Unknown unmanned aerial vehicle signal detection method and device, computer device and storage medium

Abstract

The application relates to an unknown unmanned aerial vehicle signal detection method and device, computer equipment and a storage medium. IQ data obtained from an unmanned aerial vehicle signal is used to generate a slow-time delay correlation coefficient diagram. Periodic repetition signals are judged according to the characteristics of the periodic repetition signals presented in the slow-time delay correlation coefficient diagram. If the periodic repetition signals exist, the start and end times and the repetition period of the periodic repetition signals are estimated. The periodic repetition signals are rearranged and phase-aligned according to the start and end times and the repetition period, an aligned signal matrix is obtained, the signal fingerprint of the unknown unmanned aerial vehicle signal is obtained according to the average characteristics of the aligned signal matrix, and the detection of the unknown unmanned aerial vehicle signal is realized. The method can be used for the rapid detection and estimation of the signals of unknown unmanned aerial vehicles, and then passive detection is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of passive detection sensing, in particular to an unknown unmanned aerial vehicle signal detection method and device, computer equipment and storage medium. BACKGROUND

[0002] With the rapid development and wide application of unmanned aerial vehicles, they have shown great value in aerial photography, agricultural plant protection, logistics transportation and other fields. However, the disordered use of unmanned aerial vehicles has also brought a series of safety hazards, such as illegal intrusion into sensitive areas, interference with normal communication, and theft of private data, so it is crucial to effectively detect and control unmanned aerial vehicles. Passive detection, processing of electromagnetic signals emitted by unmanned aerial vehicles, identification and positioning of unmanned aerial vehicles, with the advantages of strong concealment and not easy to be disturbed, has become one of the main technical means for unmanned aerial vehicle detection.

[0003] The existing passive detection technology of unmanned aerial vehicles mainly relies on matching of signal feature library, which leads to a significant deficiency: it can only detect known models of unmanned aerial vehicle signals. When facing new models of unmanned aerial vehicles that have not been recorded in the feature library, traditional passive detection technology often has no choice but to fail to discover and identify them in time, which brings great challenges to safety precautions. SUMMARY

[0004] Therefore, it is necessary to provide an unknown unmanned aerial vehicle signal detection method, device, computer equipment and storage medium for passive detection of periodic repetitive signals of unknown unmanned aerial vehicles.

[0005] An unknown unmanned aerial vehicle signal detection method, the method comprising:

[0006] Obtaining an unknown unmanned aerial vehicle signal, the unknown unmanned aerial vehicle signal being received by a radio frequency antenna;

[0007] Generating a slow-time delay correlation coefficient graph according to IQ data obtained from the unknown unmanned aerial vehicle signal;

[0008] According to the characteristics of the periodic repetitive signal presented in the slow-time delay correlation coefficient graph, judging the periodic repetitive signal, if there is a periodic repetitive signal, estimating the start and end time and the repetition period of the periodic repetitive signal;

[0009] According to the start and end time and the repetition period, rearranging and phase aligning the periodic repetitive signal to obtain an aligned signal matrix;

[0010] According to the average characteristics of the aligned signal matrix, obtaining a signal fingerprint of the unknown unmanned aerial vehicle signal to realize detection of the unknown unmanned aerial vehicle signal.

[0011] In one embodiment, the slow-time delay correlation coefficient map is generated according to the IQ data, comprising:

[0012] The IQ data is divided into multiple data segments with overlapping parts according to a preset maximum period, an overlap rate and a segment length;

[0013] Each of the data segments is subjected to short-time Fourier transform, and a short-time Fourier autocorrelation spectrum representing the frequency domain energy distribution of each of the data segments is obtained;

[0014] Each of the data segments in the short-time Fourier autocorrelation spectrum is subjected to inverse Fourier transform, and a modulus value thereof is taken to obtain a slow-time delay correlation coefficient matrix, and then the slow-time delay correlation coefficient map is obtained.

