CN112327331A - GNSS deception jamming detection method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a GNSS deception jamming detection method, a GNSS deception jamming detection device, GNSS deception jamming detection equipment and a GNSS deception jamming detection storage medium, wherein the method comprises the following steps: acquiring a two-dimensional search matrix to be analyzed of Doppler frequency shift-pseudo code phase when a GNSS receiver captures a satellite signal to be analyzed; counting a first number of first correlation peaks which are larger than a preset threshold in a two-dimensional search matrix to be analyzed; when the first quantity is 1, intercepting data in a preset range to form an intermediate matrix to be analyzed by taking the maximum first correlation peak as a center in the two-dimensional search matrix to be analyzed, and setting data lower than a preset threshold in the intermediate matrix to be analyzed to be 0 to obtain a target matrix; normalizing the target matrix to obtain a target detection matrix; the target detection matrix is input to the GAN network model to obtain a detection result output by the GAN network model, and the technical problem that the existing detection method for the deception interference is difficult to detect the deception signal with the time delay within the range of 0-2 chips is solved.
Description
Technical Field
The present application relates to the technical field of spoofing interference detection, and in particular, to a GNSS spoofing interference detection method, apparatus, device, and storage medium.
Background
With the wide and deep application of satellite navigation systems, it plays an increasingly important role in people's work and daily life. Meanwhile, people are also paying more attention to the safety and reliability of satellite navigation applications. However, since the satellite signal is transmitted over a long distance, the signal strength is very weak when the satellite signal reaches the ground, and the satellite signal is easily affected by interference signals in the same frequency band, and civil signals are generally used in the open world, which makes the receiver very vulnerable to interference.
Among all interference classes, GNSS spoofing interference is a type of interference that is more harmful. The GNSS deception jamming means that a deception signal similar to a real satellite signal is emitted, so that a GNSS receiver outputs a position and time result designed by a deception party, and the GNSS receiver is controlled. If the system uses this wrong information, serious consequences will result.
In view of the serious hazard of GNSS spoofing interference, the related art has proposed many detection methods for this purpose, including distortion detection and analysis detection based on statistics. However, as the equipment carrying out GNSS spoofing interference aims at concealing spoofing, the time delay of the transmitted spoofing signal is very small. Generally, when the phase difference between the pseudo code of the spoofed signal and the pseudo code of the real satellite signal is large, such as an offset of 2 or more chips, it is easy to identify. However, for a spoofed signal with a delay in the range of 0-2 chips, the existing detection method is difficult to detect.
Disclosure of Invention
The application provides a GNSS deception jamming detection method, a GNSS deception jamming detection device and a GNSS deception jamming detection storage medium, and solves the technical problem that deception signals with time delay within the range of 0-2 chips are difficult to detect in the existing deception jamming detection method.
In view of the above, a first aspect of the present application provides a GNSS spoofing interference detection method, including:
acquiring a two-dimensional search matrix to be analyzed of Doppler frequency shift-pseudo code phase when a GNSS receiver captures a satellite signal to be analyzed;
counting a first number of first correlation peaks which are larger than a preset threshold in the two-dimensional search matrix to be analyzed;
when the first number is 1, in the two-dimensional search matrix to be analyzed, taking the maximum first correlation peak as a center, and intercepting data in a preset range to form an intermediate matrix to be analyzed, wherein the preset range is as follows: a region of pseudo code phase ± 2 chips of the largest first correlation peak;
setting the data lower than the preset threshold in the intermediate matrix to be analyzed to be 0 to obtain a target matrix;
normalizing the target matrix to obtain a target detection matrix;
and inputting the target detection matrix into a GAN network model to obtain a detection result output by the GAN network model, wherein the GAN network model is a mapping relation model of the target detection matrix and the detection result.
Optionally, the preset range further includes: the maximum first correlation peak is in the region of 1kHz doppler shift.
