A GNSS Spoofing Interference Detection Method and System

Abstract

The present invention provides a GNSS spoofing interference detection method and system, which relates to the technical field of GNSS. By collecting GNSS signals of multiple satellites, the autocorrelation power information of each channel is obtained through a tracking loop; the leading, prompt, and lag correlator outputs of each channel are obtained from the autocorrelation power information of each channel, and a tracking loop autocorrelation power detection quantity of a single satellite is constructed; the tracking loop autocorrelation detection quantity is subjected to a moving average filtering process to generate a TLAP-MA detection quantity; the TLAP-MA detection quantities of multiple satellites are combined to construct a multi-satellite joint matrix, and dynamic classification and decision are performed based on a preset decision threshold; according to the dynamic classification decision result, multipath interference and spoofing attacks are distinguished, and a detection result is output. The technical effect of effectively detecting and distinguishing GNSS spoofing interference and multipath interference is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of GNSS, and in particular, to a GNSS spoofing interference detection method and system. Background Art

[0002] With the wide application of the Global Navigation Satellite System (GNSS) in both civilian and military fields, the security of navigation information faces many challenges. Due to reasons such as weak signals and public structures, civilian GNSS is extremely vulnerable to external spoofing interference. Spoofing detection and spoofing suppression are necessary means to ensure the security of GNSS navigation spatio-temporal information. Studying efficient and practical spoofing detection methods is one of the hot issues in the navigation field. According to the information and signal types used, spoofing detection technologies can be divided into three categories: spatial processing, measurement domain, and baseband signal processing. Compared with spatial processing technologies and measurement domain methods, baseband signal processing technologies have the advantages of simple design and low cost, and are a hot research direction for GNSS spoofing detection.

[0003] Baseband signal processing technology is one of the commonly used methods for spoofing detection in static scenarios, mainly including Signal Quality Montoring (SQM), power monitoring, Doppler frequency shift detection, etc. By monitoring the changes in the characteristics of the correlation peak in the tracking loop, the SQM technology was initially used to identify multipath signals and later applied to the field of navigation spoofing detection. Ratio and Delta are two commonly used types of indicators in SQM. Detection quantities are constructed using the in-phase branch leading, prompt, and lag correlation values to measure the symmetry and sharpness of the correlation peak, which can effectively detect the distortion of the correlation peak caused by multipath or spoofing in the frequency-locked scenario.

[0004] In the prior art, both multipath interference and spoofing attacks can cause distortion of the correlation peak, and traditional single-channel SQM detection quantities cannot effectively distinguish between the two. At the same time, current spoofing detection methods mainly target static spoofing, where the receiver and the spoofing source are fixed in an open experimental site with a clear view. Different from this, in dynamic scenarios such as complex channels in urban canyons and moving receiving platforms, the azimuth and elevation angles of multiple satellites are different, and reflection obstacles cause multipath interference to the signals of individual satellites. If the SQM detection quantity of this satellite is used for spoofing detection, misjudgment may occur. From the output results of the tracking channel, spoofing signals will affect all visible satellites, while multipath generally affects individual satellites.

[0005] Therefore, how to effectively detect and distinguish GNSS spoofing interference and multipath interference has become an urgent technical problem to be solved. Summary of the Invention

[0006] To achieve effective detection and differentiation between GNSS spoofing interference and multipath interference, the present application provides a GNSS spoofing interference detection method and system.

[0007] In a first aspect, a GNSS spoofing interference detection method provided by the present application adopts the following technical solution:

[0008] A GNSS spoofing interference detection method includes:

[0009] Collect GNSS signals of multiple satellites and obtain the autocorrelation power information of each channel through a tracking loop;

[0010] Obtain the outputs of the early, prompt, and late correlators of each channel from the autocorrelation power information of each channel, and construct the tracking loop autocorrelation power detection quantity of a single satellite;

[0011] Perform a moving average filtering process on the tracking loop autocorrelation detection quantity to generate a TLAP-MA detection quantity;

[0012] Combine the TLAP-MA detection quantities of multiple satellites to construct a multi-satellite joint matrix, and perform dynamic classification and decision-making based on a preset decision threshold;

[0013] According to the dynamic classification and decision result, differentiate multipath interference and spoofing attacks, and output the detection result.