[0015] In one embodiment, the periodic signal appears as a series of vertical lines in the slow-time delay correlation coefficient map, wherein the vertical lines correspond to an integer multiple of the period of the signal in the length of the horizontal coordinate, and the start / end position of the vertical line in the vertical coordinate is the start / end time of the signal.

[0016] In one embodiment, when the periodic signal is detected based on the slow-time delay correlation coefficient map:

[0017] A preset sliding window is used to slide in the delay direction for each slow-time step on the slow-time delay correlation coefficient map, and a minimum value of the difference between the average amplitude of the current center window and the average amplitude of the left and right windows is calculated to obtain a regional maximum contrast degree;

[0018] Threshold segmentation is performed according to a preset threshold and the maximum contrast degree of each region in the slow-time delay correlation coefficient map to obtain a binary map, and a duty cycle is calculated along the slow-time dimension of the binary map to obtain a candidate delay set corresponding to the vertical lines in the slow-time delay correlation coefficient map;

[0019] The candidate delay set is subjected to pulse repetition interval transformation to determine whether there is a periodic signal, and if there is, the repetition period and the start / end time of the periodic signal are obtained.

[0020] In one embodiment, the determination of whether there is a periodic signal by using pulse repetition interval transformation on the candidate delay set comprises:

[0021] Multiple candidate periods are generated at equal intervals according to a preset maximum period, the candidate delay set is taken as a pulse train, and the pulse repetition interval transformation probability of each of the candidate periods is calculated;

[0022] The candidate period corresponding to the maximum pulse repetition interval transformation probability is selected as an estimated repetition period, and the number of pulses conforming to the estimated repetition period is counted;

[0023] If the counted number of pulses exceeds a preset threshold, it is determined that the unknown unmanned aerial vehicle signal contains a periodic repetition signal.

[0024] In one embodiment, rearranging and phase aligning the periodic repetition signal according to the start and end time and the repetition period comprises:

[0025] According to the estimated repetition period and the start and end time, the corresponding IQ data in the start and end time is segmented and rearranged to obtain a plurality of rearranged signals.

[0026] For the plurality of rearranged signals, taking the first rearranged signal as a reference, the maximum correlation coefficient and the corresponding delay of the remaining segments relative to the first rearranged signal are calculated based on FFT, and each rearranged signal is shifted and aligned according to the delay to obtain the aligned signal matrix.

[0027] In one embodiment, the signal fingerprint of the unknown unmanned aerial vehicle signal is obtained according to the average characteristics of the aligned signal matrix, comprising:

[0028] Based on the aligned signal matrix, the root mean square of each column of data is calculated along the slow time direction.

[0029] The root mean square of each column of data is subjected to Otsu two-class adaptive threshold segmentation and morphological filtering to obtain the start and end indexes of the periodic repetition part.

[0030] On the aligned signal matrix, the signal fingerprint is obtained by averaging along the slow time direction according to the start and end indexes of the periodic repetition part.

[0031] The application also provides an unknown unmanned aerial vehicle signal detection device, which comprises:

[0032] A signal acquisition module is configured to acquire an unknown unmanned aerial vehicle signal, which is received by a radio frequency antenna.

[0033] A slow time delay correlation coefficient map generation module is configured to generate a slow time delay correlation coefficient map based on IQ data obtained from the unknown unmanned aerial vehicle signal.

[0034] A periodic repetition signal judgment module is configured to judge the presence of a periodic repetition signal according to the characteristics of the periodic repetition signal presented in the slow time delay correlation coefficient map, and estimate the start and end time and the repetition period of the periodic repetition signal if the periodic repetition signal exists.

[0035] An aligned signal matrix obtaining module is configured to rearrange and phase align the periodic repetition signal according to the start and end time and the repetition period to obtain an aligned signal matrix.

[0036] The signal detection module is configured to obtain a signal fingerprint of the unknown unmanned aerial vehicle signal according to the average feature of the alignment signal matrix, so as to realize detection of the unknown unmanned aerial vehicle signal.

