CN117237833B - Automatic threshold extraction-based rapid unmanned aerial vehicle graph signaling identification method and device - Google Patents
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Abstract
The invention discloses a rapid unmanned aerial vehicle graph signal recognition method and device based on automatic threshold extraction, wherein the method comprises the following steps: s1, collecting unmanned aerial vehicle image transmission signals in the surrounding environment; s2, preprocessing a signal transmitted by the unmanned aerial vehicle graph; s3, converting the unmanned aerial vehicle image transmission time domain signal into a time-frequency signal; step S4, storing the time-frequency signal for a period of time into a memory; s5, extracting a time domain signal of a frequency point from the memory every time, scanning and detecting the time domain signal according to the frequency point, wherein the detection process comprises amplitude moving average, generating an amplitude histogram, generating an automatic threshold, time slot judgment, feature extraction and feature library comparison; and S6, bandwidth calculation is carried out according to the comparison result. Compared with the prior art, the invention has the beneficial effects that: the method has low hardware requirements and is very suitable for application in unmanned aerial vehicle detection projects with requirements on portability and cost. The method and the device have short detection time and can reserve sufficient reaction time.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle detection, in particular to a rapid unmanned aerial vehicle graph signal recognition method based on automatic threshold extraction and a rapid unmanned aerial vehicle graph signal recognition device based on automatic threshold extraction.
Background
The main unmanned aerial vehicle detection means at present mainly comprise radar system detection, optical monitoring, sound wave identification and radio detection. Radio detection is a means for detecting unmanned aerial vehicles by receiving and recognizing radio signals sent by unmanned aerial vehicles, and is widely applied at present. The drone typically emits radio communication signals, navigation signals, or other characteristic signals that may be captured and identified by the detection device. At present, two main radio detection technical methods in the industry are mainly adopted, one is to input time-frequency diagram information into a large-scale neural network which is learned and trained in advance for identification after converting wireless signals into time-frequency diagrams, and the other is to extract special sequence signals in various unmanned aerial vehicle signals and scan at each frequency point for relevant detection so as to achieve the purpose of detection. The two methods have the following defects that firstly, the requirements on a hardware platform are very high, the carrying is inconvenient, the cost is very high, secondly, the detection time of the two radio detection methods is very long, the detection time is generally different in seconds according to different hardware performances from the input of a collection signal to the output of a detection result, and the efficiency of the detection device user for adopting a countercheck strategy to the unmanned aerial vehicle after the unmanned aerial vehicle is detected can be greatly influenced by the overlong detection time.
Therefore, development of an unmanned aerial vehicle signal recognition method with low requirements on a hardware platform and extremely short detection time is needed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a rapid unmanned aerial vehicle graph signal recognition method based on automatic threshold extraction, which has low requirements on a hardware platform and extremely short detection time, and the method comprises the following steps:
s1, collecting unmanned aerial vehicle image transmission signals in the surrounding environment;
s2, preprocessing a signal transmitted by the unmanned aerial vehicle graph;
s3, converting the unmanned aerial vehicle image transmission time domain signal into a time-frequency signal;
step S4, storing the time-frequency signal for a period of time into a memory;
s5, extracting a time domain signal of a frequency point from the memory every time, scanning and detecting the time domain signal according to the frequency point, wherein the detection process comprises amplitude moving average, generating an amplitude histogram, generating an automatic threshold, time slot judgment, feature extraction and feature library comparison;
and S6, bandwidth calculation is carried out according to the comparison result.
Preferably, the signal preprocessing in the step S2 includes digital filtering processing and digital down-conversion processing.
Preferably, in the step S3, the unmanned aerial vehicle image-transmitted time domain signal is converted into a time-frequency signal using a short-time fast fourier transform.
Preferably, the formula of the sliding average in the step S5 is as follows:
wherein I is an in-phase signal, Q is a quadrature signal, N is an accumulated point number, and P is a moving average output signal.
Preferably, in the step S5, the amplitude histogram is generated by returning the groups with uniform width by using an automatic grouping algorithm; the group can encompass a range of elements in the single-frequency-point time-domain signal and display a basic shape of the distribution.