[0014] Optionally, the step of obtaining the outputs of the early, prompt, and late correlators of each channel from the autocorrelation power information of each channel and constructing the tracking loop autocorrelation power detection quantity of a single satellite includes:

[0015] Determine the in-phase branch output values of the early, prompt, and late correlators in the autocorrelation power information of each channel 、 、 and the quadrature branch output values 、 、 , and calculate the tracking loop autocorrelation power detection quantity TLAP:

[0016]

[0017] where the specific calculation methods of E, P, and L are as follows:

[0018] .

[0019] Optionally, the window length of the moving average filtering process is 100 ms, and the expression of the TLAP-MA detection quantity is:

[0020]

[0021] wherein, is the length of each filtering window, N is the number of windows, and L is the sliding interval.

[0022] Optionally, the rule of the dynamic classification decision is:

[0023] Set the decision threshold = 50%, and traverse each column of the multi-satellite joint matrix;

[0024] If the detection probabilities of all satellites in the current column are all less than , it is determined as no interference;

[0025] If the detection probabilities of all satellites in the current column are all greater than , it is determined that there is a spoofing attack;

[0026] If the detection probabilities of some satellites in the current column are greater than , it is determined as multipath interference.

[0027] Optionally, the method further includes: in a dynamic scenario, adjusting the decision interval T and the number of decisions N in real time according to the motion state of the receiving platform to adapt to the time-varying characteristics of multipath interference and spoofing attacks.

[0028] Optionally, the step of constructing a multi-satellite joint matrix by combining the TLAP-MA detection quantities of multiple satellites includes:

[0029] Arrange the TLAP-MA detection probabilities of M satellites in a time series as an M×T matrix, where M is the number of satellite channels and T is the number of sampling points within the decision interval.

[0030] Optionally, the method further includes: setting the false alarm probability based on the Neyman-Pearson criterion, and optimizing the threshold of the detection threshold by statistically analyzing the detection probabilities within the sliding window.

[0031] In a second aspect, the present application provides a GNSS spoofing interference detection system, and the GNSS spoofing interference detection system includes:

[0032] An information acquisition module, configured to acquire GNSS signals of multiple satellites and obtain the autocorrelation power information of each channel through a tracking loop;

[0033] A construction module, configured to obtain the outputs of the leading, prompt, and lag correlators of each channel from the autocorrelation power information of each channel, and construct the tracking loop autocorrelation power detection quantity of a single satellite;

[0034] A detection quantity calculation module, configured to perform a moving average filtering process on the self-correlation detection quantity of the tracking loop to generate a TLAP-MA detection quantity;

[0035] A joint decision module, configured to jointly use the TLAP-MA detection quantities of multiple satellites to construct a multi-satellite joint matrix, and perform dynamic classification and decision-making based on a preset decision threshold;

[0036] An output result module, configured to distinguish multipath interference and spoofing attacks according to the dynamic classification decision result, and output a detection result.

[0037] In a third aspect, the present application provides a computer device, which includes: a memory and a processor. When the processor runs the computer instructions stored in the memory, it executes the method described above.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium, including instructions, which when run on a computer, cause the computer to execute the method described above.

[0039] In summary, the present application includes the following beneficial technical effects:

[0040] The present application collects GNSS signals of multiple satellites, and obtains the self-correlation power information of each channel through a tracking loop; obtains the outputs of the early, prompt, and late correlators of each channel in the self-correlation power information of each channel, and constructs a self-correlation power detection quantity of the tracking loop of a single satellite; performs a moving average filtering process on the self-correlation detection quantity of the tracking loop to generate a TLAP-MA detection quantity; jointly uses the TLAP-MA detection quantities of multiple satellites to construct a multi-satellite joint matrix, and performs dynamic classification and decision-making based on a preset decision threshold; distinguishes multipath interference and spoofing attacks according to the dynamic classification decision result, and outputs a detection result. It realizes the technical effect of effectively detecting and distinguishing GNSS spoofing interference and multipath interference. Description of the Drawings

[0041] Figure 1 It is a schematic structural diagram of a computer device in the hardware operating environment related to the solution of the embodiment of the present application;

[0042] Figure 2 It is a schematic flowchart of the first embodiment of the GNSS spoofing interference detection method of the present application;

[0043] Figure 3 It is a multipath interference model diagram in the GNSS spoofing interference detection method of the present application;

[0044] Figure 4 It is a schematic diagram of a generative spoofing principle in the GNSS spoofing interference detection method of the present application;

[0045] Figure 5 is the flowchart of spoofing interference detection in the GNSS spoofing interference detection method of this application;

[0046] Figure 6 is the structural block diagram of the first embodiment of the GNSS spoofing interference detection system of this application. Detailed implementation manners

[0047] In order to make the objectives, technical solutions and advantages of this application clearer, the following further elaborates on this application through the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0048] Refer to Figure 1 , Figure 1 which is the structural schematic diagram of the computer device for the hardware operating environment involved in the solution of the embodiment of this application.

