second signal is mixed with -7.5 MHz to obtain the second third signal after mixing; the second signal is mixed with +10 MHz to obtain the third third signal after mixing; the second signal is mixed with -10 MHz to obtain the fourth third signal after mixing.

[0095] Furthermore, each third signal after mixing is subjected to 10 MHz narrowband filtering to obtain the fourth signal after filtering.

[0096] Among them, the filtering process is to remove the unwanted frequency components through a filter and only retain the signals within the required frequency range.

[0097] Among them, through the mixing and filtering processes, the multiple fourth signals finally obtained are actually different representations of the original radio frequency signal. Each fourth signal after filtering can be regarded as a copy of the original radio frequency signal, except that the frequency positions may be different.

[0098] It can be seen that the fourth signals after filtering in this embodiment are all the original signals corresponding to the radio frequency signals. Each fourth signal after filtering has undergone mixing processing and narrowband filtering at specific frequencies, ensuring the accuracy and effectiveness of the signals.

[0099] In one embodiment, the original signal includes a plurality of fourth signals. Detecting and calculating the original signal to obtain a detection result, where the detection result is whether the drone is detected, includes: performing correlation calculations on the plurality of fourth signals respectively to obtain a set of correlation coefficients corresponding to each fourth signal in the plurality of fourth signals, where the set of correlation coefficients includes a first correlation coefficient and a second correlation coefficient; extracting peaks from each correlation coefficient in the set of correlation coefficients corresponding to each signal to obtain a peak coefficient corresponding to each fourth signal; determining whether the peak coefficient corresponding to each fourth signal is greater than a preset peak threshold; if it is greater than the preset peak threshold, determining the peak coefficient greater than the preset peak threshold as the target peak coefficient; and obtaining the detection result as detecting the drone according to the target peak coefficient.

[0100] Among them, the set of correlation coefficients includes a first correlation coefficient and a second correlation coefficient. The first correlation coefficient can be a symbol 6 correlation coefficient, and the second correlation coefficient can be a symbol 4 correlation coefficient.

[0101] Among them, correlation calculation is used to measure the similarity between two signals. In drone detection, this usually involves calculating the correlation between the received signal and a known signal (such as a Zadoff-Chu sequence). For each signal, a correlation calculation is performed to obtain a set of correlation coefficients, which reflects the similarity degree of the signal and the reference signal under different phase offsets.

[0102] For reference Figure 3 , Figure 3 is a schematic flowchart of the detection calculation process. In Figure 3 , the correlation calculation processes are all performed simultaneously. In Figure 3 , the first original signal, the second original signal, the third original signal, and the fourth original signal correspond to the plurality of filtered fourth signals described above.

[0103] Among them, the Zadoff-Chu (ZC) sequence is a class of complex constant modulus sequences widely used in wireless communication systems. They have good autocorrelation and cross-correlation characteristics and are suitable for applications such as channel estimation, synchronization, and random access. The ZC sequence is calculated through a set of parameterized generation formulas, where the "root" is a key parameter. Zadoff-Chu sequence generation formula: Given a sequence length N and a "root" u, the generation formula of the ZC sequence x(n) is: x(n) = exp(j·πun(n + 1) / N)x(n)) = exp(j·πun(n + 1) / N).

[0104] where \(n = 0, 1, 2, \ldots, N - 1\). For example, for a ZC sequence with a root of 600, assuming we want to generate a ZC sequence of length \(N\) and the "root" \(u\) is 600, we calculate the ZC sequence using the Zadoff - Chu sequence generation formula. Then, we perform correlation calculations between each received signal and the ZC sequence to obtain the first correlation coefficient and the second correlation coefficient. Next, we extract the peak coefficient from these correlation coefficients. If the peak coefficient is greater than a preset peak threshold, it can be determined that a drone has been detected.

[0105] Among them, peak extraction refers to finding the maximum value from a set of correlation coefficients, and this value is called the peak coefficient, which represents the degree of similarity between the signal and the reference signal.

