GNSS deception jamming detection method and system

Abstract

The invention provides a GNSS deception jamming detection method and system, and relates to the technical field of GNSS. GNSS signals of a plurality of satellites are collected, and self-correlation power information of each channel is obtained through a tracking loop; acquiring lead, instant and lag correlator outputs of each channel from the self-correlation power information of each channel, and constructing a tracking loop self-correlation power detection quantity of a single satellite; performing moving average filtering processing on the tracking loop autocorrelation detection quantity to generate a TLAP-MA detection quantity; combining the TLAP-MA detection quantities of the plurality of satellites to construct a multi-satellite combined matrix, and performing dynamic classification judgment based on a preset judgment threshold; and according to the dynamic classification judgment result, multi-path interference and spoofing attack are distinguished, and a detection result is output. The technical effect of effectively detecting and distinguishing GNSS deception jamming and multipath jamming is achieved.

Description

Technical Field

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

[0002] With the widespread application of the Global Navigation Satellite System (GNSS) in military and civilian fields, the security of navigation information faces many challenges. Due to weak signals and open structures, civilian GNSS is extremely susceptible to external deception interference. Deception detection and deception suppression are necessary means to ensure the security of GNSS navigation spatiotemporal information. Researching efficient and practical deception detection methods is one of the hot issues in the navigation field. Depending on the type of information and signal used, deception detection technology can be divided into three categories: spatial processing, measurement domain, and baseband signal processing. Compared with spatial processing technology and measurement domain methods, baseband signal processing technology has the advantages of simple design and low cost, and is a hot topic in the study of GNSS deception detection.

[0003] Baseband signal processing technology is one of the commonly used methods for deception detection in static scenarios, mainly including signal quality monitoring (SQM), power monitoring, Doppler shift detection, etc. SQM technology was initially used to identify multipath signals by monitoring changes in the correlation peak characteristics of the tracking loop, and was later applied to the detection of navigation deception. Ratio and Delta are two commonly used indicators in SQM. The detection quantity is constructed using the leading, immediate, and lagging correlation values ​​on the in-phase branch to measure the symmetry and sharpness of the correlation peak, which can effectively detect the correlation peak distortion caused by multipath or deception in the frequency locking scenario.

[0004] In the existing technology, both multipath interference and spoofing attacks will cause correlation peak distortion, and the traditional single-channel SQM detection quantity cannot effectively distinguish between the two. At the same time, the current deception detection method is mainly aimed at static deception, and the receiver and the deception source are fixed in the experimental site with a wide field of view. In contrast, in dynamic scenes such as complex channels in urban canyons and mobile receiving platforms, multiple satellites have different azimuths and elevations, and reflective obstructions cause multipath interference to individual satellite signals. If the SQM detection quantity of the satellite is used for deception detection, it may cause misjudgment. Judging from the output results of the tracking channel, the deception signal will affect all visible stars, while multipath generally affects individual satellites.

[0005] Therefore, how to effectively detect and distinguish GNSS spoofing interference and multipath interference has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] In order to effectively detect and distinguish GNSS spoofing interference and multipath interference, the present application provides a GNSS spoofing interference detection method and system.

[0007] In the first aspect, a GNSS spoofing interference detection method provided by the present application adopts the following technical solution: A GNSS spoofing interference detection method, comprising: 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, multipath interference and deception attack are distinguished, and the detection result is output.

[0008] Optionally, the step of acquiring the advance, immediate and delayed correlator outputs of each channel from the autocorrelation power information of each channel to construct the tracking loop autocorrelation power detection amount 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: .

[0009] Optionally, the window length of the sliding average filtering process is 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.

[0010] Optionally, the rule for dynamic classification decision is: 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.

[0011] 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 deception attacks.

[0012] Optionally, the step of combining 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.

[0013] Optionally, the method further comprises: setting the 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.

[0014] In a second aspect, the present application provides a GNSS spoofing interference detection system, the GNSS spoofing interference detection system comprising: 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.

[0015] In a third aspect, the present application provides a computer device, comprising: a memory and a processor, wherein the processor executes the method described above when running computer instructions stored in the memory.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a computer, enable the computer to execute the method described above.