[0037] A computer device includes a memory and a processor, the memory stores a computer program, and the processor realizes the steps in the unknown unmanned aerial vehicle signal detection method when executing the computer program.

[0038] A computer readable storage medium stores a computer program, and the computer program realizes the steps in the unknown unmanned aerial vehicle signal detection method when executed by a processor.

[0039] The unknown unmanned aerial vehicle signal detection method, device, computer device and storage medium, by generating a slow-time delay correlation coefficient diagram according to the IQ data obtained from the unmanned aerial vehicle signal, judging the periodic repeating signal according to the feature presented by the periodic repeating signal in the slow-time delay correlation coefficient diagram, if there is a periodic repeating signal, estimating the start and end time and the repetition period of the periodic repeating signal, rearranging and phase aligning the periodic repeating signal according to the start and end time and the repetition period, obtaining an alignment signal matrix, obtaining a signal fingerprint of the unknown unmanned aerial vehicle signal according to the average feature of the alignment signal matrix, so as to realize detection of the unknown unmanned aerial vehicle signal. The method can be used for rapid detection and estimation of the signal of unknown unmanned aerial vehicles, and then passive detection is realized. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 It is a flowchart of the unknown unmanned aerial vehicle signal detection method in one embodiment;

[0041] Figure 2 It is a schematic diagram of a pulse burst IQ data of a certain unmanned aerial vehicle image transmission signal in an experiment;

[0042] Figure 3 It is Figure 2 a schematic diagram of the autocorrelation result of the signal in the embodiment;

[0043] Figure 4 It is the signal period according to the peak position estimation in the embodiment; Figure 3 Figure 2 It is a schematic diagram of N groups of signals obtained by segmenting the signal in the embodiment, wherein, Figure 4 (a) represents a schematic diagram of the average signal after period segmentation, Figure 4 (b) represents a schematic diagram of the sliding window correlation between each segmented signal and the average signal after period segmentation, Figure 4 (c) represents a schematic diagram of the average correlation;

[0044] Figure 5 It is a schematic diagram of the periodic repeating signal obtained by extracting the signal between the start and end positions of the strong correlation from the average signal, wherein,​Figure 5 (a) represents a schematic diagram of the repeated signal obtained by analysis, Figure 5 (b) represents a schematic diagram of the input signal, Figure 5 (c) represents a schematic diagram of the sliding window delay correlation of the repeated signal and the input signal;

[0045] Figure 6 is a structural block diagram of an unknown unmanned aerial vehicle signal detection device in an embodiment;

[0046] Figure 7 is an internal structural diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0047] In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the 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.

[0048] As shown in Figure 1 , in the present application, an unknown unmanned aerial vehicle signal detection method is provided, which specifically includes the following steps:

[0049] Step S100, an unknown unmanned aerial vehicle signal is obtained, which is received by a radio frequency antenna.

[0050] Step S110, according to the IQ data obtained from the unknown unmanned aerial vehicle signal, a slow-time delay correlation coefficient diagram is generated.

[0051] Step S120, according to the characteristics of the periodic repeated signal presented in the slow-time delay correlation coefficient diagram, a judgment of the periodic repeated signal is made, and if the periodic repeated signal exists, the start and end time and the repetition period of the periodic repeated signal are estimated.

[0052] Step S130, according to the start and end time and the repetition period, the periodic repeated signal is rearranged and phase-aligned to obtain an aligned signal matrix.

[0053] Step S140, according to the average characteristics of the aligned signal matrix, a signal fingerprint of the unknown unmanned aerial vehicle signal is obtained to realize the detection of the unknown unmanned aerial vehicle signal.

[0054] In the present embodiment, for the problem of unknown model unmanned aerial vehicle communication signal detection, fully considering that most unmanned aerial vehicle communication signals have certain periodic characteristics, the methods of signal processing, image processing and data processing are comprehensively used to judge the existence and start and end time of the period, and estimate the signal period and extract the periodic repeated part as the signal fingerprint.

