An Unmanned Aerial Vehicle Detection Method and System Based on Pilot Synchronization Sequence Matching

Abstract

The present application relates to a method and system for detecting unmanned aerial vehicles (UAVs) based on pilot synchronization sequence matching, belonging to the technical field of UAV detection. The detection method includes: constructing a general reference sequence; receiving a signal to be detected, and intercepting a signal segment with a preset number of points from the signal to be detected; performing a fast Fourier transform on the signal segment to obtain the frequency-domain representation of the signal to be detected; performing a fast Fourier transform on the general reference sequence according to the preset number of points to obtain a frequency-domain general reference sequence; performing a conjugate operation on the frequency-domain general reference sequence to obtain a frequency-domain reference signal; performing a convolution operation on the frequency-domain representation of the signal to be detected and the frequency-domain reference signal to obtain a frequency-domain convolution result; performing a constant false alarm rate detection on the frequency-domain convolution result to obtain a UAV detection result. The present application can meet the requirements for fast and efficient detection of UAVs in a complex signal environment.

Description

Technical Field

[0001] The present application relates to the technical field of UAV detection, and in particular to a UAV detection method and system based on pilot synchronization sequence matching. Background Art

[0002] In recent years, with the rapid development of UAV technology, the use of consumer UAVs has gradually become popular, and UAVs have been widely used in various industries. However, the rapid popularization of this technology has also brought many potential safety hazards and management problems. In key areas such as airports, energy facilities, and large event venues, the illegal intrusion of UAVs may pose a serious threat to public safety. Therefore, the detection of UAVs has become an important technical means to ensure public safety and privacy.

[0003] Currently, common UAV detection technologies mainly include frequency band detection technology and machine learning recognition technology based on spectral waterfall diagrams. Among them, the frequency band detection technology needs to scan each sub-frequency band one by one, with a long detection time and a complex process, making it difficult to detect multiple targets in a dynamic frequency band; while the signal of the recognition technology based on the spectral waterfall diagram has problems such as long acquisition time and insufficient real-time performance. Therefore, how to improve real-time performance and detection accuracy to meet the requirements of rapid and efficient detection of UAVs in a complex signal environment has become an urgent problem to be solved at present. Summary of the Invention

[0004] In order to meet the requirements of rapid and efficient detection of UAVs in a complex signal environment, the present application provides a UAV detection method and system based on pilot synchronization sequence matching.

[0005] In the first aspect, the present application provides a UAV detection method based on pilot synchronization sequence matching, adopting the following technical solutions:

[0006] A UAV detection method based on pilot synchronization sequence matching, the detection method comprising:

[0007] Construct a general reference sequence;

[0008] Receive a signal to be detected, and intercept a signal segment with a preset number of points from the signal to be detected;

[0009] Perform a fast Fourier transform on the signal segment to obtain the frequency domain representation of the signal to be detected;

[0010] Perform a fast Fourier transform on the general reference sequence according to the preset number of points to obtain a frequency domain general reference sequence;

[0011] Perform a conjugate operation on the frequency domain general reference sequence to obtain a frequency domain reference signal;

[0012] Perform a convolution operation on the frequency-domain representation of the signal to be detected and the frequency-domain reference signal to obtain a frequency-domain convolution result;

[0013] Perform a constant false alarm rate (CFAR) detection on the frequency-domain convolution result to obtain a UAV detection result.

[0014] By adopting the above technical solution, a general reference sequence adaptable to different bandwidths is constructed. Combining frequency-domain transformation, convolution operation, and constant false alarm rate detection, the efficient and accurate detection of UAV signals is realized. Utilizing the characteristics of the pilot synchronization sequence in the UAV video transmission signal / UAV remote control signal, this method performs precise matching in the frequency domain, greatly improving the signal-to-noise ratio, and effectively reducing false detection and missed detection through dynamic threshold detection. The technical solution of this application has the advantages of strong real-time performance, high detection accuracy, and low computational cost, and is suitable for the UAV security monitoring requirements in complex signal environments, providing reliable technical support for the security guarantee of airports, energy facilities, and key areas.

[0015] Optionally, the UAV detection result is used to determine whether there is a UAV video transmission signal in the signal to be detected; the steps of constructing the general reference sequence include:

[0016] According to the bandwidth characteristics of the UAV video transmission signal, obtain the pilot synchronization sequence of the UAV video transmission signal, and the pilot synchronization sequence is composed of ZC sequences;

[0017] Construct a sequence of all zeros with the number of points of the pilot synchronization sequence matching the sampling rate of the UAV video transmission signal, and embed the pilot synchronization sequences corresponding to different bandwidths into the specified positions of the all-zero sequence to obtain a frequency-domain pilot synchronization sequence; where the preset number of points is an integer multiple n of the number of points of the pilot synchronization sequence, n≥2;

[0018] Convert the frequency-domain pilot synchronization sequence to the time domain through inverse fast Fourier transform to obtain the general reference sequence.

[0019] By adopting the above technical solution, a general reference sequence applicable to signals with different bandwidths is constructed based on the pilot synchronization sequence of the UAV video transmission signal characteristics. During the construction process, by matching the bandwidth points, embedding the all-zero sequence, and performing inverse fast Fourier transform, it is ensured that the spectral characteristics of the general reference sequence are consistent with the UAV signal. The finally generated general reference sequence can not only cover UAV signals with different bandwidths but also effectively improve the accuracy and signal-to-noise ratio of signal detection in subsequent matching operations.