[0017] In summary, the present application includes the following beneficial technical effects: This application collects GNSS signals from multiple satellites, obtains the autocorrelation power information of each channel through a tracking loop; obtains the advance, immediate, and delayed correlator outputs of each channel from the autocorrelation power information of each channel, and constructs a tracking loop autocorrelation power detection amount of a single satellite; performs sliding average filtering on the tracking loop autocorrelation detection amount to generate a TLAP-MA detection amount; combines the TLAP-MA detection amounts of multiple satellites to construct a multi-satellite joint matrix, and performs dynamic classification and judgment based on a preset judgment threshold; distinguishes multipath interference from deception attacks based on the dynamic classification judgment results, and outputs the detection results. The technical effect of effectively detecting and distinguishing GNSS deception interference and multipath interference is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the computer device structure of the hardware operating environment involved in the embodiment of the present application; Figure 2 It is a flowchart of the first embodiment of the GNSS spoofing interference detection method of the present application; Figure 3 It is a multipath interference model diagram in the GNSS deception interference detection method of the present application; Figure 4 This is a schematic diagram of the generative deception principle in the GNSS deception interference detection method of the present application; Figure 5 It is a deception interference detection flow chart in the GNSS deception interference detection method of the present application; Figure 6 It is a structural block diagram of the first embodiment of the GNSS spoofing interference detection system of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below through the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0020] Reference Figure 1 , Figure 1A schematic diagram of the computer device structure of the hardware operating environment involved in the embodiment of the present application.

[0021] like Figure 1 As 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), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also 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 (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 storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0022] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the computer device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0023] like Figure 1 As 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.

[0024] exist Figure 1 In the computer device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the present 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 embodiment of the present application.

[0025] The present application embodiment provides a GNSS spoofing interference detection method, referring to Figure 2 , Figure 2 This is a flow chart of the first embodiment of the GNSS spoofing interference detection method of the present application.

[0026] In this embodiment, the GNSS spoofing interference detection method includes the following steps: Step S10: Collect GNSS signals from multiple satellites and obtain autocorrelation power information of each channel through a tracking loop.

[0027] It should be noted that the terms in this embodiment are explained as follows: GNSS: The abbreviation of Global Navigation Satellite System, refers to a system that provides positioning, navigation and timing services through satellites.

[0028] SQM: Signal Quality Monitoring is an important process to ensure the reliability and accuracy of the Global Navigation Satellite System (GNSS) signal. Its purpose is to detect and evaluate various interferences and anomalies in GNSS signals in order to provide high-quality navigation services.

[0029] In a specific implementation, the application object described in this embodiment is intermediate deception interference and multipath interference. When deception interference and multipath interference exist in the received satellite signal, the deception interference and multipath interference can be detected as early as possible and more accurately.

[0030] When there are obstacles such as buildings and mountains, GNSS signals are reflected, refracted or diffracted during propagation, causing the signal to reach the receiving end along multiple paths. Due to the difference in propagation path length, each component arrives at the receiving antenna at different times, causing signal fading or distortion. The multipath interference model is as follows: Figure 3 When there are multipath signals, the received signal consists of the true direct signal, multipath signal and noise, which can be expressed as Wherein, the superscripts ad and m represent the true direct signal and multipath signal respectively, and n(t) is Gaussian white noise with zero mean.

[0031] Different from the formation of multipath interference, generative spoofing uses the structure of real satellite signals to spoof signals and broadcasts them to receivers to seize control of GNSS. Figure 4 shown.

[0032] Step S20: 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.

[0033] In a specific implementation, the step of obtaining the output of the leading, immediate and delayed correlators 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: determining the in-phase branch output value of the leading, immediate and delayed 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 purpose of which is to comprehensively utilize the three information of E, P and L, improve the sensitivity of the detection quantity to the abnormal output of the tracking loop, and at the same time ensure a relatively small calculation cost: The specific calculation methods of E, P and L are as follows: .

[0034] In the spoofing scenario with a wide field of view (without considering multipath signals), the received signal contains the real direct signal, the spoofing signal and Gaussian noise, which can be expressed as The superscript s represents the spoofing signal. In a dynamic and complex spoofing scenario, the received signal contains the real signal, multipath signal, spoofing signal and noise, which can be expressed as According to the formation mechanism of multipath interference and deceptive signals, multipath signals, real direct signals and deceptive signals have the same signal structure. They are respectively expressed as Among them, signal power, C is the pseudo-random spreading code, For navigation data, is the code delay of C / A, is the Doppler shift, is the carrier phase.