[0106] Among them, the preset peak threshold is a pre - set value used to determine whether the peak coefficient is large enough to be certain that a drone has been detected. If the peak coefficient is greater than this threshold, it is considered that a drone has been detected.

[0107] Among them, the target peak coefficient refers to those peak coefficients that are greater than the preset peak threshold, which indicate the presence of the drone signal.

[0108] Specifically, in the curve corresponding to the correlation coefficients, find all local maximum points. These local maximum points are usually the peak candidate points of the signal. Compare the local maximum points with the preset threshold, and filter out the peak points greater than the threshold. Among the filtered peak points, select the largest one as the final peak coefficient. Compare the extracted peak coefficient with the preset peak threshold to determine whether it is greater than the threshold. If it is greater than the threshold, then determine that this peak coefficient is the target peak coefficient.

[0109] It can be seen that in this embodiment, by performing correlation calculations, peak extraction, and judgment on multiple signals, the detection result of whether a drone is detected is finally determined.

[0110] In one embodiment, the judgment of whether the peak coefficient corresponding to each fourth signal is greater than the preset peak threshold includes: if it is not greater than the preset peak threshold, then the detection result is that the drone has not been detected.

[0111] Among them, if the peak coefficient is lower than the preset peak threshold, it is judged that the detected signal may be caused by noise, interference, or other non - drone signals, so it will not be marked as a drone.

[0112] Among them, in wireless signal detection, environmental noise and interference from other wireless signals are common problems. Setting an accurate preset peak threshold helps to reduce false alarms, that is, misidentifying noise or other signals as drones.

[0113] It can be seen that in this embodiment, according to the preset peak threshold, it is possible to accurately determine whether it is really a drone.

[0114] In one embodiment, the method of respectively performing correlation calculations on the plurality of fourth signals to obtain a set of correlation coefficients corresponding to each fourth signal in the plurality of fourth signals, where the set of correlation coefficients includes a first correlation coefficient and a second correlation coefficient, includes: obtaining a preset first correlation symbol number and a preset second correlation symbol number; performing a correlation calculation on each fourth signal with the preset first correlation symbol number to obtain a first correlation coefficient corresponding to each fourth signal; performing a correlation calculation on each fourth signal with the preset second correlation symbol number to obtain a second correlation coefficient corresponding to each fourth signal; and obtaining a set of correlation coefficients corresponding to each fourth signal according to the first correlation coefficient corresponding to each fourth signal and the second correlation coefficient corresponding to each fourth signal.

[0115] Among them, the preset first correlation symbol number is a known or preset symbol sequence for performing a correlation calculation with each fourth signal. The preset first correlation symbol number can be symbol 6.

[0116] Among them, the preset second correlation symbol number is also a known or preset symbol sequence for performing a correlation calculation with each fourth signal, and the preset second correlation symbol number can be symbol 4.

[0117] Among them, the correlation calculation can be implemented through a cross-correlation function to obtain a first correlation coefficient corresponding to each fourth signal.

[0118] Among them, combining the first correlation coefficient and the second correlation coefficient corresponding to each fourth signal forms a set of correlation coefficients corresponding to each fourth signal. This set contains the similarity information between each fourth signal and the preset first and second correlation symbol numbers.

[0119] It can be seen that in this embodiment, by obtaining the preset first and second correlation symbol numbers and performing correlation calculations with a plurality of fourth signals, the first and second correlation coefficients corresponding to each fourth signal can be obtained, which can more accurately identify and analyze the radio frequency signals of drones, thereby improving the reliability and efficiency of detection.

[0120] In one embodiment, after determining that the detection result is that the drone is detected according to the target peak coefficient, the method further includes: calculating the peak position of the target peak coefficient to obtain the peak position corresponding to the target peak coefficient, where the detection result further includes the peak position corresponding to the target peak coefficient.

[0121] Among them, the target peak coefficient refers to the coefficient that exceeds a preset threshold and is considered relevant to the UAV signal among a series of correlation coefficients. The target peak coefficient usually indicates the part of the signal that best matches the communication characteristics of the UAV.