[0020] Optionally, the steps of performing a constant false alarm rate (CFAR) detection on the frequency-domain convolution result to obtain a UAV detection result include:

[0021] Analyze the peak characteristics in the frequency-domain convolution result;

[0022] Determine whether the peak feature meets the preset threshold condition;

[0023] If so, it is determined that there is a UAV video transmission signal in the signal to be detected, that is, a UAV is detected; if not, it is determined that there is no UAV video transmission signal in the signal to be detected, that is, no UAV is detected.

[0024] By adopting the above technical solution, it is possible to effectively extract and identify UAV video transmission signals in a complex signal environment. Using the dynamic threshold strategy and combining the amplitude and position characteristics of the peaks, the noise interference and the possibility of false alarms are greatly reduced, while ensuring the reliable detection of weak target signals. This technical solution has the characteristics of high accuracy, high robustness and real-time performance, and can provide a fast and accurate solution for UAV detection, which is suitable for the security guarantee requirements of airports, energy facilities and other key areas.

[0025] Optionally, the UAV detection result is used to determine whether there is a UAV remote control signal in the signal to be detected; the steps of constructing the general reference sequence include:

[0026] According to the bandwidth characteristics of the UAV remote control signal, obtain the pilot synchronization sequence of the UAV remote control signal, and the pilot synchronization sequence is composed of ZC sequences;

[0027] Construct a all-zero sequence with the number of points of the pilot synchronization sequence matching the sampling rate of the UAV remote control signal, and embed the pilot synchronization sequences corresponding to different bandwidths into the specified positions of the all-zero sequence to obtain the frequency-domain pilot synchronization sequence; where the preset number of points is an integer multiple n of the number of points of the pilot synchronization sequence, n≥2;

[0028] Convert the frequency-domain pilot synchronization sequence to the time domain through the inverse fast Fourier transform to obtain the general reference sequence.

[0029] By adopting the above technical solution, combining the bandwidth characteristics, frequency-domain embedding and time-domain signal generation, this solution can generate a reference template consistent with the actual remote control signal, ensuring the matching degree and robustness of subsequent detection.

[0030] Optionally, the steps of performing constant false alarm rate detection on the frequency-domain convolution result to obtain the UAV detection result include:

[0031] Analyze the peak features (including the position and amplitude of the peaks) in the frequency-domain convolution result;

[0032] Determine whether the peak feature meets the preset threshold condition;

[0033] If so, it is determined that there is the UAV remote control signal in the signal to be detected, that is, a UAV is detected; if not, it is determined that there is no UAV remote control signal in the signal to be detected, that is, no UAV is detected.

[0034] By adopting the above technical solution, the peak features (including position and amplitude) in the frequency-domain convolution result are analyzed, and combined with dynamic preset threshold conditions (the number of protection units, the number of reference units, and the detection threshold), the high-precision detection of the UAV remote control signal is achieved. When the peak features meet the threshold conditions, the system can reliably determine the presence of the UAV signal; otherwise, the interference of background noise or other non-target signals can be excluded.

[0035] Optionally, the constant false alarm rate detection includes a bilateral average constant false alarm rate strategy, a large selection strategy for the average of both side units, or a small selection strategy for the average of both side units.

[0036] Optionally, the preset threshold conditions include a preset number of protection units, a preset number of reference units, and a preset detection threshold.

[0037] In a second aspect, the present application provides a UAV detection system based on pilot synchronization sequence matching, adopting the following technical solution:

[0038] A UAV detection system based on pilot synchronization sequence matching, the detection system includes:

[0039] A sequence construction module for constructing a general reference sequence;

[0040] A signal reception module for receiving a signal to be detected;

[0041] A signal segment intercepting module for intercepting a signal segment with a preset number of points from the signal to be detected;

[0042] A signal segment processing module for performing a fast Fourier transform on the signal segment to obtain a frequency-domain representation of the signal to be detected;

[0043] A frequency-domain conversion module for performing a fast Fourier transform on the general reference sequence according to the preset number of points to obtain a frequency-domain general reference sequence;

[0044] A conjugate operation module for performing a conjugate operation on the frequency-domain general reference sequence to obtain a frequency-domain reference signal;

[0045] A convolution module for performing a convolution operation on the frequency-domain representation of the signal to be detected and the frequency-domain reference signal to obtain a frequency-domain convolution result;

[0046] A constant false alarm rate detection module for performing a constant false alarm rate detection on the frequency-domain convolution result to obtain a UAV detection result.

[0047] In a third aspect, the present application provides a computer device, adopting the following technical solution:

[0048] A computer device includes a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the method described in the first aspect.

[0049] In a fourth aspect, the present application provides a computer-readable storage medium, adopting the following technical solution:

[0050] A computer-readable storage medium stores a computer program that can be loaded and executed by a processor to implement any of the methods in the first aspect.

[0051] In summary, the present application includes at least one of the following beneficial technical effects: The technical solution of the present application has the advantages of strong real-time performance, high detection accuracy, and low calculation cost, and is applicable to the drone safety monitoring requirements in complex signal environments, providing reliable technical support for the security protection of airports, energy facilities, and key areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a first flowchart of the drone detection method according to one embodiment of the present application.

[0053] Figure 2 is a frequency-domain diagram of the signal segment intercepted from the signal to be detected according to one embodiment of the present application.

[0054] Figure 3 is a second flowchart of the drone detection method according to one embodiment of the present application.

[0055] Figure 4 is a frequency-domain schematic diagram of the general reference sequence constructed according to one embodiment of the present application.

[0056] Figure 5 is a time-domain schematic diagram of the general reference sequence constructed according to one embodiment of the present application.