[0049] As Figure 1 shown, the computer device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless-fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0050] Those skilled in the art can understand that Figure 1 the structure shown in

[0051] does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. Figure 1 As

[0052] shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and a GNSS spoofing interference detection program. Figure 1In the computer device shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in this application can be set in the computer device, and the computer device calls the GNSS spoofing interference detection program stored in the memory 1005 through the processor 1001 and executes the GNSS spoofing interference detection method provided in the embodiments of this application.

[0053] Embodiments of this application provide a GNSS spoofing interference detection method, referring to Figure 2 , Figure 2 which is a schematic flowchart of the first embodiment of the GNSS spoofing interference detection method of this application.

[0054] In this embodiment, the GNSS spoofing interference detection method includes the following steps:

[0055] Step S10: Collect GNSS signals of multiple satellites and obtain the autocorrelation power information of each channel through a tracking loop.

[0056] It should be noted that the term explanations in this embodiment are as follows:

[0057] GNSS: An abbreviation for Global Navigation Satellite System, which refers to a system that provides positioning, navigation, and timing services through satellites.

[0058] SQM: An abbreviation for Signal Quality Monitoring, which is an important process to ensure the reliability and accuracy of Global Navigation Satellite System (GNSS) signals. Its purpose is to detect and evaluate various interferences and anomalies in GNSS signals in order to provide high-quality navigation services.

[0059] In specific implementation, the application object described in this embodiment is intermediate spoofing interference and multipath interference. When there are spoofing interference and multipath interference in the received satellite signals, the spoofing interference and multipath interference can be detected earlier and more accurately.

[0060] When there are obstacles such as buildings and mountains, the GNSS signals are reflected, refracted, or diffracted during propagation, resulting in the signals arriving at the receiving end along multiple paths. Due to the difference in propagation path lengths, the arrival times of each component at the receiving antenna are different, causing signal fading or distortion. The multipath interference model is as Figure 3 shown. When there are multipath signals, the received signal consists of a true direct signal, multipath signals, and noise, and can be expressed as

[0061]

[0062] In the formula, the superscripts ad and m represent the true direct signal and the multipath signal respectively, and n(t) is Gaussian white noise with a mean of zero.

[0063] Different from the formation method of multipath interference, the generative spoofing spoofs signals according to the structure of the true satellite signal and broadcasts them to the receiver to seize the control of GNSS. The generation principle of the generative spoofing signal is as Figure 4 shown.

[0064] Step S20: Obtain the outputs of the early, prompt, and late correlators of each channel from the autocorrelation power information of each channel, and construct the autocorrelation power detection quantity of the tracking loop of a single satellite.

[0065] In a specific implementation, the step of obtaining the outputs of the early, prompt, and late correlators of each channel from the autocorrelation power information of each channel and constructing the autocorrelation power detection quantity of the tracking loop of a single satellite includes: determining the in-phase branch output values of the early, prompt, and late correlators in the autocorrelation power information of each channel 、 、 and the quadrature branch output values 、 、 , and calculating the autocorrelation power detection quantity TLAP of the tracking loop. The purpose is to comprehensively utilize the three information of E, P, and L to improve the sensitivity of the detection quantity to the abnormal output of the tracking loop, and at the same time to ensure a relatively small computational overhead:

[0066]

[0067] Among them, the specific calculation methods of E, P, and L are as follows:

[0068] .

[0069] In an open spoofing scenario (without considering multipath signals), the received signal includes the true direct signal, the spoofing signal, and Gaussian noise, expressed as

[0070]

[0071] Among them, the superscript s represents the spoofing signal. In a dynamic and complex spoofing scenario, the received signal includes the true signal, the multipath signal, the spoofing signal, and noise, and can be expressed as

[0072]

[0073] According to the formation mechanism of multipath interference and spoofing signals, the multipath signal, the true direct signal, and the spoofing signal have the same signal structure. They are respectively expressed as

[0074]

[0075] wherein, for signal power, C is a pseudo-random spreading code, is navigation data, is the code delay of C / A, is the Doppler frequency shift, is the carrier phase.