Preferably, the method for generating the automatic threshold in step S5 is as follows: and detecting the maximum group of wave peaks to obtain amplitude value data with the maximum distribution quantity, and subtracting a set experience value data from the amplitude value data with the maximum distribution quantity to obtain the automatic threshold.
Preferably, the method for determining the time slot in the step S5 is: and carrying out time slot judgment according to the automatic threshold, wherein the time slot judgment is carried out according to the condition that the time slot judgment is greater than the threshold and is set to be 1 and less than or equal to the threshold and is set to be 0, so that the judged single-frequency-point unmanned aerial vehicle signal time slot is obtained.
Preferably, in the feature extraction process, unmanned aerial vehicle feature data is extracted according to the determined single-frequency point unmanned aerial vehicle signal time slots, wherein the feature data comprises time slot widths, time slot number of each time slot width and period among the time slots.
Preferably, the bandwidth calculating method in step S6 includes the following steps:
step S11, performing bit-wise logical exclusive OR operation on the single-frequency point unmanned aerial vehicle time slot signal after the judgment of the current time and the single-frequency point unmanned aerial vehicle time slot signal after the judgment of the previous frequency point, and adding the obtained result bit by bit to obtain a logical exclusive OR value;
step S12, if the logic exclusive OR value obtained in the step S11 is smaller than a set threshold value, judging that the frequency point and the last frequency point are the same unmanned aerial vehicle image transmission signal;
and step S13, circularly taking down a frequency point according to the steps S11 to S12 until the logic exclusive OR sum value does not meet the set threshold value or the required bandwidth exceeds the characteristic bandwidth of the unmanned aerial vehicle stored in the characteristic database, and finally obtaining the bandwidth information of the unmanned aerial vehicle image transmission signal.
The invention also provides a rapid unmanned aerial vehicle image signal recognition device based on the automatic threshold extraction, which is used for executing the rapid unmanned aerial vehicle image signal recognition method based on the automatic threshold extraction.
Compared with the prior art, the invention has the following beneficial effects:
(1) The rapid unmanned aerial vehicle graph signal recognition method based on automatic threshold extraction provided by the invention does not relate to complex and huge mathematical algorithm operation, the implementation of the method mainly relates to a plurality of conventional digital signal processing methods and digital logic operation, the hardware requirement for algorithm implementation is very low, the method can be realized only by a general programmable digital logic chip, and the method is very suitable for application in unmanned aerial vehicle detection projects with requirements on portability and cost.
(2) The detection time is very short, and only more than 100 milliseconds are needed from the acquisition of unmanned aerial vehicle image signal transmission to the output of the detection result according to different chip performances. The short detection time can provide sufficient reaction time for the unmanned aerial vehicle reaction equipment.
Drawings
The invention is further described below with reference to the accompanying drawings:
fig. 1 is a flowchart of a rapid unmanned aerial vehicle graph signaling identification method based on automatic threshold extraction in a first embodiment of the invention;
fig. 2 is a time-frequency diagram of a signal of a drone according to an embodiment of the present invention;
fig. 3 is a single-frequency-point unmanned aerial vehicle signal time slot diagram after smoothing filtering in the first embodiment of the invention;
fig. 4 is a histogram of single frequency point amplitude statistics of an unmanned aerial vehicle signal in accordance with an embodiment of the present invention;
fig. 5 is a signal time slot diagram of a single-frequency point unmanned aerial vehicle after judgment in the first embodiment of the invention;
fig. 6 is a flowchart of a bandwidth calculating method in step S6 according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a rapid unmanned aerial vehicle graph signaling identification device based on automatic threshold extraction in the first embodiment of the invention.
Reference numerals:
the device comprises a control module 1, a signal acquisition module 2, a signal preprocessing module 3, a signal conversion module 4, a frequency point scanning detection module 5, a bandwidth calculation module 6 and a memory 7.
Detailed Description
The above and further technical features and advantages of the present invention are described in more detail below with reference to the accompanying drawings.
In the description of the present invention, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" is at least two unless explicitly defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; may be mechanically connected, may be electrically connected or may be in communication with each other; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention.