[0057] Figure 6 is a third flowchart of the drone detection method according to one embodiment of the present application.

[0058] Figure 7 is a schematic diagram of the distribution of detection peaks in the frequency-domain convolution result according to one embodiment of the present application.

[0059] Figure 8 is a schematic diagram of the distribution of detection peaks in the frequency-domain convolution result according to another embodiment of the present application.

[0060] Figure 9 is a schematic diagram of the distribution of detection peaks in the frequency-domain convolution result according to yet another embodiment of the present application.

[0061] Figure 10It is the fourth process schematic diagram of the UAV detection method according to one embodiment of the present application.

[0062] Figure 11 It is the fifth process schematic diagram of the UAV detection method according to one embodiment of the present application. Detailed implementation manners

[0063] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the following further describes the present application in detail with reference to the accompanying Figures 1-11 drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0064] Currently, common UAV detection technologies mainly focus on the following two methods:

[0065] Frequency band detection technology: Detect the radio frequency band where the UAV communication signal is located and detect it. For example, detect each sub-band in a specific frequency band list one by one to determine whether there is a UAV. This technical solution usually needs to scan each sub-band step by step, and the detection time is long. It is difficult to cope with UAVs with multi-band communication in complex environments, and the detection process is complex. When the frequency of the UAV communication signal changes, it is easy to cause missed detection or false detection.

[0066] Machine learning recognition technology based on spectrogram waterfall: Construct a sample data set by collecting the spectrogram waterfall of the UAV signal, and use a convolutional neural network (CNN) for training and classification to identify the UAV signal. Although this technical solution can theoretically achieve a high recognition accuracy, it also has the following deficiencies: The process of obtaining the spectrogram waterfall in actual detection takes a long time, especially the delay of signal feature extraction in complex environments is large, which cannot meet the real-time requirements; The machine learning model has a high demand for computing resources, and the training cost and implementation cost are relatively expensive, which is not conducive to large-scale promotion and application.

[0067] Therefore, the above-mentioned existing technologies all have certain limitations in efficiently and accurately detecting multi-band UAV signals in complex environments.

[0068] Based on this, the embodiments of the present application disclose a UAV detection method based on pilot synchronization sequence matching.

[0069] Referring to Figure 1 , a UAV detection method based on pilot synchronization sequence matching, the detection method includes:

[0070] Step S101, construct a general reference sequence;

[0071] Among them, the UAV video transmission signal / UAV remote control signal transmitted by the UAV during communication contains specific pilot synchronization sequences, and these sequences have strong signal characteristics. For example, they are composed of Zadoff-Chu sequences, have low cross-correlation and good periodic correlation, and are related to the signal bandwidth. Therefore, for UAV video transmission signals / UAV remote control signals with different bandwidths, a frequency-domain general reference sequence can be constructed. The general reference sequence is formed by embedding pilot synchronization sequences with different bandwidths into specific positions of an all-zero sequence, and then converted to the time domain through inverse fast Fourier transform (IFFT) to ensure its applicability to UAV signals with different bandwidths.

[0072] Specifically, the pilot synchronization sequence of the UAV video transmission signal / UAV remote control signal is composed of ZC sequences. The ZC sequence with a length of N can be expressed as:

[0073]

[0074] In the above formula, is the root index of the ZC sequence.

[0075] Generally, when the UAV communicates, a ZC sequence with a length of N is constructed in the frequency domain, placed in the center of an all-zero sequence with M points, and then the inverse Fourier transform (IFFT) is performed to obtain the pilot synchronization sequence in the time domain; M can be calculated by the following formula:

[0076]

[0077] In the above formula, represents rounding up, M is the length of the all-zero sequence, and N is the number of points of the pilot synchronization sequence.

[0078] It can be understood that the constructed general reference sequence can effectively match UAV video transmission signals / UAV remote control signals with different bandwidths, provide a standard reference for subsequent signal detection, and improve the adaptability and accuracy of detection.

[0079] Step S102: Receive the signal to be detected, and intercept a signal segment with a preset number of points from the signal to be detected;

[0080] Among them, in a complex signal environment, UAV signals are usually buried in a large amount of background noise. Therefore, by receiving signals in the radio frequency band and intercepting signal segments with a specific length, it can be ensured that each intercepted signal segment contains a complete pilot synchronization sequence.

[0081] Refer to Figure 2As shown, it is the frequency domain diagram of a 4096 - point signal segment intercepted from the signal to be detected in one embodiment of the present application. Specifically, the length of the preset number of points can be configured as an integer multiple n of the number of points in the pilot synchronization sequence, and n ≥ 2. For example, for a 18 MHz bandwidth corresponding to a signal length of 2048 points, the corresponding intercepted length can be configured as 4096 points to ensure the periodicity of the signal and the integrity of the spectrum distribution. Through reasonable signal interception, it can ensure that the required pilot synchronization sequence to be detected is completely retained, while reducing the amount of redundant data and the computational complexity of subsequent processing.

[0082] Step S103: Perform a fast Fourier transform on the signal segment to obtain the frequency domain representation of the signal to be detected;

[0083] Among them, the fast Fourier transform (FFT) is an efficient signal transformation method that can convert a time - domain signal to the frequency domain. In the frequency domain, the pilot synchronization sequence of the UAV signal has obvious spectral characteristics, and its low correlation and high peak value characteristics are convenient for subsequent detection. At the same time, converting the signal to the frequency domain can also reduce the interference of accumulated noise in the time domain on the signal characteristics. Through the FFT transformation, the complex signal processing in the time domain is simplified to the processing of characteristic points in the frequency domain, improving the efficiency and accuracy of signal detection.