Optionally, the configuration process of the GAN network model includes:
acquiring a training matrix and a GAN network for training;
training the GAN network by taking the training matrix as an input parameter, taking a theoretical detection result of the training matrix corresponding to the deceptive interference as a target output result and taking an actual detection result of the training matrix corresponding to the deceptive interference as an actual output result to obtain an intermediate network model;
and taking a discriminator model in the intermediate network model as the GAN network model.
Optionally, the obtaining a training matrix for training specifically includes:
acquiring a Doppler frequency shift-pseudo code phase training two-dimensional search matrix when the GNSS receiver captures a training satellite signal;
counting a second number of second correlation peaks larger than the preset threshold in the training two-dimensional search matrix;
when the second quantity is 1, intercepting data in the preset range to form a training intermediate matrix by taking the largest second correlation peak as the center in the training two-dimensional search matrix;
and setting 0 to the data lower than the preset threshold in the training intermediate matrix, and normalizing the training intermediate matrix after the 0 is set to obtain the training matrix.
Optionally, the method further comprises:
and when the first quantity is 0, judging that the GNSS receiver does not receive satellite signals, and stopping the current detection process.
Optionally, the method further comprises:
and when the first number is a numerical value more than 2, judging that a deception signal exists in the satellite signal to be analyzed, and stopping the current detection process.
A second aspect of the present application provides a GNSS spoofing interference detecting apparatus, including:
the acquisition unit is used for acquiring a two-dimensional search matrix to be analyzed of Doppler frequency shift-pseudo code phase when the GNSS receiver captures a satellite signal to be analyzed;
the statistical unit is used for counting the first number of the first correlation peaks which are larger than a preset threshold in the two-dimensional search matrix to be analyzed;
an intercepting unit, configured to intercept, when the first number is 1, data in a preset range from the two-dimensional search matrix to be analyzed by taking a maximum first correlation peak as a center to form an intermediate matrix to be analyzed, where the preset range is: a region of pseudo code phase ± 2 chips of the largest first correlation peak;
the preprocessing unit is used for setting the data lower than the preset threshold in the intermediate matrix to be analyzed to be 0 to obtain a target matrix;
the normalization unit is used for normalizing the target matrix to obtain a target detection matrix;
and the detection unit is used for inputting the target detection matrix into a GAN network model to obtain a detection result output by the GAN network model, wherein the GAN network model is a mapping relation model of the target detection matrix and the detection result.
Optionally, the preset range further includes: the first peak has a doppler shift of ± 1 kHz.
The third invention of the present application provides a GNSS spoofing interference detecting device, which includes a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the GNSS spoofing interference detection method of the first aspect in accordance with instructions in the program code.
A fourth aspect of the present application provides a storage medium for storing program code for performing the GNSS spoofing interference detection method according to the first aspect.
According to the technical scheme, the method has the following advantages:
the application provides a GNSS deception jamming detection method, which comprises the following steps: acquiring a two-dimensional search matrix to be analyzed of Doppler frequency shift-pseudo code phase when a GNSS receiver captures a satellite signal to be analyzed; counting a first number of first correlation peaks which are larger than a preset threshold in a two-dimensional search matrix to be analyzed; when the first number is 1, in the two-dimensional search matrix to be analyzed, taking the maximum first correlation peak as the center, and intercepting data in a preset range to form an intermediate matrix to be analyzed, wherein the preset range is as follows: a region of pseudo code phase ± 2 chips of the largest first correlation peak; setting data lower than a preset threshold in the intermediate matrix to be analyzed to be 0 to obtain a target matrix; normalizing the target matrix to obtain a target detection matrix; and inputting the target detection matrix into the GAN network model to obtain a detection result output by the GAN network model, wherein the GAN network model is a mapping relation model of the target detection matrix and the detection result.