[0076] As a core component of the receiver tracking loop, the correlator is used to strip the pseudo-code. Taking the C / A code of the GPS L1 carrier as an example, the correlator output is

[0077]

[0078] wherein, , , and are the cross-correlation results of the true direct signal, multipath signal, spoofing signal, and noise with the local code respectively. From the characteristics of the pseudo-random noise (PRN) code, is expressed as

[0079]

[0080] Since the spoofing signal, multipath signal, and true direct signal have the same structure, so and can be expressed as:

[0081]

[0082] wherein, and represent the time delays between the multipath signal, spoofing signal, and true direct signal respectively.

[0083] The tracking loop consists of three pairs of correlators: early, prompt, and late, with a chip spacing of 0.5 chips and a coherent integration time of 1 ms. Their outputs are IE (QE), IP (QP), and IL (QL) respectively. Taking the output of the prompt correlator as an example, the in-phase branch IP and the quadrature branch QP outputs are

[0084]

[0085] wherein, is the autocorrelation power of the spreading code. The output amplitudes E, P, and L of the early, prompt, and late correlators are

[0086]

[0087] Among them, E, P, and L all follow the Rice distribution. When the signal-to-noise ratio is much greater than 1, E, P, and L all approximately follow the normal distribution, and this distribution characteristic provides a theoretical basis for the setting of the detection threshold in the SQM-class algorithm.

[0088] Step S30: Perform a moving average filtering process on the tracking loop autocorrelation detection quantity to generate a TLAP-MA detection quantity.

[0089] It should be noted that the window length ω of the moving average filtering process is 100 ms, and the expression of the TLAP-MA detection quantity is:

[0090]

[0091] where ω is the length of each filtering window, N is the number of windows, and L is the sliding interval.

[0092] Step S40: Combine the TLAP-MA detection quantities of multiple satellites to construct a multi-satellite joint matrix, and perform dynamic classification and decision-making based on a preset decision threshold.

[0093] In a specific implementation, the rule of the dynamic classification and decision-making is: set the decision threshold = 50%, and traverse each column of the multi-satellite joint matrix; if the detection probabilities of all satellites in the current column are all less than , it is determined as no interference; if the detection probabilities of all satellites in the current column are all greater than , it is determined as the existence of spoofing attack; if the detection probabilities of some satellites in the current column are greater than , it is determined as multipath interference.

[0094] It should be noted that the step of combining the TLAP-MA detection quantities of multiple satellites to construct a multi-satellite joint matrix includes:

[0095] Arrange the TLAP-MA detection probabilities of M satellites in a time series as an M×T matrix, where M is the number of satellite channels and T is the number of sampling points within the decision interval.

[0096] In a specific implementation, the overall algorithm flow chart corresponding to this step is as Figure 5 shown. The TLAP-MA-MSC jointly utilizes the TLAP-MA detection results of multiple satellites to distinguish spoofing attacks and multipath interference. The algorithm steps are as follows:

[0097] Step 1: Set the number of tracking channels to M, the decision interval to T seconds, the number of decisions to N, each channel tracks for NT seconds, and the initial values of the loop parameters k and j are 0;

[0098] Step 2: k = k + 1, calculate the TLAP value of the k-th channel;

[0099] Step 3: Calculate the TLAP-MA value, detection threshold and ;

[0100] Step 4: Store the of TLAP-MA;

[0101] Step 5: Determine if k = M? Yes: Combine the TLAP-MA of M satellites into a matrix A with M rows and T columns; No: Return to Step 2; Combine the TLAP-MA of M satellites into a matrix A with M rows and T columns; No: Return to Step 2;

[0102] Step 6: j = (j + 1), does the j-th column of A have an element greater than 50%? Yes: Execute the next step; No: No interference;

[0103] Step 7: Determine if all elements in the j-th column of A are greater than 50% of the elements? Yes: Judge as spoofing interference and execute the next step; No: Judge as multipath interference;

[0104] Step 8: Is j = N? Yes, return to Step 6; No, end the detection.