[0084] In one embodiment of the present application, the signal to be detected that may contain a UAV signal received is denoted as s UAV , and at this time, it is impossible to determine whether there is a UAV signal in the signal to be detected. To ensure that the possible UAV pilot synchronization sequence can be completely included in the intercepted signal during each detection, every 2048 points, a 4096 - point signal is extracted from the received signal to be detected, and an FFT is performed to transform the signal to the frequency domain, denoted as S UAV . The present application does not need to divide the sub - frequency bands for the signal to be detected, but directly detects the signal to be detected that has been transformed to the frequency domain, that is, it detects the entire frequency band range of the signal to be detected, so the process is more simple and easy to implement.

[0085] Step S104: Perform a fast Fourier transform on the general reference sequence according to the preset number of points to obtain the frequency domain general reference sequence;

[0086] Among them, in order to achieve signal matching in the frequency domain, it is necessary to perform a fast Fourier transform on the general reference sequence with the same length as the signal to be detected to make it have the same frequency domain characteristic representation. The generation of the frequency domain general reference sequence is a process of converting the time - domain general reference sequence to the frequency domain, and its characteristics include the position and energy distribution of the pilot synchronization sequence.

[0087] Specifically, after obtaining the frequency domain general reference sequence, it can be matched with the frequency domain representation of the signal to be detected to lay a foundation for subsequent convolution operations.

[0088] Step S105: Perform a conjugate operation on the frequency-domain general reference sequence to obtain a frequency-domain reference signal;

[0089] Among them, before performing the convolution operation in the frequency domain, the general reference sequence needs to undergo a conjugate operation to enhance the matching performance. The conjugate operation can change the phase of the complex signal, making the convolution result have higher peak characteristics during matching, while reducing the influence of background noise on the convolution result. The frequency-domain reference signal generated through the conjugate operation further improves the performance of the convolution peak characteristics.

[0090] In the embodiment of the present application, perform a 4096-point FFT on the constructed general reference sequence and transform it to the frequency domain, denoted as S ref , and then take the conjugate of S ref , denoted as .

[0091] Step S106: Perform a convolution operation on the frequency-domain representation of the signal to be detected and the frequency-domain reference signal to obtain a frequency-domain convolution result;

[0092] Among them, the convolution operation is the core step of signal matching. In the frequency domain, the convolution operation can highlight the pilot synchronization sequence characteristics of the UAV signal by multiplying each point and accumulating the frequency-domain characteristics of the signal to be detected and the reference signal. When there is a UAV signal in the signal to be detected, obvious peaks will appear in the convolution result, while when there is no UAV signal, the convolution result tends to be smoothly distributed.

[0093] Specifically, perform a convolution on the signal to be detected that has been intercepted and transformed to the frequency domain and the general reference sequence that has been transformed to the frequency domain and conjugated . The calculation formula is:

[0094]

[0095] Among them, represents the convolution operation, is the result after convolution, is the reference signal after the conjugate operation.

[0096] In the embodiment of the present application, through the convolution operation, a relatively high signal-to-noise ratio (SNR) improvement can be obtained. For example, the UAV video transmission signal of 9 MHz can be improved by 28 dB, the UAV video transmission signal of 18 MHz can be improved by 31 dB, and the UAV remote control signal of 2.2 MHz can be improved by 22 dB, enabling better detection results to be obtained even when the SNR of the original signal to be detected is relatively low, greatly improving the detection performance of the UAV.

[0097] Step S107: Perform a constant false alarm rate detection on the frequency-domain convolution result to obtain the UAV detection result.

[0098] Among them, constant false alarm rate (CFAR) detection is a commonly used target detection technology, which determines the presence or absence of a target in a signal by setting preset threshold conditions. In the embodiments of the present application, CFAR detection dynamically adjusts the detection threshold according to the peak characteristics (such as amplitude and position) of the convolution result and combines the statistical characteristics of the background noise. By presetting preset threshold conditions such as the number of guard cells, the number of reference cells, and the detection threshold, the influence of target points on the threshold calculation is avoided. When there is a peak exceeding the detection threshold in the convolution result, it is considered that the UAV signal is detected.

[0099] Exemplarily, the number of guard cells can be set to 16, the number of reference cells can be set to 32, and the detection threshold can be set to 12 dB. The above parameters can be flexibly adjusted and configured according to the actual situation.

[0100] In some embodiments, the cell-averaging (CA-CFAR) strategy can be adopted. This strategy calculates the average value of the background noise on both sides of the target and is suitable for scenarios with uniform background noise distribution. The greater-of-average (GO-CFAR) strategy can also be adopted. This strategy selects the larger average value among the noise cells on both sides of the target and is suitable for high-noise environments, which can effectively avoid false alarms. The smaller-of-average (SO-CFAR) strategy can also be adopted. This strategy selects the smaller average value among the noise cells on both sides of the target and is suitable for scenarios with low noise or sparse targets, which can enhance the detection ability for weak targets.

[0101] In the above embodiments, a general reference sequence adapted to different bandwidths is constructed, combined with frequency domain transformation, convolution operation, and constant false alarm rate detection, realizing efficient and accurate detection of UAV signals. Utilizing the characteristics of the pilot synchronization sequence in the UAV video transmission signal / UAV remote control signal, this method performs precise matching in the frequency domain, greatly improving the signal-to-noise ratio, and effectively reducing false detection and missed detection through dynamic threshold detection. The technical solution of the present application has the advantages of strong real-time performance, high detection accuracy, and low calculation cost, and is suitable for the UAV safety monitoring requirements in complex signal environments, providing reliable technical support for the security guarantee of airports, energy facilities, and key areas.