In the application, a two-dimensional search matrix to be analyzed when a GNSS receiver captures satellite signals to be analyzed is obtained, then a first number of first correlation peaks which are larger than a preset threshold in the two-dimensional search matrix to be analyzed is counted, when the first number is 1, whether deceptive signals exist or not is difficult to judge, so in the two-dimensional search matrix to be analyzed, the maximum first correlation peak is taken as a center, data in a pseudo code phase +/-2 chip area of the first peak value is intercepted to form an intermediate matrix to be analyzed, then data which are lower than the preset threshold in the intermediate matrix to be analyzed are set to be 0 to obtain a target matrix, then the target matrix is normalized to obtain a target detection matrix, finally the target detection matrix is input to a GAN network model to obtain a detection result output by the GAN network model, wherein the GAN network model is a mapping relation model of the target matrix and the detection result, the target matrix for detection is obtained based on data in a pseudo code phase +/-2 chips of the first peak value, effective detection of deception signals with time delay within the range of 0-2 chips is achieved, detection is carried out through a GAN network model, detection accuracy is further improved, and therefore the technical problem that the existing detection method for deception interference is difficult to detect the deception signals with the time delay within the range of 0-2 chips is solved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating a GNSS spoofing interference detection method according to a first embodiment of the present disclosure;
FIG. 2 is a representation of a two-dimensional search matrix in an embodiment of the present application;
FIG. 3 is a flowchart illustrating a GNSS spoofing interference detection method according to a second embodiment of the present disclosure;
FIG. 4 is a representation form diagram of a two-dimensional search matrix when the first number is 1 in the embodiment of the present application;
FIG. 5 is the intermediate matrix to be analyzed corresponding to FIG. 4;
FIG. 6 is a corresponding target matrix of FIG. 5;
fig. 7 is a GAN detection probability of spoofed signals at different delay chips in the application example of the present application;
fig. 8 is a CNN detection probability of a spoofed signal under different delay chips in an application example of the present application;
fig. 9 is a schematic structural diagram of an embodiment of a GNSS spoofing interference detecting apparatus according to the embodiment of the present application.
Detailed Description
The embodiment of the application provides a GNSS deception jamming detection method, a GNSS deception jamming detection device, GNSS deception jamming detection equipment and a GNSS deception jamming storage medium, and solves the technical problem that deception signals with time delay within the range of 0-2 chips are difficult to detect in the existing deception jamming detection method.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, a flowchart of a GNSS spoofing interference detection method according to a first embodiment of the present application is shown.
The GNSS deception jamming detection method in the embodiment comprises the following steps:
The satellite signal is always present and the spoofed signal may be present, i.e. there are two cases of the system. One is the presence of only satellite signals in the received signal, and the other is the presence of both spoofed and satellite signals in the received signal.
The GNSS receiver captures the intermediate frequency signal, and the methods include a time domain correlator-based method, a matched filter-based method, an FFT-based method and the like, and a two-dimensional search matrix is generated for searching a correlation peak and roughly estimating the Doppler frequency shift and the code phase of the satellite navigation signal. In the signal acquisition stage, the receiver searches satellite signals in sequence to generate a two-dimensional search matrix with Doppler frequency shift and pseudo code phase as axes. As shown in fig. 2, when the signal is a GPS signal, the C/a code range is [1, 1023], and the doppler shift search range is [ -5kHz,5kHz ].
The value of the preset threshold may be set by those skilled in the art as needed, and is not limited and described in detail herein.
103, when the first number is 1, in the two-dimensional search matrix to be analyzed, taking the maximum first correlation peak as the center, intercepting data in a preset range to form an intermediate matrix to be analyzed, wherein the preset range is as follows: the pseudo code phase of the largest first correlation peak is in the region of 2 chips.
And step 104, setting the data lower than the preset threshold in the intermediate matrix to be analyzed to be 0 to obtain a target matrix.
And 105, normalizing the target matrix to obtain a target detection matrix.
And 106, inputting the target detection matrix into the GAN network model to obtain a detection result output by the GAN network model, wherein the GAN network model is a mapping relation model of the target detection matrix and the detection result.