[0105] Step S50: According to the dynamic classification and decision result, distinguish multipath interference from spoofing attacks and output the detection result.

[0106] In a specific implementation, the method further includes: in a dynamic scenario, adjusting the decision interval T and the number of decisions N in real time according to the motion state of the receiving platform to adapt to the time-varying characteristics of multipath interference and spoofing attacks.

[0107] It can be understood that the method further includes: setting the false alarm probability based on the Neyman-Pearson criterion and optimizing the threshold of the detection threshold by statistically calculating the detection probability within the statistical sliding window .

[0108] In a specific implementation, the description of the detection threshold and probability analysis in this embodiment is as follows:

[0109] In the signal detection problem, the distribution characteristics of the detection quantity are the theoretical basis for setting the detection threshold. Considering the form of the three summation terms in the TLAP detection quantity, let the product of the random variables R and V be , then the probability density function of Y is

[0110]

[0111] where, is the joint probability density function (PDF) of R and V. Since the integrand in the formula has a complex structure, it is difficult to calculate the theoretical expression of the PDF of the TLAP measurement. According to the central limit theorem, the sum of a large number of independent and identically distributed random variables will tend to a normal distribution. The TLAP measurement is composed of the sum of three random variables with the same type of distribution. It is assumed that it approximately follows a normal distribution, and further statistical verification is carried out on this hypothesis. In mathematical statistics theory, skewness (S) and kurtosis (K) are often used to test the normality of the distribution of the measurement.

[0112] Skewness is used to measure the symmetry of the PDF of a random variable: when S > 0, its PDF shows right skewness, and vice versa shows left skewness; when S = 0, the PDF is symmetric about the mean. Since the number of actual sampled signals is limited, the sample skewness is often used to replace the skewness in the theoretical sense, which is expressed as

[0113]

[0114] where is the sample point, is the mean of the sample U.

[0115] Kurtosis reflects the thickness of the tail of the PDF of a random variable. Compared with the PDF of the standard normal distribution, when K < 3, the PDF of the random variable shows a fat tail, and vice versa shows a thin tail; when K = 3, the PDF shows the tail of the standard normal distribution. In practice, the sample kurtosis is often used to replace the kurtosis in the theoretical sense, which is expressed as

[0116]

[0117] The standard errors of the sample skewness and the sample kurtosis are respectively expressed as

[0118]

[0119]

[0120] Furthermore, the Z-scores (ZS) of the sample skewness and the sample kurtosis are respectively

[0121]

[0122]

[0123] Based on the sample skewness and the sample kurtosis to test the normal distribution, the higher the absolute value of its Z-score, the higher the correct rate of the test. When the Z-score is greater than 2.575, the correct rate is not less than 99%.

[0124] Without loss of generality, randomly select 10 satellite signals (i.e., clean signals) during the period without spoofing signal injection in TEXBAT, and statistically analyze the normality index of the TLAP detection quantity. The results are shown in Table 1:

[0125]

[0126] In Table 1, for all satellites, <0.3, indicating that the distribution of the TLAP detection quantity is approximately symmetric; Close to 3, indicating that the envelope of the distribution of the TLAP detection quantity is approximately the same as that of the standard normal distribution. The absolute value of the Z-score is much greater than 2.575, and the correct rate of the test is close to 100%. Based on the above statistical results, it can be seen that the TLAP detection quantity approximately follows a normal distribution. Let its mean be and the variance be , and Z = MTLAP. Then the PDF of Z can be expressed as

[0127]

[0128] Taking satellite N0.1 in the clean DS in Table 1 as an example, the statistical result of the mean of the TLAP detection quantity is , and the statistical result of the variance is .

[0129] When there is no multipath interference, spoofing detection can be regarded as a binary hypothesis testing problem. Let represent no spoofing, and represent spoofing, that is,

[0130]

[0131] Considering the change of the detection quantity, the spoofing detection problem generally adopts a double-threshold method. The false alarm probability and the detection probability

[0132]

[0133] Among them, and are the upper and lower limits of the detection, respectively, expressed as

[0134]

[0135]

[0136] Among them, is the complementary error function.

[0137] Based on the Neyman-Pearson (NP) criterion, preset , and after calculating the detection threshold therefrom, further evaluate . In practice, the parameters of the spoofing signal are time-varying and unknown, and the theoretical result of Pd cannot be obtained, so a statistical method is used to calculate

[0138]

[0139] where q is the total number of samples in the window, and N() is the number of samples that meet the conditions.