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
In order to illustrate the technical scheme of the invention, the following description is made by specific examples.
Example 1
As shown in fig. 1, the method for identifying the rapid unmanned aerial vehicle graph signaling based on automatic threshold extraction provided by the invention comprises the following steps:
s1, collecting unmanned aerial vehicle image transmission signals in the surrounding environment;
s2, preprocessing a signal transmitted by the unmanned aerial vehicle graph;
s3, converting the unmanned aerial vehicle image transmission time domain signal into a time-frequency signal;
step S4, storing the time-frequency signal for a period of time into a memory;
s5, extracting a time domain signal of a frequency point from the memory every time, scanning and detecting the time domain signal according to the frequency point, wherein the detection process comprises amplitude moving average, generating an amplitude histogram, generating an automatic threshold, time slot judgment, feature extraction and feature library comparison;
and S6, bandwidth calculation is carried out according to the comparison result.
In this embodiment, specifically, the high-speed ADC is used to collect the signal in step S1, and the sampling rate is set to 245.76MHz.
In the present embodiment, the signal preprocessing in step S2 includes digital filtering processing and digital down-conversion processing. Further preferably, the digital down-conversion process employs a polyphase digital filtering method, and the digital signal is decimated by a factor of 2 by the polyphase digital filtering method.
In step S3 of the present embodiment, the unmanned aerial vehicle image-transmitted time domain signal is converted into a time-frequency signal using a short-time fast fourier transform (SFFT). Further preferably, 256-point SFFT is used. The reason for using a 256-point SFFT is that: the phase information of this data is needed in the direction finding function of the device and is therefore not processed here. Fig. 2 shows a time-frequency chart of a 100 ms-based signal transmission of a man unmanned aerial vehicle acquired in the first embodiment of the invention.
In step S4 of the present embodiment, specifically, the time-frequency signal with phase information generated in step S3 is stored in a ddr4 ram with a storage amount of 1 gbyte, and a specific storage time length is 100ms.
In step S5, the moving average is to accumulate and average the single frequency point time domain signal amplitude of N points, and remove the last old data every time a new data is accumulated, which makes the time domain amplitude signal curve smooth.
In step S5 of the present embodiment, the formula of the moving average is as follows:
where I is an in-phase signal, Q is a quadrature signal, N is the number of accumulated points, preferably, N is set to 10 and p is a moving average output signal in this embodiment.
The method for generating the amplitude histogram is as follows: by using an automatic grouping algorithm, groups of uniform width are returned that can encompass the range of elements in the single frequency point time domain signal and display the basic shape of the distribution. The algorithm displays the groups as rectangles such that the height of each rectangle represents the number of elements in the group.
In step S5 of the present embodiment, the process of generating the amplitude histogram is: the output signal is converted into a decibel value by the following formula:
the Pbd signal is then counted in number at 1dB intervals to generate an amplitude histogram, as shown in fig. 4.
The method for generating the automatic threshold is that the maximum amplitude value data with the largest distribution number is obtained through maximum group peak detection, and the automatic threshold is obtained by subtracting a set experience value data from the amplitude value data with the largest distribution number. In this embodiment, the empirical data is preferably set to 2dB.
The method for judging the time slot comprises the following steps: and carrying out time slot judgment according to the automatic threshold, wherein the time slot judgment is carried out according to the condition that the time slot judgment is greater than the threshold and is set to be 1 and less than or equal to the threshold and is set to be 0, so that the judged single-frequency-point unmanned aerial vehicle signal time slot is obtained. The transition from 0 to 1 is the rising edge of the signal, and the transition from 1 to 0 is the falling edge of the signal. The single frequency point unmanned aerial vehicle signal time slot diagram after the judgement is as shown in figure 5.
The characteristic extraction process comprises the following steps: and extracting unmanned aerial vehicle characteristic data according to the judged single-frequency-point unmanned aerial vehicle signal time slots, wherein the characteristic data comprises time slot widths, the time slot number of each time slot width and the period between the time slots. In this embodiment, preferably, the characteristic parameters of the slot width in the 100ms signal, the slot number of each slot width, the period between slots, and the like are counted according to the jump edges of the signal slots.