In the embodiment, a two-dimensional search matrix to be analyzed when a GNSS receiver captures a satellite signal to be analyzed is obtained, then a first number of first correlation peaks larger than a preset threshold in the two-dimensional search matrix to be analyzed is counted, and when the first number is 1, it is indicated that a spoofed signal is difficult to judge, so in the two-dimensional search matrix to be analyzed, the largest first correlation peak is taken as a center, data in a pseudo code phase ± 2 chip region of the first peak value is intercepted to form an intermediate matrix to be analyzed, then data lower than the preset threshold in the intermediate matrix to be analyzed is set to 0 to obtain a target matrix, then the target matrix is normalized to obtain a target detection matrix, and finally the target detection matrix is input to a GAN network model to obtain a detection result output by the GAN network model, wherein the GAN network model is a mapping relation model of the target matrix and the detection result, the target matrix for detection is obtained based on data in a pseudo code phase +/-2 chips of the first peak value, effective detection of deception signals with time delay within the range of 0-2 chips is achieved, detection is carried out through a GAN network model, detection accuracy is further improved, and therefore the technical problem that the existing detection method for deception interference is difficult to detect the deception signals with the time delay within the range of 0-2 chips is solved.
The above is a first embodiment of a GNSS spoofing interference detection method provided in the embodiments of the present application, and the following is a second embodiment of a GNSS spoofing interference detection method provided in the embodiments of the present application.
Referring to fig. 3, a flowchart of a GNSS spoofing interference detection method according to a second embodiment of the present application is shown.
The GNSS deception jamming detection method in the embodiment comprises the following steps:
It should be noted that the description of step 301 is the same as that of step 101 in the embodiment, and is not repeated here.
It should be noted that the description of step 302 is the same as that of step 102 in the embodiment, and is not repeated here.
It can be understood that, as shown in fig. 4, when the first number corresponding to the correlation peak is 1, it indicates that whether the spoofed signal exists is difficult to be judged by the conventional method, so that the subsequent steps are performed.
It should be noted that the preset range further includes: the maximum first correlation peak is in the region of 1kHz doppler shift. After the first peak value of the correlation peak in fig. 4 is processed in step 303, the result shown in fig. 5 is obtained, and the intermediate matrix to be analyzed in this embodiment is a position selected as a rectangular box, where the black square in the middle of the rectangle is the first peak value of the correlation peak.
And step 304, setting the data lower than the preset threshold in the intermediate matrix to be analyzed to be 0 to obtain a target matrix.
Setting the data lower than the preset threshold in the to-be-analyzed matrix to 0 is equivalent to a normalization action, so that the calculation is simpler, and the result obtained after the processing of step 304 is performed on the to-be-analyzed intermediate matrix in fig. 5 is shown in fig. 6.
And 305, normalizing the target matrix to obtain a target detection matrix.
And step 306, inputting the target detection matrix into the GAN network model to obtain a detection result output by the GAN network model, wherein the GAN network model is a mapping relation model of the target detection matrix and the detection result.
In this embodiment, the configuration process of the GAN network model includes:
acquiring a training matrix and a GAN network for training;
training the GAN network by taking the training matrix as an input parameter, taking a theoretical detection result of the training matrix corresponding to the deceptive interference as a target output result and taking an actual detection result of the training matrix corresponding to the deceptive interference as an actual output result to obtain an intermediate network model;
and taking the discriminator model in the intermediate network model as the GAN network model.
Wherein, acquiring a training matrix for training specifically comprises:
acquiring a Doppler frequency shift-pseudo code phase training two-dimensional search matrix when a GNSS receiver captures a training satellite signal;
counting a second number of second correlation peaks which are larger than a preset threshold in the training two-dimensional search matrix;
when the second quantity is 1, intercepting data in a preset range to form a training intermediate matrix by taking the maximum second correlation peak as the center in the training two-dimensional search matrix;
and setting 0 for the data lower than the preset threshold in the training intermediate matrix, and normalizing the training intermediate matrix after the 0 setting to obtain the training matrix.