[0140] In summary, the spoofing detection and evaluation process is as follows: preset , jointly solve the detection threshold with the above formula, and substitute the threshold to calculate the in each sliding window.

[0141] To evaluate the generalization detection performance of the algorithm, statistically calculate the under the value conditions of multiple satellites and multiple to obtain the average detection probability , expressed as

[0142]

[0143] where I is the number of satellite, J is the number of different settings, is the under the value of a certain satellite and a given .

[0144] In a specific implementation, this embodiment discloses a GNSS spoofing interference detection method, aiming to solve the problem that the existing technology cannot effectively distinguish multipath interference from spoofing attacks. The method includes: collecting GNSS signals of multiple satellites, and obtaining the autocorrelation power information of each channel through a tracking loop; constructing a tracking loop autocorrelation power (TLAP) measurement based on the outputs of early, prompt, and late correlators; performing a moving average (MA) filter on the TLAP to generate a TLAP-MA measurement; jointly constructing a multi-satellite combination (MSC) matrix with the TLAP-MA measurements of multiple satellites, and dynamically classifying and judging through a preset decision threshold to distinguish multipath interference from spoofing attacks. The TLAP-MA-MSC algorithm significantly improves the detection probability and accuracy, and shows excellent robustness and timeliness in dynamic scenarios. Experiments verify that its detection probability in the TEXBAT dataset exceeds 85%, and it can effectively distinguish spoofing attacks from multipath interference in complex environments.

[0145] In this embodiment, GNSS signals of multiple satellites are collected, and the autocorrelation power information of each channel is obtained through a tracking loop; the leading, prompt, and lag correlator outputs of each channel are obtained from the autocorrelation power information of each channel to construct an autocorrelation power detection quantity of the tracking loop of a single satellite; the tracking loop autocorrelation detection quantity is subjected to a moving average filtering process to generate a TLAP-MA detection quantity; the TLAP-MA detection quantities of multiple satellites are combined to construct a multi-satellite combined matrix, and dynamic classification and decision are performed based on a preset decision threshold; according to the dynamic classification and decision result, multipath interference and spoofing attacks are distinguished, and a detection result is output. The technical effect of effectively detecting and distinguishing GNSS spoofing interference and multipath interference is achieved.

[0146] In addition, an embodiment of the present application further provides a computer-readable storage medium, on which a program for GNSS spoofing interference detection is stored. When the program for GNSS spoofing interference detection is executed by a processor, the steps of the method for GNSS spoofing interference detection as described above are implemented.

[0147] Refer to Figure 6 , Figure 6 which is a structural block diagram of the first embodiment of the GNSS spoofing interference detection system of the present application.

[0148] As Figure 6 shown, the GNSS spoofing interference detection system proposed in the embodiment of the present application includes:

[0149] An information collection module 10, configured to collect GNSS signals of multiple satellites and obtain the autocorrelation power information of each channel through a tracking loop;

[0150] A construction module 20, configured to obtain the leading, prompt, and lag correlator outputs of each channel from the autocorrelation power information of each channel and construct an autocorrelation power detection quantity of the tracking loop of a single satellite;

[0151] A detection quantity calculation module 30, configured to perform a moving average filtering process on the tracking loop autocorrelation detection quantity to generate a TLAP-MA detection quantity;

[0152] A joint decision module 40, configured to combine the TLAP-MA detection quantities of multiple satellites to construct a multi-satellite combined matrix and perform dynamic classification and decision based on a preset decision threshold;

[0153] An output result module 50, configured to distinguish multipath interference and spoofing attacks according to the dynamic classification and decision result and output a detection result.

[0154] It should be understood that the above is only for illustration and does not constitute any limitation to the technical solution of the present application. In specific applications, those skilled in the art can set according to needs, and the present application does not make any restrictions on this.