[0055] Specifically, the periodic characteristics of the communication signals of the multiple unmanned aerial vehicles are presented, such as the regular spectrum spreading and shrinking in spread spectrum signals, the periodic start identification of a synchronization frame of the communication signals, the periodic redundant data addition of a cyclic prefix, and the like. These periodic characteristics contain rich signal information. For passive detection of unknown unmanned aerial vehicles, the periodic characteristics of the signals can be fully mined and utilized to perform signal detection, the signal period is estimated through an advanced signal processing algorithm, and the periodic signal is extracted as a unique signal fingerprint, thereby providing a new technical direction for effectively detecting unknown unmanned aerial vehicles.

[0056] In step S100, the unknown unmanned aerial vehicle signal is a passive communication signal received through a radio frequency antenna.

[0057] In step S110, IQ data received and down-converted to a baseband by the radio frequency are processed, and a slow-time delay correlation coefficient diagram is generated according to the IQ data, including: according to a preset maximum period , an overlap rate, and a segment length, the IQ data is divided into multiple data segments with overlapping parts, each data segment is subjected to a short-time Fourier transform, and a short-time Fourier autocorrelation spectrum representing the frequency energy distribution of each data segment is obtained, each data segment in the short-time Fourier autocorrelation spectrum is subjected to an inverse Fourier transform, and a modulus value thereof is taken, thereby obtaining a slow-time delay correlation coefficient matrix, and then obtaining the slow-time delay correlation coefficient diagram.

[0058] Specifically, the segment length is determined by the following formula:

[0059] ;

[0060] In the above formula, fs represents a sampling rate, represents that the segment length should cover a multiple of the maximum period, and is usually set to 3, and can also be set to other values according to different situations.

[0061] Preferably, the overlap rate can be set to 1 / 2.

[0062] Further, after the segmenting, the multiple data segments obtained can be represented as , wherein represents the number of data segments.

[0063] Then, each data segment is subjected to a Fourier transform, and a modulus square value of the Fourier transform is taken, thereby obtaining a short-time Fourier autocorrelation spectrum. Each segment of the short-time Fourier autocorrelation spectrum is subjected to an inverse Fourier transform, and a modulus value is taken, thereby obtaining a slow-time delay correlation coefficient matrix , i.e., a slow-time delay correlation coefficient diagram (slow-time ​delay The correlation coefficient graph is shown in FIG. 2, in which the horizontal coordinate is represented as:

[0064]

[0065] In the embodiment, the periodic signal appears as a series of vertical lines in the slow-time delay correlation coefficient graph, in which the vertical line corresponds to the horizontal coordinate length value being an integer multiple of the signal period, and the start / end position of the vertical line corresponding to the vertical coordinate is the start / end time of the signal. In step S120, based on the above characteristics, the periodic signal is detected by processing the slow-time delay correlation coefficient graph.

[0066] In the embodiment, when detecting the periodic signal based on the slow-time delay correlation coefficient graph, a preset sliding window is used to slide along the delay direction for each slow-time step on the slow-time delay correlation coefficient graph, and the minimum value of the difference between the average amplitude of the current center window and the average amplitudes of the left and right windows is calculated to obtain the regional maximum contrast degree. According to the preset threshold and the maximum contrast degree of each region in the slow-time delay correlation coefficient graph, threshold segmentation is performed to obtain a binary graph, and the duty cycle is calculated along the slow-time dimension of the binary graph, so as to obtain a candidate delay set corresponding to the vertical line on the slow-time delay correlation coefficient graph. The candidate delay set is used to determine whether there is a periodic signal by using the pulse repetition interval transformation, and if there is, the repetition period and the start / end time of the periodic signal are obtained.

[0067] Specifically, in the slow-time delay correlation coefficient graph, the regional maximum contrast degree is verified by sliding window inspection along the delay direction (column) for each slow-time step (each row), that is, the minimum value of the difference between the average value of the center window and the average values of the left and right windows, which is represented as:

[0068]

[0069] In the above formula, represents the average value of the center window, , represent the average values of the left and right windows, respectively.