And 307, when the first quantity is 0, judging that the GNSS receiver does not receive the satellite signal, and stopping the current detection process.
And 308, judging that deception signals exist in the satellite signals to be analyzed when the first quantity is a numerical value more than 2, and stopping the current detection process.
In the embodiment, a two-dimensional search matrix to be analyzed when a GNSS receiver captures a satellite signal to be analyzed is obtained, then a first number of first correlation peaks larger than a preset threshold in the two-dimensional search matrix to be analyzed is counted, and when the first number is 1, it is indicated that a spoofed signal is difficult to judge, so in the two-dimensional search matrix to be analyzed, the largest first correlation peak is taken as a center, data in a pseudo code phase ± 2 chip region of the first peak value is intercepted to form an intermediate matrix to be analyzed, then data lower than the preset threshold in the intermediate matrix to be analyzed is set to 0 to obtain a target matrix, then the target matrix is normalized to obtain a target detection matrix, and finally the target detection matrix is input to a GAN network model to obtain a detection result output by the GAN network model, wherein the GAN network model is a mapping relation model of the target matrix and the detection result, the target matrix for detection is obtained based on data in a pseudo code phase +/-2 chips of the first peak value, effective detection of deception signals with time delay within the range of 0-2 chips is achieved, detection is carried out through a GAN network model, detection accuracy is further improved, and therefore the technical problem that the existing detection method for deception interference is difficult to detect the deception signals with the time delay within the range of 0-2 chips is solved.
The above is an embodiment two of the GNSS spoofing interference detection method provided in the embodiment of the present application, and the following is an application example of the GNSS spoofing interference detection method provided in the embodiment of the present application.
In order to verify the performance of the GNSS deception interference detection method based on the GAN, the following simulation experiment is carried out for verification:
simulating an intermediate frequency signal of the GNSS receiver, wherein the sampling frequency of the intermediate frequency signal is set to be 16.368 MHz; the theoretical intermediate frequency is set to 4.092MHz, random satellite signal. The signal-to-noise ratio of the simulated satellite signal is set to be between-15 and-10 dB. Since it is difficult to maintain accurate synchronization of the spoofed signal with the real satellite signal, the simulated spoofed signal differs from the real satellite signal primarily in doppler shift, pseudo code phase, and power. In simulation, the Doppler frequency shift difference is randomly changed within a range of +/-1 kHz, the pseudo code phase difference between a deception signal and a real satellite signal is changed within a range of +/-2 chips, and the power of the deception signal is 1.1-3.0 dB higher than that of the real signal. Simulation data in experiments are divided into two main categories:
H0: only true satellite signals are present;
H1: both true satellite signals and spoofed signals are present.
Wherein H0The real satellite signals in the scene total 150000 groups. H1And distinguishing the data of the scene according to the code phase difference value of the deception signal and the real satellite signal, wherein the value range is (0,2) chips, the step length is 0.1 chip, 20 types are counted, and 5000 groups of data of each type are obtained. It should be noted that, the CNN algorithm is used to test the data set simultaneously in the experiment to compare with the GAN test effect. The following are the settings for the GAN and CNN training sets and test sets:
and (3) GAN: h is to be0100000 groups of data are used for training the GAN network; h is to be0The remaining 100000 sets of data and H 15000 groups of data in each type of data were used for the test.
CNN: h is to be1Corresponding to 5000 groups of data and 100000 groups of H in various types of data0Merging the data to be used as training data, and totaling 200000 groups; remaining 50000 groups H0Data sum H12500 groups of data in each type of data are taken as test data, and the total number is 100000 groups.
In the simulation experiment, the Doppler frequency shift search step length is set to be in two modes: 500Hz and 250 Hz. The pseudo code phase search step size is set to 0.5 chips and 0.25 chips, respectively. The simulation experiment result is obtained by independently and repeatedly operating for 100 times by adopting a Monte Carlo method and then averaging.