The feature library comparison method comprises the following steps: and comparing the extracted characteristic parameters with the unmanned aerial vehicle characteristic library, and outputting a comparison result. Specifically, if a certain model can meet the characteristic conditions, the unmanned aerial vehicle is detected, and unmanned aerial vehicle model number information is output.
If a single-frequency point unmanned aerial vehicle image signal is detected in step S5, the bandwidth of the image signal needs to be detected. The number of signal time slot digits of the single-frequency point unmanned aerial vehicle after judgment in the embodiment is preferably 4800 digits. As shown in fig. 6, the bandwidth calculation method in step S6 is as follows:
step S11, performing bit-wise logical exclusive OR operation on the single-frequency point unmanned aerial vehicle time slot signal after the judgment of the current time and the single-frequency point unmanned aerial vehicle time slot signal after the judgment of the previous frequency point, and adding the obtained result bit by bit to obtain a logical exclusive OR value;
step S12, if the logic exclusive OR value obtained in the step S11 is smaller than a set threshold value, judging that the frequency point and the last frequency point are the same unmanned aerial vehicle image transmission signal;
and step S13, circularly taking down a frequency point according to the steps S11 to S12 until the logic exclusive OR sum value does not meet the set threshold value or the required bandwidth exceeds the characteristic bandwidth of the unmanned aerial vehicle stored in the characteristic database, and finally obtaining the bandwidth information of the unmanned aerial vehicle image transmission signal.
In this embodiment, the sampling rate in step S1 is preferably set to 245.76MHz. The corresponding bandwidth of the frequency point in this embodiment is (122.88/256) MHz, i.e., 0.48MHz. If the logical exclusive or sum value of the single-frequency-point unmanned aerial vehicle signal time slot signals after the judgment of the M frequency points meets the threshold condition, the bandwidth of the unmanned aerial vehicle image transmission signal is M0.48 MHz. Finally, the unmanned aerial vehicle image signal type, image signal frequency point and bandwidth information are obtained.
In this embodiment, the threshold value in step S12 is preferably set to 100.
As shown in fig. 7, the invention further provides a rapid unmanned aerial vehicle graph signal recognition device based on automatic threshold extraction, which is used for executing the rapid unmanned aerial vehicle graph signal recognition method based on automatic threshold extraction. The rapid unmanned aerial vehicle graph signal recognition device based on automatic threshold extraction comprises a control module 1, a signal acquisition module 2, a signal preprocessing module 3, a signal conversion module 4, a frequency point scanning detection module 5, a bandwidth calculation module 6 and a memory 7, wherein the signal acquisition module 2, the signal preprocessing module 3, the signal conversion module 4, the frequency point scanning detection module 6 and the memory 7 are respectively connected with the control module 1. The control module 1 is used for controlling each module and realizing the transmission of data or signals among each module. The signal acquisition module 2 is used for executing the step S1, and the signal preprocessing module 3 is used for executing the step S2. The signal conversion module 4 is configured to perform step S3. The frequency point scanning detection module 5 is used for executing step S5, and the bandwidth calculation module 6 is used for executing step S6.
Compared with the prior art, the rapid unmanned aerial vehicle graph signaling identification method based on automatic threshold extraction has the beneficial effects that:
(1) The method does not involve complex and huge mathematical algorithm operation, the implementation of the method mainly involves some conventional digital signal processing methods and digital logic operation, the hardware requirement for algorithm implementation is very low, the method can be implemented only by a general programmable digital logic chip, and the method is very suitable for application in unmanned aerial vehicle detection projects with requirements on portability and cost.
(2) The detection time is very short, and only more than 100 milliseconds are needed from the acquisition of unmanned aerial vehicle image signal transmission to the output of the detection result according to different chip performances. The short detection time can provide sufficient reaction time for the unmanned aerial vehicle reaction equipment.
Example two
The implementation method of the digital down-conversion process can also be a low-pass filtering method and an interpolation method.