[0102] As one of the embodiments of the present application, the present application can be applied to the detection of UAV video transmission signals to achieve UAV detection, that is, the UAV detection result is used to determine whether there is a UAV video transmission signal in the signal to be detected.

[0103] Referring to Figure 3 , when applying the UAV video transmission signal detection, the steps of constructing the general reference sequence in step S101 include:

[0104] Step S201, according to the bandwidth characteristics of the UAV video transmission signal, obtain the pilot synchronization sequence of the UAV video transmission signal; wherein, the pilot synchronization sequence is composed of ZC sequences;

[0105] Among them, the video transmission signal of the drone usually contains a pilot synchronization sequence during transmission. This is a fixed sequence designed for communication synchronization, with characteristics such as strong periodic correlation and small noise influence. The pilot synchronization sequence is composed of Zadoff-Chu (ZC) sequences. The mathematical properties of ZC sequences include constant amplitude property (all elements of the ZC sequence have the same amplitude, which helps to reduce the peak-to-average ratio), low cross-correlation (the cross-correlation between different ZC sequences is extremely low, and signals can be effectively distinguished even in a complex communication environment), and bandwidth dependence (the length and spectral characteristics of the ZC sequence are directly related to the bandwidth and sampling rate of the signal).

[0106] In some embodiments, the video transmission signal of the drone is usually transmitted at a specific bandwidth (such as 9 MHz or 18 MHz), and its system sampling rate is fixed. Therefore, the corresponding pilot synchronization sequence can be extracted according to these known bandwidth characteristics. For example, for a 9 MHz video transmission signal, the system sampling rate is 15.36 MHz, and the number of points of the pilot synchronization sequence is 1024 points, which includes a ZC sequence with a root index of 600 and a length of 601 points (denoted as ). Among them, when generating the pilot synchronization sequence of the 9 MHz video transmission signal, first construct a sequence of all zeros with 1024 points, and then make the 213th to 813th points equal to , that is, the pilot synchronization sequence of the 9 MHz video transmission signal in the frequency domain is obtained, denoted as ; perform IFFT on , and the pilot synchronization sequence of the 9 MHz video transmission signal in the time domain can be obtained .

[0107] For an 18 MHz video transmission signal, its system sampling rate is 30.72 MHz, and the number of points of the pilot synchronization sequence is 2048 points, which includes a ZC sequence with a root index of 1200 and a length of 1201 points (denoted as ). When generating the pilot synchronization sequence of the 18 MHz video transmission signal, first construct a sequence of all zeros with 2048 points, and then make the 425th to 1625th points equal to , that is, the pilot synchronization sequence of the 18 MHz video transmission signal in the frequency domain is obtained, denoted as ; perform IFFT on , and the pilot synchronization sequence of the 18 MHz video transmission signal in the time domain can be obtained .

[0108] It can be understood that by extracting the pilot synchronization sequence specific to the bandwidth, the unique characteristics of the drone video transmission signal can be obtained, providing a basis for constructing a general reference sequence in the future.

[0109] Step S202: Construct a sequence of all zeros with the number of pilot synchronization sequence points matching the sampling rate of the UAV video transmission signal, and embed the pilot synchronization sequences corresponding to different bandwidths into specified positions of the all-zero sequence to obtain the frequency-domain pilot synchronization sequence. Among them, the preset number of points is an integer multiple n of the number of pilot synchronization sequence points, where n≥2.

[0110] Among them, in order to make the pilot synchronization sequence have a correct position distribution in the frequency domain, it needs to be embedded in an all-zero sequence that matches the sampling rate of the UAV video transmission signal. The key to the embedding process is that the number of points of the all-zero sequence is consistent with the sampling rate of the video transmission signal to ensure that the generated frequency-domain signal can accurately reflect the spectral characteristics of the UAV signal. For example, for video transmission signals with different bandwidths, the number of points of the all-zero sequence corresponding to a 9 MHz bandwidth is 1024, while the number of points corresponding to an 18 MHz bandwidth is 2048.

[0111] Step S203: Convert the frequency-domain pilot synchronization sequence to the time domain through the inverse fast Fourier transform to obtain a general reference sequence.

[0112] Among them, the inverse fast Fourier transform (IFFT) converts the frequency-domain signal to the time-domain signal, and the time-domain signal is the complex representation of the frequency-domain signal on the time axis. By performing IFFT on the frequency-domain pilot synchronization sequence, a time-domain signal is generated, which is the general reference sequence and can reflect the characteristics of the UAV video transmission signal in time, such as periodicity and amplitude distribution.

[0113] In the above embodiments, based on the pilot synchronization sequence of the UAV video transmission signal characteristics, a general reference sequence applicable to signals with different bandwidths is constructed. During the construction process, by matching the bandwidth points, embedding the all-zero sequence, and performing the inverse fast Fourier transform, it is ensured that the spectral characteristics of the general reference sequence are consistent with the UAV signal. The finally generated general reference sequence can not only cover UAV signals with different bandwidths, but also effectively improve the accuracy of signal detection and the signal-to-noise ratio in subsequent matching operations.