[0070] Preferably, the size of the center window can be set to 1-3 sampling points, and the size of the left and right windows can be set to 1-2 sampling points.

[0071] Further, a fixed threshold is used to perform threshold segmentation on , so as to obtain a binary graph . The threshold is equivalent to the signal-to-noise ratio threshold, and is preferably 5 dB.

[0072] In the embodiment, the duty cycle is calculated along the slow-time dimension of the binary graph ​​The duty cycle is calculated along the slow time dimension, that is, the duty cycle with a value of "1". The process is expressed as follows:

[0073] ;

[0074] In the above formula, for Number of slow-time sampling points.

[0075] Furthermore, regarding Search for the maximum value and compare the found maximum value with the preset maximum value. By comparing these values, we obtain the set of possible delay times, i.e., the candidate delay set. Each delay in this set corresponds to a vertical line, and the minimum interval is not less than... Non-Maximum Suppression (NMS) is used for deduplication to refine the candidate delay set. The selection process is carried out. Among them, the preferred ones are... The value can be set to 0.5.

[0076] In this embodiment, determining whether a periodic repetitive signal exists in the candidate delay set by utilizing pulse repetition interval transformation includes: generating multiple candidate periods at equal intervals according to a preset maximum period, using the candidate delay set as a pulse train, calculating the pulse repetition interval transformation probability corresponding to each candidate period, selecting the candidate period corresponding to the largest pulse repetition interval transformation probability as the estimated repetition period, and counting the number of pulses that conform to the estimated repetition period. If the number of pulses counted exceeds a preset threshold, it is determined that a periodic repetitive signal exists in the unknown UAV signal.

[0077] Specifically, each segment of data in the candidate delay set Viewed as a pulse train For pulse time, the periodicity corresponding to the periodic signal. The correlation and periodicity test of vertical lines are performed using the pulse period estimation method.

[0078] Furthermore, based on the preset maximum possible cycle According to preset intervals Generate several possible candidate cycles And calculate each candidate period The probability of the corresponding pulse repetition interval change is expressed as:

[0079] ;

[0080] In the above formula, and These are the corresponding delays and correlation value.

[0081] and the maximum corresponding As the estimated repetition period, the process is represented as:

[0082]

[0083] Further, the pulse that meets the estimated repetition period relationship is calculated, represented as:

[0084] ;

[0085] Then, according to and the threshold value, if greater than the threshold value, it is considered that there is a periodic signal, and the corresponding period is the estimated repetition period. Preferably, the threshold value is 3.

[0086] In step S130, the periodic repetition signal is rearranged and phase-aligned according to the start and end time and the repetition period, including: according to the estimated repetition period and the start and end time, the corresponding IQ data in the start and end time is segmented and rearranged to obtain a plurality of rearranged signals. Wherein the plurality of rearranged signals are represented as a complex matrix, which includes segments, the length of which is rearranged signal. For the plurality of rearranged signals, the first rearranged signal is taken as a reference, and the maximum correlation coefficient and the corresponding delay of the remaining segments and the first rearranged signal are calculated based on FFT, and each segment of the rearranged signal is shifted and aligned according to the delay, to obtain an aligned signal matrix.

[0087] Specifically, the length of the rearranged signal is represented as:

[0088] .

[0089] In step S140, the signal fingerprint of the unknown unmanned aerial vehicle signal is obtained according to the average characteristics of the aligned signal matrix, including: based on the aligned signal matrix, the root mean square of each column of data is calculated along the slow time direction, the root mean square of each column of data is calculated along the slow time direction, Otsu two-class adaptive threshold segmentation and morphological filtering are performed to obtain the start and end indexes of the periodic repetition part, and the signal fingerprint is obtained by averaging along the slow time direction on the aligned signal matrix according to the start and end indexes of the periodic repetition part.

[0090] In this paper, the effectiveness of the method is also proved by experiments.