As shown in fig. 7, the doppler shift and the code phase spoof the GAN detection probability of the signal under different delay chips at different search step lengths. As shown in fig. 8, the doppler shift and the code phase are at different search step lengths respectively, so as to spoof the CNN detection probability of the signal under different delay chips. As can be seen from a comparison between fig. 7 and fig. 8, when the spoofed signal delay is greater than 0.5 chip, the detection probability of the GAN algorithm is significantly better than that of the CNN algorithm.
The above is an application example of the GNSS spoofing interference detection method provided in the embodiment of the present application, and the following is an embodiment of the GNSS spoofing interference detection apparatus provided in the embodiment of the present application.
Referring to fig. 9, a schematic structural diagram of an embodiment of a GNSS spoofing interference detecting apparatus in an embodiment of the present application is shown.
The GNSS spoofing interference detection apparatus of the present embodiment includes:
an obtaining unit 901, configured to obtain a two-dimensional search matrix to be analyzed of a doppler frequency shift-pseudo code phase when a GNSS receiver captures a satellite signal to be analyzed;
a counting unit 902, configured to count a first number of first correlation peaks greater than a preset threshold in a two-dimensional search matrix to be analyzed;
a truncating unit 903, configured to truncate, when the first number is 1, data in a preset range to form an intermediate matrix to be analyzed with a maximum first correlation peak as a center in the two-dimensional search matrix to be analyzed, where the preset range is: a region of pseudo code phase ± 2 chips of the largest first correlation peak;
a preprocessing unit 904, configured to set 0 to data in the intermediate matrix to be analyzed, where the data is lower than a preset threshold, to obtain a target matrix;
a normalization unit 905, configured to normalize the target matrix to obtain a target detection matrix;
the detecting unit 906 is configured to input the target detection matrix into the GAN network model to obtain a detection result output by the GAN network model, where the GAN network model is a mapping relationship model between the target detection matrix and the detection result.
Optionally, the preset range further includes: the maximum first correlation peak is in the region of 1kHz doppler shift.
In the embodiment, a two-dimensional search matrix to be analyzed when a GNSS receiver captures a satellite signal to be analyzed is obtained, then a first number of first correlation peaks larger than a preset threshold in the two-dimensional search matrix to be analyzed is counted, and when the first number is 1, it is indicated that a spoofed signal is difficult to judge, so in the two-dimensional search matrix to be analyzed, the largest first correlation peak is taken as a center, data in a pseudo code phase ± 2 chip region of the first peak value is intercepted to form an intermediate matrix to be analyzed, then data lower than the preset threshold in the intermediate matrix to be analyzed is set to 0 to obtain a target matrix, then the target matrix is normalized to obtain a target detection matrix, and finally the target detection matrix is input to a GAN network model to obtain a detection result output by the GAN network model, wherein the GAN network model is a mapping relation model of the target matrix and the detection result, the target matrix for detection is obtained based on data in a pseudo code phase +/-2 chips of the first peak value, effective detection of deception signals with time delay within the range of 0-2 chips is achieved, detection is carried out through a GAN network model, detection accuracy is further improved, and therefore the technical problem that the existing detection method for deception interference is difficult to detect the deception signals with the time delay within the range of 0-2 chips is solved.
The embodiment of the application also provides an embodiment of GNSS deception jamming detection equipment, and the GNSS deception jamming detection equipment in the embodiment comprises a processor and a memory; the memory is used for storing the program codes and transmitting the program codes to the processor; the processor is configured to execute the GNSS spoofing interference detection method according to the first embodiment or the second embodiment according to instructions in the program code.