[0114] Refer to Figure 4 、 Figure 5 , in one embodiment of the present application, based on the 18 MHz video transmission signal, two all-zero sequences with 2048 points can be constructed, denoted as and respectively. Let be equal to at points 725 to 1325, and let be equal to at points 425 to 1625. Then, add and point by point to obtain the general reference sequence in the frequency domain (as shown in Figure 3 ); perform IFFT on the general reference sequence in the frequency domain to obtain the general reference sequence in the time domain. (as shown in Figure 4 ).

[0115] Referring to Figure 6 , when applying UAV video transmission signal detection, the steps of performing constant false alarm rate detection on the frequency domain convolution result in step S107 to obtain the UAV detection result include:

[0116] Step S301, analyzing the peak characteristics in the frequency domain convolution result;

[0117] Among them, the peak characteristics include the position and amplitude of the peak;

[0118] Specifically, in the frequency domain, the peak characteristics of the convolution result are the main criteria for the presence or absence of the UAV signal. Convolution is essentially an evaluation of the correlation between the signal to be detected and the reference signal through mathematical operations. When there is a pilot synchronization sequence in the signal to be detected, peaks will appear in the convolution result. The characteristics of these peaks include position and amplitude. The position of the peak reflects the time offset or spectral distribution of the pilot synchronization sequence in the signal. If the peak position is consistent with the characteristic position of the reference signal, it indicates that the signal may contain the UAV video transmission signal; the amplitude of the peak reflects the strength of the match. A higher amplitude usually indicates that the signal contains a stronger pilot synchronization sequence, while a lower amplitude may be noise or a weak signal.

[0119] Referring to Figure 7 , Figure 8 , which are respectively schematic diagrams showing the distribution of detected peaks in the frequency domain convolution results of two different embodiments of the present application. Figure 7 There is a peak result and the peak amplitude is high, which reflects a relatively strong pilot synchronization sequence; Figure 8 There is no peak result, indicating that the signal to be detected does not match the frequency domain reference signal.

[0120] Step S302, determining whether the peak characteristics meet the preset threshold conditions; if so, jump to step S303; if not, jump to step S304;

[0121] Among them, the preset threshold conditions include the preset number of protection units, the preset number of reference units, and the preset detection threshold. The number of protection units is used to avoid the influence of the target signal itself on the background noise statistics; the number of reference units is the number of units used to statistically analyze the background noise characteristics. The more the number, the more stable the statistical result; the detection threshold is the dynamic threshold value calculated based on the background noise intensity statistically analyzed by the reference units. The threshold value will be adjusted according to the change of the background noise intensity and is used to distinguish the target signal and noise.

[0122] Referring to Figure 9As shown in the figure, it is a schematic diagram of the frequency-domain convolution result of one embodiment of the present application meeting the preset threshold condition. It can be clearly observed that the peak amplitude in the frequency-domain convolution result exceeds the threshold value and the position matches the expectation.

[0123] Step S303: Determine that there is a drone video transmission signal in the signal to be detected, that is, a drone is detected.

[0124] Step S304: Determine that there is no drone video transmission signal in the signal to be detected, that is, no drone is detected.

[0125] Specifically, when the peak amplitude in the convolution result exceeds the threshold value and the position matches the expectation, it can be determined that there is a drone video transmission signal in the signal to be detected. If the peak amplitude in the convolution result does not exceed the dynamic threshold, or the peak position does not match the expected characteristics, it can be determined that there is no drone video transmission signal in the signal to be detected. This determination is based on the statistical characteristics of CFAR detection and can effectively distinguish random noise or other non-correlated signals from the target signal.

[0126] In the above embodiment, by using the dynamic threshold strategy and combining the amplitude and position characteristics of the peak, the noise interference and the possibility of false alarms are greatly reduced. At the same time, the reliable detection of weak target signals is ensured, and the drone video transmission signal can be effectively extracted and recognized in a complex signal environment. This technical solution has the characteristics of high accuracy, high robustness and real-time performance, and can provide a fast and accurate solution for drone detection, meeting the security guarantee requirements of airports, energy facilities and other key areas.

[0127] As another embodiment of the present application, the present application can be applied to the detection of drone remote control signals to realize drone detection, that is, the drone detection result is used to determine whether there is a drone remote control signal in the signal to be detected.

[0128] Refer to Figure 10 , when applying the detection of drone remote control signals, the steps of constructing the general reference sequence in step S101 include:

[0129] Step S401: Obtain the pilot synchronization sequence of the drone remote control signal according to the bandwidth characteristics of the drone remote control signal; specifically, the pilot synchronization sequence is composed of ZC sequences.

[0130] Among them, the pilot synchronization sequence of the drone remote control signal is an important part for signal synchronization in a specific communication protocol. Its core characteristics are stability and low correlation. The pilot synchronization sequence is usually composed of Zadoff-Chu (ZC) sequences, and the length of the pilot synchronization sequence directly depends on the bandwidth characteristics and sampling rate of the signal.

[0131] In some embodiments, the common bandwidth of the UAV remote control signal is 2.2 MHz. For a 2.2-MHz remote control signal, its system sampling rate is 15.36 MHz, and the number of pilot synchronization sequence points is 1,024, which includes a ZC sequence with a length of 145 points and a root index of 121 (denoted as ). When generating the pilot synchronization sequence of the 2.2-MHz remote control signal, first construct a sequence of all zeros with 1,024 points, and then make the 441st to 585th points equal to , that is, the pilot synchronization sequence of the 2.2-MHz remote control signal in the frequency domain is obtained, denoted as S 2 ; perform IFFT on S 2 , and the pilot synchronization sequence S of the 2.2-MHz remote control signal in the time domain can be obtained 2 .