[0091] As Figure 2 shown, it is an IQ data diagram of a certain unmanned aerial vehicle collected, wherein the sampling rate is 153.6MHz.

[0092] As shown in Figure 3 , it is a schematic diagram of the cross-correlation result of the middle signal, in which the horizontal axis is the delay point number, and the vertical axis is the normalized correlation coefficient. The normalized correlation coefficient is 1 at 0, and there are multiple large and equidistant normalized correlation peaks other than 0. The position of the peak corresponds to the signal period. Figure 2

[0093] As shown in Figure 4 , it is a signal period versus the peak position estimation according to Figure 3 , in which the N groups of signals are obtained by segmenting the signal according to the signal period. The average signal including the N groups of signals is shown in the schematic diagram, and the sliding window correlation of each group of signals with the average signal is shown in the schematic diagram. It can be seen from the two schematic diagrams that there is strong correlation at some positions. The signal at the position corresponding to the strong correlation is the periodic repetition signal. Figure 2 Figure 4 The average of the sliding window correlation coefficients is also included in , and a simple threshold bisection of the average can obtain the start and end positions of the periodic repetition signal.

[0094] Figure 5 As shown in Figure 5 , the signal between the start and end positions of the strong correlation extracted from the average signal is the periodic repetition signal, Figure 5 , which includes the input signal schematic diagram Figure 5 (a), the repetitive signal schematic diagram obtained by analysis Figure 5 (b), and the sliding delay correlation schematic diagram of the input signal and the extracted periodic repetition signal (c). It can be seen from the sliding delay correlation schematic diagram that there are signals highly correlated (the normalized correlation coefficient is close to 1) with the periodic repetition signal at several positions in the input signal, which verifies the effectiveness of the extracted periodic repetition signal.

[0095] In the above unknown unmanned aerial vehicle signal detection method, for the problem of detecting unknown model unmanned aerial vehicle communication signals, the signal period characteristics are fully utilized, and signal processing, image processing and data processing methods are comprehensively used. By sequentially generating a slow time-delay correlation coefficient diagram, making a period signal decision and period estimation, rearranging and aligning the signal based on the period and start and end time, segmenting the periodic repetition part and extracting the signal fingerprint, etc., it is determined whether the period exists and the start and end time, the signal period is estimated, and the periodic repetition part is extracted as the signal fingerprint. The signal detection needs of unknown model unmanned aerial vehicle communication detection are met, and the adaptability of the unmanned aerial vehicle passive detection system to unknown model unmanned aerial vehicles is effectively enhanced.

[0096] Figure 1 ​​The steps in the flowcharts are displayed in sequence according to the arrows, but the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, Figure 1 At least part of the steps in the flowcharts can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0097] In one embodiment, as shown in Figure 6 An unknown unmanned aerial vehicle signal detection device is provided, comprising: a signal acquisition module 200, a slow-time delay correlation coefficient map generation module 210, a periodic repetition signal judgment module 220, an aligned signal matrix obtaining module 230, and a signal detection module 240, wherein:

[0098] The signal acquisition module 200 is configured to acquire an unknown unmanned aerial vehicle signal, wherein the unknown unmanned aerial vehicle signal is received by a radio frequency antenna.

[0099] The slow-time delay correlation coefficient map generation module 210 is configured to generate a slow-time delay correlation coefficient map according to IQ data obtained from the unknown unmanned aerial vehicle signal.

[0100] The periodic repetition signal judgment module 220 is configured to judge a periodic repetition signal according to a feature of the periodic repetition signal presented in the slow-time delay correlation coefficient map, and estimate start and end times and a repetition period of the periodic repetition signal if the periodic repetition signal exists.

[0101] The aligned signal matrix obtaining module 230 is configured to rearrange and phase align the periodic repetition signal according to the start and end times and the repetition period to obtain an aligned signal matrix.

[0102] The signal detection module 240 is configured to obtain a signal fingerprint of the unknown unmanned aerial vehicle signal according to an average feature of the aligned signal matrix, so as to realize detection of the unknown unmanned aerial vehicle signal.