The above is only a specific embodiment of the present invention, but the technical features of the present invention are not limited thereto. Any simple changes, equivalent substitutions or modifications made on the basis of the present invention to solve the substantially same technical problems and achieve the substantially same technical effects are encompassed within the scope of the present invention.
Claims (6)
1. The rapid unmanned aerial vehicle graph signaling identification method based on automatic threshold extraction is characterized by comprising the following steps of:
s1, collecting unmanned aerial vehicle image transmission signals in the surrounding environment;
s2, preprocessing a signal transmitted by the unmanned aerial vehicle graph;
s3, converting the unmanned aerial vehicle image transmission time domain signal into a time-frequency signal;
step S4, storing the time-frequency signal for a period of time into a memory;
s5, extracting a time domain signal of a frequency point from the memory every time, scanning and detecting the time domain signal according to the frequency point, wherein the detection process comprises amplitude moving average, generating an amplitude histogram, generating an automatic threshold, time slot judgment, feature extraction and feature library comparison;
s6, bandwidth calculation is carried out according to the comparison result;
in the step S5, an automatic grouping algorithm is used to return the groups with uniform width to generate an amplitude histogram; the group can cover the element range in the single-frequency point time domain signal and display the basic shape of distribution;
the method for generating the automatic threshold in the step S5 is as follows: obtaining amplitude value data with the largest distribution quantity by maximum group peak detection, and subtracting a set experience value data from the amplitude value data with the largest distribution quantity to obtain an automatic threshold;
the method for judging the time slot in the step S5 is as follows: performing time slot judgment according to an automatic threshold, wherein the time slot judgment is performed according to the condition that the time slot judgment is greater than the threshold and is set to be 1 and less than or equal to the threshold and is set to be 0, so that a judged single-frequency-point unmanned aerial vehicle signal time slot is obtained;
and in the characteristic extraction process, extracting unmanned aerial vehicle characteristic data according to the judged single-frequency-point unmanned aerial vehicle signal time slots, wherein the characteristic data comprises time slot widths, the time slot number of each time slot width and the period between the time slots.
2. The method for identifying the rapid unmanned aerial vehicle graph signal based on automatic threshold extraction according to claim 1, wherein the signal preprocessing in the step S2 comprises a digital filtering process and a digital down-conversion process.
3. The method for identifying the unmanned aerial vehicle map signal based on automatic threshold extraction according to claim 1, wherein in the step S3, the unmanned aerial vehicle map time domain signal is converted into a time-frequency signal using a short-time fast fourier transform.
4. The method for identifying the rapid unmanned aerial vehicle map signaling based on the automatic threshold extraction according to claim 1, wherein the formula of the moving average in the step S5 is as follows:
wherein I is an in-phase signal, Q is a quadrature signal, N is an accumulated point number, and P is a moving average output signal.
5. The method for identifying the rapid unmanned aerial vehicle graph signaling based on the automatic threshold extraction according to claim 1, wherein the method for calculating the bandwidth in the step S6 comprises the following steps:
step S11, performing bit-wise logical exclusive OR operation on the single-frequency point unmanned aerial vehicle time slot signal after the judgment of the current time and the single-frequency point unmanned aerial vehicle time slot signal after the judgment of the previous frequency point, and adding the obtained result bit by bit to obtain a logical exclusive OR value;
step S12, if the logic exclusive OR value obtained in the step S11 is smaller than a set threshold value, judging that the frequency point and the last frequency point are the same unmanned aerial vehicle image transmission signal;
and step S13, circularly taking down a frequency point according to the steps S11 to S12 until the logic exclusive OR sum value does not meet the set threshold value or the required bandwidth exceeds the characteristic bandwidth of the unmanned aerial vehicle stored in the characteristic database, and finally obtaining the bandwidth information of the unmanned aerial vehicle image transmission signal.
6. The rapid unmanned aerial vehicle image signal recognition device based on automatic threshold extraction is characterized by being used for executing the rapid unmanned aerial vehicle image signal recognition method based on automatic threshold extraction according to any one of claims 1-5, and comprises a control module, and a signal acquisition module, a signal preprocessing module, a signal conversion module, a memory, a frequency point scanning detection module and a bandwidth calculation module which are respectively connected with the control module.
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