[0155] In this embodiment, GNSS signals of multiple satellites are collected, and the autocorrelation power information of each channel is obtained through a tracking loop; the early, prompt, and late correlator outputs of each channel are obtained from the autocorrelation power information of each channel to construct an autocorrelation power detection quantity of the tracking loop of a single satellite; the tracking loop autocorrelation detection quantity is subjected to a moving average filtering process to generate a TLAP-MA detection quantity; the TLAP-MA detection quantities of multiple satellites are combined to construct a multi-satellite joint matrix, and dynamic classification and decision-making are performed based on a preset decision threshold; according to the dynamic classification and decision result, multipath interference and spoofing attacks are distinguished, and a detection result is output. The technical effect of effectively detecting and distinguishing GNSS spoofing interference and multipath interference is achieved.

[0156] It should be noted that the above-described work process is only illustrative and does not limit the protection scope of this application. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and no limitation is made here.

[0157] In addition, for the technical details not described in detail in this embodiment, reference can be made to the GNSS spoofing interference detection method provided in any embodiment of this application, which will not be elaborated here.

[0158] In addition, it should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0159] The serial numbers of the above embodiments of this application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0160] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0161] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A GNSS spoofing interference detection method, characterized in that: include: Collect GNSS signals from multiple satellites and obtain the autocorrelation power information of each channel through a tracking loop; The advance, immediate and delayed correlator outputs of each channel are obtained from the autocorrelation power information of each channel, and the autocorrelation power detection quantity of the tracking loop of a single satellite is constructed; Performing sliding average filtering on the tracking loop autocorrelation detection amount to generate a TLAP-MA detection amount; Combine the TLAP-MA detection data of multiple satellites to build a multi-satellite joint matrix, and make dynamic classification decisions based on the preset decision threshold; According to the dynamic classification judgment result, distinguish multipath interference from deception attack and output the detection result; The step of obtaining the advance, immediate and delayed correlator outputs of each channel from the autocorrelation power information of each channel and constructing the autocorrelation power detection amount of the tracking loop of a single satellite includes: Determine the in-phase branch output values ​​of the leading, immediate and lagging correlators in the autocorrelation power information of each channel , , And the orthogonal branch output value , , , calculate the tracking loop autocorrelation power detection quantity TLAP: The specific calculation methods of E, P and L are as follows: 。 2. The method according to claim 1, characterized in that: The sliding average filtering process The window length is 100 ms, and the expression of the TLAP-MA detection amount is: in, is the length of each filter window, N is the number of windows, and L is the sliding interval.

3. The method according to claim 1, characterized in that: The rules for dynamic classification decision are: Setting the decision threshold =50%, and traverse each column of the multi-star joint matrix; If the detection probability of all satellites in the current column All less than , it is judged as no interference; If the detection probability of all satellites in the current column Both greater than , it is determined that there is a deception attack; If the detection probability of some satellites in the current column Greater than , determined to be multipath interference.

4. The method according to claim 1, characterized in that The method further includes: in a dynamic scenario, adjusting the decision interval T and the number of decisions N in real time according to the motion state of the receiving platform to adapt to the time-varying characteristics of multipath interference and deception attacks.

5. The method according to claim 1, characterized in that: The step of combining the TLAP-MA detection quantities of multiple satellites to construct a multi-satellite joint matrix includes: The TLAP-MA detection probability of M satellites The time series are arranged into an M×T matrix, where M is the number of satellite channels and T is the number of sampling points in the decision interval.

6. The method according to claim 1, characterized in that The method further comprises: setting a false alarm probability based on the Neyman-Pearson criterion , and by counting the detection probability within the sliding window , optimize the threshold of the detection threshold.

7. A GNSS spoofing interference detection system, characterized in that: Executing the method according to claim 1, the GNSS spoofing interference detection system comprises: The information acquisition module is used to collect GNSS signals from multiple satellites and obtain the autocorrelation power information of each channel through a tracking loop; A construction module is used to obtain the advance, immediate and delayed correlator outputs of each channel from the autocorrelation power information of each channel, and construct the autocorrelation power detection amount of the tracking loop of a single satellite; A detection amount calculation module, used for performing sliding average filtering on the tracking loop autocorrelation detection amount to generate a TLAP-MA detection amount; The joint decision module is used to combine the TLAP-MA detection quantities of multiple satellites, build a multi-satellite joint matrix, and perform dynamic classification and decision based on the preset decision threshold; The output result module is used to distinguish multipath interference from deception attack according to the dynamic classification judgment result and output the detection result.

8. A computer device, characterized in that: The device comprises: a memory and a processor, and when the processor runs the computer instructions stored in the memory, the processor executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The method comprises instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 6.

Images