[0122] Among them, peak position calculation refers to the process of determining the position where the target peak coefficient appears in the signal. The peak position is usually a specific point in the time domain or frequency domain, which corresponds to a specific feature of the UAV signal, such as a synchronization header or a specific modulation mode.

[0123] Specifically, the process of peak position calculation can be as follows: find the maximum coefficient value in the set of correlation coefficients, that is, the peak; determine the specific position of this peak in the signal, which can be achieved by recording the index or timestamp when the peak appears; in some cases, further processing may be required to improve the calculation accuracy of the peak position, such as using interpolation techniques to estimate the exact position of the peak.

[0124] Among them, the detection result is a data set containing multiple parameters, which may include the following: the value of the target peak coefficient, the time domain or frequency domain coordinates of the peak position, the possible type or model of the UAV, and the communication protocol information of the UAV.

[0125] It can be seen that in this embodiment, by performing peak position calculation on the target peak coefficient, the peak position corresponding to the target peak coefficient can be obtained. The peak position and the peak coefficient together constitute the detection result, which is used for further signal analysis and processing.

[0126] It should be noted that in the above various embodiments, there is not necessarily a certain order between the above steps. Those of ordinary skill in the art can understand according to the description of the embodiments of the present application that in different embodiments, the above steps can have different execution orders, that is, they can be executed in parallel or exchanged, etc.

[0127] As another aspect of the embodiments of the present application, the embodiments of the present application provide a UAV frequency band scanning detection device.

[0128] See Figure 4 , Figure 4 is a schematic structural diagram of a UAV frequency band scanning detection device provided by the embodiments of the present application. Applied to UAV detection equipment, such as Figure 4 shown, the UAV frequency band scanning detection device 400 includes:

[0129] An acquisition unit 401, configured to acquire the target frequency of the UAV detection device, where the target frequency is a first frequency arbitrarily selected from a first preset frequency set according to a polling rule;

[0130] A receiving unit 402, configured to receive a radio frequency signal of a drone;

[0131] A processing unit 403, configured to perform mixing processing on the radio frequency signal based on the target frequency to obtain a first mixed signal;

[0132] The processing unit 403 is further configured to obtain an original signal corresponding to the radio frequency signal according to the first mixed signal;

[0133] The processing unit 403 is further configured to perform detection calculation on the original signal to obtain a detection result, where the detection result is whether the drone is detected.

[0134] In this embodiment, by receiving the radio frequency signal of the drone and obtaining the target frequency of the drone detection device, the target frequency corresponding to the radio frequency signal can be quickly determined, so as to perform subsequent processing targeted, reduce unnecessary signal analysis time, and improve the detection efficiency; further, by performing mixing processing on the radio frequency signal, a first mixed signal can be obtained, and then the original signal corresponding to the radio frequency signal can be accurately restored, thereby effectively reducing the distortion and interference of the signal during transmission and improving the detection accuracy; then, through the original signal obtained by detection calculation, it can be more accurately determined whether the drone is detected, reducing false alarms caused by signal interference or other factors and improving the detection reliability; therefore, this method can perform mixing processing for different target frequencies and has good adaptability to drone signals of different frequency bands, without the need to design independent drone detection devices for signals of different frequency bands.

[0135] In an embodiment, in the process of obtaining the original signal corresponding to the radio frequency signal according to the first mixed signal, the processing unit 403 is further configured to: perform zero intermediate frequency processing on the first mixed signal to obtain a baseband signal, where the baseband signal is an analog signal; perform analog-to-digital conversion sampling processing on the baseband signal to obtain a target sampling signal, where the target sampling signal is a digital signal; perform preset sampling processing on the target sampling signal to obtain a second signal, where the second signal is a signal obtained by performing low-pass filtering on the target sampling signal and then performing downsampling; perform preset frequency band processing on the second signal to obtain the original signal corresponding to the radio frequency signal.

[0136] In one embodiment, in the process of performing preset frequency band processing on the second signal to obtain the original signal corresponding to the radio frequency signal, the processing unit 403 is further configured to: sequentially mix the second signal with each second frequency in a second preset frequency set to obtain a plurality of third signals after mixing, where the corresponding second frequencies among the plurality of third signals are all different; respectively perform filtering processing on the plurality of third signals to obtain a plurality of fourth signals after filtering, and the plurality of fourth signals are all the original signals corresponding to the radio frequency signal.