[0103] Specific limitations of the unknown unmanned aerial vehicle signal detection device can be referred to the limitations of the unknown unmanned aerial vehicle signal detection method described above, which will not be repeated here. Each module in the unknown unmanned aerial vehicle signal detection device described above can be realized by software, hardware, and combinations thereof, in whole or in part. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0104] In one embodiment, a computer device is provided, which can be a terminal, and an internal structure diagram thereof can be as shown in the figure. Figure 7 The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement an unknown unmanned aerial vehicle signal detection method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0105] Those skilled in the art can understand that Figure 7 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0106] In one embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores a computer program. The processor executes the computer program to implement the following steps:

[0107] An unknown unmanned aerial vehicle signal is obtained, which is received by a radio frequency antenna;

[0108] An IQ data is obtained from the unknown unmanned aerial vehicle signal, and a slow-time delay correlation coefficient graph is generated according to the IQ data;

[0109] According to the characteristics of the periodic repetitive signal presented in the slow-time delay correlation coefficient graph, a judgment of the periodic repetitive signal is made. If the periodic repetitive signal exists, the start and end times and the repetition period of the periodic repetitive signal are estimated;

[0110] The periodic repetitive signal is rearranged and phase-aligned according to the start and end times and the repetition period, and an aligned signal matrix is obtained;

[0111] A signal fingerprint of the unknown unmanned aerial vehicle signal is obtained according to the average characteristics of the aligned signal matrix, so as to realize the detection of the unknown unmanned aerial vehicle signal.

[0112] In one embodiment, a computer readable storage medium is provided, having stored thereon a computer program, which when executed by a processor implements the following steps:

[0113] An unknown UAV signal is acquired, the unknown UAV signal being received by a radio frequency antenna;

[0114] A slow-time delay correlation coefficient map is generated according to IQ data derived from the unknown UAV signal;

[0115] A periodic repetitive signal is judged according to a feature presented by the periodic repetitive signal in the slow-time delay correlation coefficient map, and if the periodic repetitive signal exists, a start time and an end time of the periodic repetitive signal and a repetition period are estimated;

[0116] The periodic repetitive signal is rearranged and phase-aligned according to the start time and the end time and the repetition period, to obtain an aligned signal matrix;

[0117] A signal fingerprint of the unknown UAV signal is obtained according to an average feature of the aligned signal matrix, to realize detection of the unknown UAV signal.

[0118] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0119] Any combination of the technical features in the above embodiments can be made, and for the sake of brevity, not all possible combinations are described above, however, as long as the combination of the technical features does not exist in contradiction, it shall be considered within the scope of the present disclosure.