[0132] Step S402: Construct a sequence of all zeros whose number of pilot synchronization sequence points matches the sampling rate of the UAV remote control signal, and embed the pilot synchronization sequences corresponding to different bandwidths into the specified positions of the sequence of all zeros to obtain the pilot synchronization sequence in the frequency domain; where the preset number of points is an integer multiple n of the number of pilot synchronization sequence points, n ≥ 2;

[0133] Specifically, the number of points of the sequence of all zeros needs to match the sampling rate and the time domain length of the remote control signal to ensure that the frequency domain characteristics can be fully mapped. For example, for a 2.2-MHz bandwidth remote control signal with a sampling rate of 15.36 MHz, the number of points of the sequence of all zeros should be 1,024 corresponding to the sampling rate. Then, according to the modulation method and frequency domain characteristics of the UAV remote control signal, embed the pilot synchronization sequence into the center or specified position of the sequence of all zeros to ensure that its signal spectrum position in the frequency domain is consistent with the actual spectrum range of the remote control signal.

[0134] Step S403: Convert the pilot synchronization sequence in the frequency domain to the time domain through the inverse fast Fourier transform to obtain a general reference sequence.

[0135] Among them, the inverse fast Fourier transform (IFFT) is the core algorithm for converting a frequency domain signal to the time domain, and its function is to generate the corresponding time domain signal from the frequency domain characteristics.

[0136] In the above embodiments, combined with the bandwidth characteristics, frequency domain embedding, and time domain signal generation, this solution can generate a reference template consistent with the actual remote control signal, ensuring the matching degree and robustness of subsequent detections.

[0137] Referring to Figure 11 , when applying the detection of the UAV remote control signal, the steps of performing a constant false alarm rate detection on the frequency domain convolution result in step S107 to obtain the UAV detection result include:

[0138] Step S501: Analyze the peak characteristics in the frequency domain convolution result;

[0139] Among them, during the detection of the UAV remote control signal, the peak feature of the frequency-domain convolution result is an important basis for judging the existence of the target signal. The convolution operation matches the signal to be detected with a general reference signal through a matching operation, and concentrates the matching characteristics on the peak. In the convolution result, the peak position corresponds one-to-one with the position of the pilot synchronization sequence in the signal to be detected, reflecting the offset characteristics of the target signal in the frequency domain, which can be used for subsequent signal time synchronization or positioning; the peak amplitude indicates the matching degree between the signal to be detected and the reference signal. The higher the amplitude, the stronger the matching correlation, and the more likely it is that there is a UAV remote control signal.

[0140] Step S502, determine whether the peak feature meets the preset threshold condition; if so, jump to step S503; if not, jump to step S504;

[0141] Among them, the preset threshold condition is the key basis for distinguishing real signals and background noise, and usually includes the number of protection units, the number of reference units, and the detection threshold.

[0142] Step S503, determine that there is a UAV remote control signal in the signal to be detected, that is, the UAV is detected;

[0143] Step S504, determine that there is no UAV remote control signal in the signal to be detected, that is, the UAV is not detected.

[0144] Among them, when the peak feature meets the preset threshold condition, it can be determined that there is a UAV remote control signal in the signal to be detected. The basis for the determination lies in the high correlation characteristic of the pilot synchronization sequence, that is, the pilot synchronization sequence can only match the reference signal to generate a significant peak when the UAV signal exists.

[0145] In the above embodiments, by analyzing the peak features (including position and amplitude) in the frequency-domain convolution result and combining the dynamic preset threshold conditions (the number of protection units, the number of reference units, and the detection threshold), high-precision detection of the UAV remote control signal is achieved. When the peak feature meets the threshold condition, the system can reliably judge the existence of the UAV signal; otherwise, the interference of background noise or other non-target signals can be excluded.

[0146] The embodiment of the present application also discloses a UAV detection system based on pilot synchronization sequence matching.

[0147] A UAV detection system based on pilot synchronization sequence matching, the detection system includes:

[0148] A sequence construction module, used to construct a general reference sequence;

[0149] A signal receiving module, used to receive the signal to be detected;

[0150] A signal segment intercepting module, configured to intercept a signal segment with a preset number of points from a signal to be detected;

[0151] A signal segment processing module, configured to perform a fast Fourier transform on the signal segment to obtain a frequency-domain representation of the signal to be detected;

[0152] A frequency-domain conversion module, configured to perform a fast Fourier transform on a general reference sequence according to a preset number of points to obtain a frequency-domain general reference sequence;

[0153] A conjugate operation module, configured to perform a conjugate operation on the frequency-domain general reference sequence to obtain a frequency-domain reference signal;

[0154] A convolution module, configured to perform a convolution operation on the frequency-domain representation of the signal to be detected and the frequency-domain reference signal to obtain a frequency-domain convolution result;

[0155] A constant false alarm rate detection module, configured to perform a constant false alarm rate detection on the frequency-domain convolution result to obtain a UAV detection result.

[0156] A UAV detection system based on pilot synchronization sequence matching according to an embodiment of the present application can implement any of the above UAV detection methods, and the specific working processes of each module in the UAV detection system can refer to the corresponding processes in the above method embodiments.

[0157] In several embodiments provided in the present application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0158] An embodiment of the present application also discloses a computer device.

[0159] The computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements a UAV detection method based on pilot synchronization sequence matching as described above.

[0160] An embodiment of the present application also discloses a computer-readable storage medium.

[0161] The computer-readable storage medium stores a computer program that can be loaded and executed by a processor to implement any of the above UAV detection methods based on pilot synchronization sequence matching.