[0137] In one embodiment, the original signal includes a plurality of fourth signals. In the process of performing detection calculation on the original signal to obtain a detection result, where the detection result is whether the drone is detected, the processing unit 403 is further configured to: respectively perform correlation calculation on the plurality of fourth signals to obtain a correlation coefficient set corresponding to each fourth signal among the plurality of fourth signals, where the correlation coefficient set includes a first correlation coefficient and a second correlation coefficient; extract the peak value of each correlation coefficient in the correlation coefficient set corresponding to each signal to obtain a peak coefficient corresponding to each fourth signal; determine whether the peak coefficient corresponding to each fourth signal is greater than a preset peak threshold; if it is greater than the preset peak threshold, determine the peak coefficient greater than the preset peak threshold as the target peak coefficient; and obtain the detection result as detecting the drone according to the target peak coefficient.

[0138] In one embodiment, in the process of determining whether the peak coefficient corresponding to each fourth signal is greater than a preset peak threshold, the processing unit 403 is further configured to: if it is not greater than the preset peak threshold, obtain the detection result as not detecting the drone.

[0139] In one embodiment, in the process of respectively performing correlation calculation on the plurality of fourth signals to obtain a correlation coefficient set corresponding to each fourth signal among the plurality of fourth signals, where the correlation coefficient set includes a first correlation coefficient and a second correlation coefficient, the processing unit 403 is further configured to: obtain a preset first correlation symbol number and a preset second correlation symbol number; perform correlation calculation on each fourth signal and the preset first correlation symbol number to obtain a first correlation coefficient corresponding to each fourth signal; perform correlation calculation on each fourth signal and the preset second correlation symbol number to obtain a second correlation coefficient corresponding to each fourth signal; and obtain a correlation coefficient set corresponding to each fourth signal according to the first correlation coefficient corresponding to each fourth signal and the second correlation coefficient corresponding to each fourth signal.

[0140] In one embodiment, after determining, according to the target peak factor, that the detection result is that the drone is detected, the processing unit 403 is further configured to: calculate a peak position of the target peak factor to obtain a peak position corresponding to the target peak factor, where the detection result further includes the peak position corresponding to the target peak factor.

[0141] It should be noted that the above drone band scanning detection device can execute the drone band scanning detection method provided by the embodiments of the present application, and has corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in the embodiments of the drone band scanning detection device, reference can be made to the drone band scanning detection method provided by the S10-S50 embodiments of the present application.

[0142] See Figure 5 , Figure 5 is a schematic structural diagram of a drone detection device in a drone detection system provided by an embodiment of the present application. As Figure 5 shown, the processor 501 is communicatively connected to the memory 502.

[0143] Specifically, the processor 501 is configured to support the drone detection device to execute the corresponding functions in the drone band scanning detection method in the above method embodiments. The processor 501 may be a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof. The above hardware chip may be an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The above PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0144] Specifically, the processor 501 may include a sending card, a receiving card, and a driving chip.

[0145] Specifically, the memory 502 is used to store program codes and the like. The memory 502 may include a volatile memory (VM), such as a random access memory (RAM); the memory 502 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); the memory 502 may further include a combination of the above types of memories.

[0146] An embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the drone frequency band scanning detection method as described in the foregoing embodiment.

[0147] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it may include the processes of the embodiments of the above methods. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0148] The foregoing disclosure is only for the preferred embodiments of the present application. Of course, the scope of the rights of the present application cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present application still fall within the scope covered by the present application.

Claims

1. A method for detecting a frequency band of an unmanned aerial vehicle, characterized in that: Applied to drone detection equipment, the method comprises: Acquire a target frequency of the drone detection device, where the target frequency is any first frequency selected from a first preset frequency set according to a polling rule; Receive the RF signal from the drone; Performing a mixing process on the radio frequency signal based on the target frequency to obtain a mixed first signal; Obtaining an original signal corresponding to the radio frequency signal according to the mixed first signal; Perform detection calculation on the original signal to obtain a detection result, where the detection result is whether the drone is detected.