An embodiment of the present invention further provides an embodiment of a storage medium, where the storage medium is used to store a program code, and the program code is used to execute the GNSS spoofing interference detection method according to the first embodiment or the second embodiment.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be implemented, for example, a plurality of units or components may be combined or integrated into another grid network to be installed, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to the needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A GNSS spoofing interference detection method is characterized by comprising the following steps:
acquiring a two-dimensional search matrix to be analyzed of Doppler frequency shift-pseudo code phase when a GNSS receiver captures a satellite signal to be analyzed;
counting a first number of first correlation peaks which are larger than a preset threshold in the two-dimensional search matrix to be analyzed;
when the first number is 1, in the two-dimensional search matrix to be analyzed, taking the maximum first correlation peak as a center, and intercepting data in a preset range to form an intermediate matrix to be analyzed, wherein the preset range is as follows: a region of pseudo code phase ± 2 chips of the largest first correlation peak;
setting the data lower than the preset threshold in the intermediate matrix to be analyzed to be 0 to obtain a target matrix;
normalizing the target matrix to obtain a target detection matrix;
and inputting the target detection matrix into a GAN network model to obtain a detection result output by the GAN network model, wherein the GAN network model is a mapping relation model of the target detection matrix and the detection result.
2. The GNSS spoofing interference detection method of claim 1, wherein the preset range further comprises: the maximum first correlation peak is in the region of 1kHz doppler shift.
3. The GNSS spoofing interference detecting method of claim 1, wherein the configuration process of the GAN network model includes:
acquiring a training matrix and a GAN network for training;
training the GAN network by taking the training matrix as an input parameter, taking a theoretical detection result of the training matrix corresponding to the deceptive interference as a target output result and taking an actual detection result of the training matrix corresponding to the deceptive interference as an actual output result to obtain an intermediate network model;
and taking a discriminator model in the intermediate network model as the GAN network model.
4. The GNSS spoof interference detection method of claim 3, wherein obtaining a training matrix for training specifically comprises:
acquiring a Doppler frequency shift-pseudo code phase training two-dimensional search matrix when the GNSS receiver captures a training satellite signal;
counting a second number of second correlation peaks larger than the preset threshold in the training two-dimensional search matrix;
when the second quantity is 1, intercepting data in the preset range to form a training intermediate matrix by taking the largest second correlation peak as the center in the training two-dimensional search matrix;
and setting 0 to the data lower than the preset threshold in the training intermediate matrix, and normalizing the training intermediate matrix after the 0 is set to obtain the training matrix.
5. The GNSS spoofing interference detection method of claim 1, further comprising:
and when the first quantity is 0, judging that the GNSS receiver does not receive satellite signals, and stopping the current detection process.
6. The GNSS spoofing interference detection method of claim 1, further comprising:
and when the first number is a numerical value more than 2, judging that a deception signal exists in the satellite signal to be analyzed, and stopping the current detection process.
7. A GNSS spoofing interference detection apparatus, comprising:
the acquisition unit is used for acquiring a two-dimensional search matrix to be analyzed of Doppler frequency shift-pseudo code phase when the GNSS receiver captures a satellite signal to be analyzed;
the statistical unit is used for counting the first number of the first correlation peaks which are larger than a preset threshold in the two-dimensional search matrix to be analyzed;
an intercepting unit, configured to intercept, when the first number is 1, data in a preset range from the two-dimensional search matrix to be analyzed by taking a maximum first correlation peak as a center to form an intermediate matrix to be analyzed, where the preset range is: a region of pseudo code phase ± 2 chips of the largest first correlation peak;
the preprocessing unit is used for setting the data lower than the preset threshold in the intermediate matrix to be analyzed to be 0 to obtain a target matrix;
the normalization unit is used for normalizing the target matrix to obtain a target detection matrix;
and the detection unit is used for inputting the target detection matrix into a GAN network model to obtain a detection result output by the GAN network model, wherein the GAN network model is a mapping relation model of the target detection matrix and the detection result.
8. The GNSS spoofing interference detecting apparatus of claim 7, wherein the preset range further includes: the maximum first correlation peak is in the region of 1kHz doppler shift.
9. A GNSS spoofing interference detection device, the device comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the GNSS spoofing interference detection method of any of claims 1-6 in accordance with instructions in the program code.
10. A storage medium for storing a program code for performing the GNSS spoofing interference detection method of any one of claims 1 to 6.
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