[0120] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it shall not be understood as a limitation on the patent scope of the present application. It shall be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these shall be within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for detecting unknown drone signals, the method comprising: receiving a signal; determining a signal type of the signal; and determining a signal strength of the signal. The method comprises: acquiring an unknown unmanned aerial vehicle signal, the unknown unmanned aerial vehicle signal being received by a radio frequency antenna; generating a slow-time delay correlation coefficient graph according to IQ data obtained from the unknown unmanned aerial vehicle signal; judging a periodic repetitive signal according to a feature presented by the periodic repetitive signal in the slow-time delay correlation coefficient graph, and if the periodic repetitive signal exists, estimating the start and end time and the repetition period of the periodic repetitive signal, wherein when the periodic signal is detected based on the slow-time delay correlation coefficient graph, a preset sliding window is used to slide along the delay direction for each slow-time step on the slow-time delay correlation coefficient graph, and the minimum value of the difference between the average amplitude of the current center window and the average amplitude of the left and right windows is calculated to obtain the regional maximum contrast degree, performing threshold segmentation according to a preset threshold and the maximum contrast degree of each region in the slow-time delay correlation coefficient graph to obtain a binary graph, and calculating the duty cycle along the slow-time dimension of the binary graph to obtain a candidate delay set corresponding to the vertical line on the slow-time delay correlation coefficient graph, and judging whether the periodic repetitive signal exists by using pulse repetition interval transformation on the candidate delay set, and if the periodic repetitive signal exists, obtaining the repetition period and the start and end time of the periodic repetitive signal, wherein a plurality of candidate periods are generated at equal intervals according to a preset maximum period, the candidate delay set is taken as a pulse train, the pulse repetition interval transformation probability of each candidate period is calculated, the candidate period corresponding to the maximum pulse repetition interval transformation probability is selected as the estimated repetition period, and the number of pulses meeting the estimated repetition period is counted, if the number of pulses counted exceeds a preset threshold, it is judged that the periodic repetitive signal exists in the unknown unmanned aerial vehicle signal; rearranging and phase aligning the periodic repetitive signal according to the start and end time and the repetition period to obtain an aligned signal matrix; obtaining a signal fingerprint of the unknown unmanned aerial vehicle signal according to the average feature of the aligned signal matrix, specifically, calculating the root mean square of each column data along the slow-time direction based on the aligned signal matrix, performing Otsu two-class adaptive threshold segmentation and morphological filtering on the root mean square of each column data to obtain the start and end index of the periodic repetitive part, and averaging along the slow-time direction on the aligned signal matrix according to the start and end index of the periodic repetitive part to obtain the signal fingerprint, so as to realize the detection of the unknown unmanned aerial vehicle signal.

2. The method of claim 1, wherein, The method comprises: dividing the IQ data into a plurality of data segments with overlapping parts according to a preset maximum period, an overlap rate and a segment length; performing short-time Fourier transform on each data segment to obtain a short-time Fourier autocorrelation spectrum representing the frequency energy distribution of each data segment; performing inverse Fourier transform on each data segment in the short-time Fourier autocorrelation spectrum and taking the modulus value to obtain a slow-time delay correlation coefficient matrix, and then obtaining the slow-time delay correlation coefficient graph.

3. The method of claim 1, wherein, The periodic repetition signal appears as a series of vertical lines in the slow-time delay correlation coefficient graph, wherein the vertical lines correspond to integer multiples of the signal period in the horizontal coordinate length, and the start / end positions of the vertical lines in the vertical coordinate are the start / end times of the signal.

4. The method of claim 3, wherein, The rearranging and phase aligning of the periodic repetition signal according to the start / end time and the repetition period comprises: According to the estimated repetition period and the start / end time, the corresponding IQ data in the start / end time is segmented and rearranged to obtain a plurality of rearranged signals; For the plurality of rearranged signals, the first rearranged signal is taken as a reference, the maximum correlation coefficient and the corresponding delay of the remaining rearranged signals with respect to the first rearranged signal are calculated based on FFT, and each rearranged signal is shifted and aligned according to the delay to obtain the aligned signal matrix.

5. An unknown drone signal detection apparatus, comprising: The device implements the unknown unmanned aerial vehicle signal detection method of any one of claims 1-4, comprising: a signal acquisition module configured to acquire an unknown unmanned aerial vehicle signal, wherein the unknown unmanned aerial vehicle signal is received by a radio frequency antenna; a slow-time delay correlation coefficient graph generation module configured to generate a slow-time delay correlation coefficient graph according to IQ data obtained from the unknown unmanned aerial vehicle signal; a periodic repetition signal judgment module configured to judge a periodic repetition signal according to the characteristics of the periodic repetition signal appearing in the slow-time delay correlation coefficient graph, and estimate the start / end time and the repetition period of the periodic repetition signal if the periodic repetition signal exists; an aligned signal matrix obtaining module configured to rearrange and phase align the periodic repetition signal according to the start / end time and the repetition period to obtain an aligned signal matrix; a signal detection module configured to obtain a signal fingerprint of the unknown unmanned aerial vehicle signal according to the average characteristics of the aligned signal matrix, so as to realize the detection of the unknown unmanned aerial vehicle signal. 6.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-5 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the method of any one of claims 1-4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method of any one of claims 1-4.

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