2. The method according to claim 1, characterized in that The obtaining, according to the mixed first signal, an original signal corresponding to the radio frequency signal comprises: Performing zero intermediate frequency processing on the mixed first signal to obtain a baseband signal, wherein the baseband signal is an analog signal; Performing analog-to-digital conversion sampling processing on the baseband signal to obtain a target sampling signal, wherein the target sampling signal is a digital signal; Performing a preset sampling process on the target sampling signal to obtain a second signal, where the second signal is a signal obtained by low-pass filtering the target sampling signal and then down-sampling it; The second signal is processed in a preset frequency band to obtain an original signal corresponding to the radio frequency signal.

3. The method according to claim 2, characterized in that The performing preset frequency band processing on the second signal to obtain an original signal corresponding to the radio frequency signal includes: Mixing the second signal with each second frequency in the second preset frequency set in sequence to obtain a plurality of third signals after mixing, wherein the second frequencies corresponding to the plurality of third signals are all different; The multiple third signals are filtered respectively to obtain multiple fourth signals after filtering, and the multiple fourth signals are all original signals corresponding to the radio frequency signal.

4. The method according to claim 1, characterized in that: The original signal includes a plurality of fourth signals, and the detection calculation is performed on the original signal to obtain a detection result, wherein the detection result is whether the drone is detected, and includes: Performing correlation calculations on the plurality of fourth signals respectively to obtain a set of correlation coefficients corresponding to each fourth signal in the plurality of fourth signals, wherein the set of correlation coefficients includes a first correlation coefficient and a second correlation coefficient; Performing peak extraction on each correlation coefficient in the correlation coefficient set corresponding to each fourth signal to obtain a peak coefficient corresponding to each fourth signal; Determine whether the peak coefficient corresponding to each fourth signal is greater than a preset peak threshold; If it is greater than the preset peak value threshold, determining the peak value coefficient greater than the preset peak value threshold as the target peak value coefficient; According to the target peak coefficient, the detection result is obtained as detecting the drone.

5. The method according to claim 4, characterized in that The determining whether the peak coefficient corresponding to each fourth signal is greater than a preset peak threshold comprises: If it is not greater than the preset peak value threshold, the detection result is that the drone is not detected.

6. The method according to claim 4, characterized in that The correlating calculation is performed on the plurality of fourth signals respectively to obtain a set of correlation coefficients corresponding to each fourth signal in the plurality of fourth signals, wherein the set of correlation coefficients includes a first correlation coefficient and a second correlation coefficient, including: Obtaining a preset first number of related symbols and a preset second number of related symbols; Perform correlation calculation on each fourth signal and the preset first correlation symbol number to obtain a first correlation coefficient corresponding to each fourth signal; Perform correlation calculation on each fourth signal and the preset second correlation symbol number to obtain a second correlation coefficient corresponding to each fourth signal; A correlation coefficient set corresponding to each fourth signal is obtained according to the first correlation coefficient corresponding to each fourth signal and the second correlation coefficient corresponding to each fourth signal.

7. The method according to claim 4, characterized in that After determining, according to the target peak coefficient, that the detection result is that the drone is detected, the method further includes: A peak position calculation is performed on the target peak coefficient to obtain a peak position corresponding to the target peak coefficient, wherein the detection result also includes a peak position corresponding to the target peak coefficient.

8. The method according to claim 1, characterized in that After performing detection calculation on the original signal to obtain a detection result, the method further includes: According to the polling rule, all first frequencies in the first preset frequency set are polled in sequence to complete the detection of the radio frequency signal and obtain multiple detection results, where the detection result is whether the drone is detected.

9. A drone detection device, comprising a memory and a processor, wherein the memory is connected to the processor, and the processor is used to execute one or more computer programs stored in the memory, and when the processor executes the one or more computer programs, the drone detection device implements the drone frequency band scanning detection method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor executes the drone frequency band scanning detection method according to any one of claims 1 to 8.

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