[0162] Among them, the computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device; the program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination of the above.

[0163] It should be noted that in the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0164] The above are all preferred embodiments of the present application. The protection scope of the present application is not limited thereby. Any feature disclosed in this specification (including the abstract and drawings), unless specifically described, can be replaced by other equivalent or similar-purpose alternative features. That is, unless specifically described, each feature is only an example of a series of equivalent or similar features.

Claims

1. A drone detection method based on pilot synchronization sequence matching, characterized in that: The detection method comprises: Constructing a universal reference sequence; wherein the universal reference sequence is constructed based on the ZC sequence; receiving a signal to be detected, and extracting a signal segment having a length of a preset number of points from the signal to be detected; Performing a fast Fourier transform on the signal segment to obtain a frequency domain representation of the signal to be detected; Performing a fast Fourier transform on the universal reference sequence according to the preset number of points to obtain a frequency domain universal reference sequence; Performing a conjugate operation on the frequency domain universal reference sequence to obtain a frequency domain reference signal; Performing a convolution operation on the frequency domain representation of the signal to be detected and the frequency domain reference signal to obtain a frequency domain convolution result; A constant false alarm rate test is performed on the frequency domain convolution result to obtain a drone detection result.

2. The method for detecting a drone based on pilot synchronization sequence matching according to claim 1, characterized in that: The drone detection result is used to determine whether there is a drone image transmission signal in the signal to be detected; The step of constructing a universal reference sequence comprises: According to the bandwidth characteristics of the UAV image transmission signal, a pilot synchronization sequence of the UAV image transmission signal is obtained, where the pilot synchronization sequence is composed of a ZC sequence; Construct an all-zero sequence whose pilot synchronization sequence points match the sampling rate of the drone image transmission signal, and embed the pilot synchronization sequences corresponding to different bandwidths into the specified positions of the all-zero sequence to obtain a frequency domain pilot synchronization sequence; wherein the preset number of points is an integer multiple n of the number of points of the pilot synchronization sequence, and n≥2; The frequency domain pilot synchronization sequence is converted into the time domain through inverse fast Fourier transform to obtain the universal reference sequence.

3. The method for detecting a drone based on pilot synchronization sequence matching according to claim 2, characterized in that: The steps of performing constant false alarm rate detection on the frequency domain convolution result to obtain the drone detection result include: Analyzing peak features in the frequency domain convolution result; Determining whether the peak characteristic meets a preset threshold condition; If so, it is determined that there is a drone image transmission signal in the signal to be detected, that is, the drone is detected; if not, it is determined that there is no drone image transmission signal in the signal to be detected, that is, the drone is not detected.

4. The method for detecting a drone based on pilot synchronization sequence matching according to claim 1, characterized in that: The drone detection result is used to determine whether there is a drone remote control signal in the signal to be detected; The step of constructing a universal reference sequence comprises: According to the bandwidth characteristics of the UAV remote control signal, a pilot synchronization sequence of the UAV remote control signal is obtained, where the pilot synchronization sequence is composed of a ZC sequence; Construct an all-zero sequence whose pilot synchronization sequence points match the sampling rate of the drone remote control signal, and embed the pilot synchronization sequences corresponding to different bandwidths into the specified positions of the all-zero sequence to obtain a frequency domain pilot synchronization sequence; wherein the preset number of points is an integer multiple n of the number of points of the pilot synchronization sequence, and n≥2; The frequency domain pilot synchronization sequence is converted into the time domain through inverse fast Fourier transform to obtain the universal reference sequence.

5. The method for detecting a drone based on pilot synchronization sequence matching according to claim 4, characterized in that: The steps of performing constant false alarm rate detection on the frequency domain convolution result to obtain the drone detection result include: Analyzing peak features in the frequency domain convolution result; Determining whether the peak characteristic meets a preset threshold condition; If so, it is determined that the drone remote control signal exists in the signal to be detected, that is, the drone is detected; if not, it is determined that the drone remote control signal does not exist in the signal to be detected, that is, the drone is not detected.

6. A drone detection method based on pilot synchronization sequence matching according to any one of claims 1 to 5, characterized in that: The constant false alarm rate detection includes a bilateral average constant false alarm rate strategy, a two-side unit average selection strategy or a two-side unit average selection strategy.

7. The method for detecting a drone based on pilot synchronization sequence matching according to claim 3 or 5, characterized in that: The preset threshold conditions include a preset number of protection units, a preset number of reference units and a preset detection threshold.

8. A drone detection system based on pilot synchronization sequence matching, characterized in that: The detection system comprises: A sequence construction module, used to construct a universal reference sequence; wherein the universal reference sequence is constructed based on the ZC sequence; A signal receiving module, used for receiving a signal to be detected; A signal segment interception module, used for intercepting a signal segment with a length of a preset number of points from the signal to be detected; A signal segment processing module, used for performing a fast Fourier transform on the signal segment to obtain a frequency domain representation of the signal to be detected; A frequency domain conversion module, used for performing a fast Fourier transform on the universal reference sequence according to the preset number of points to obtain a frequency domain universal reference sequence; A conjugate operation module, used for performing a conjugate operation on the frequency domain universal reference sequence to obtain a frequency domain reference signal; A convolution module, used for performing a convolution operation on the frequency domain representation of the signal to be detected and the frequency domain reference signal to obtain a frequency domain convolution result; The constant false alarm rate detection module is used to perform constant false alarm rate detection on the frequency domain convolution result to obtain the drone detection result.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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