[0035] As the core component of the receiver tracking loop, the correlator is used to strip the pseudo code. Taking the C / A code of the L1 carrier of GPS as an example, the correlator output is in, , , and They are the cross-correlation results of the real direct signal, multipath signal, deception signal and noise with the local code. According to the characteristics of the Pseudo-Random Noise (PRN) code, Expressed as Since the deceptive signal, multipath signal and real direct signal have the same structure, and Can be expressed as: in, and They represent the time delays between the multipath signal, the spoofing signal and the real direct signal respectively.

[0036] The tracking loop consists of three pairs of correlators, namely, advance, immediate and delayed, with an interval of 0.5 code chips and a coherent integration time of 1ms. Their outputs are IE (QE), IP (QP) and IL (QL) respectively. Taking the output of the immediate correlator as an example, the outputs of the in-phase branch IP and the orthogonal branch QP are in, is the autocorrelation power of the spread spectrum code. The output amplitudes E, P and L of the leading, immediate and lagging correlators are respectively Among them, E, P and L all obey Rice distribution. When the signal-to-noise ratio is much greater than 1, E, P and L all approximately obey normal distribution. This distribution characteristic provides a theoretical basis for setting the detection threshold in SQM algorithms.

[0037] Step S30: performing sliding average filtering on the tracking loop autocorrelation detection amount to generate a TLAP-MA detection amount.

[0038] It should be noted that the window length ω of the sliding average filtering process is 100 ms, and the expression of the TLAP-MA detection amount is: Among them, ω is the length of each filter window, N is the number of windows, and L is the sliding interval.

[0039] Step S40: Combine the TLAP-MA detection quantities of multiple satellites to construct a multi-satellite joint matrix, and perform dynamic classification and decision based on a preset decision threshold.

[0040] In the specific implementation, the dynamic classification decision rule is: set the decision threshold =50%, and traverse each column of the multi-satellite joint matrix; if the detection probability of all satellites in the current column All less than , it is determined that there is 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.

[0041] 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: 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.

[0042] In the specific implementation, the overall algorithm flow chart corresponding to this step is as follows Figure 5 As shown in FIG. 1 , TLAP-MA-MSC jointly utilizes the TLAP-MA detection results of multiple satellites to distinguish between spoofing attacks and multipath interference. The algorithm steps are as follows: Step 1: Set the number of tracking channels to M, the decision interval to T seconds, the number of decisions to N, each channel tracking for NT seconds, and the initial values ​​of loop parameters k and j to 0; Step 2: k=k+1, calculate the TLAP value of the kth channel; Step 3: Calculate TLAP-MA value, detection threshold and ; Step 4: Storage of TLAP-MA ; Step 5: Determine if k=M? Yes: Substitute the TLAP-MA of M satellites Combine into a matrix A with M rows and T columns; No: return to step 2; Step 6: j=(j+1), does the jth column of A have more than 50% elements? Yes: proceed to the next step; No: no interference; Step 7: Determine whether all elements in the jth column of A are greater than 50% of the elements. If yes, it is judged as deception interference and proceed to the next step; if no, it is judged as multipath interference; Step 8: j=N? If yes, return to step 6; if no, end the test.

[0043] Step S50: Distinguish multipath interference from deception attack according to the dynamic classification judgment result, and output the detection result.

[0044] 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 deception attacks.

[0045] It is understandable that the method further includes: setting the 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.

[0046] In the specific implementation, the description of the detection threshold and probability analysis in this embodiment is as follows: In the signal detection problem, the distribution characteristics of the detection quantity are the theoretical basis for setting the detection threshold. Consider the form of the three summation terms in the TLAP detection quantity, let the product of random variables R and V be , then the probability density function of Y is in, is the joint probability density function (PDF) of R and V. Due to the complex structure of the integrand in the formula, it is difficult to calculate the theoretical expression of the PDF of the TLAP test quantity. According to the central limit theorem, the sum of a large number of independent and identically distributed random variables will approach the normal distribution. The TLAP test quantity is composed of the sum of three random variables with the same distribution. The one that approximately obeys the normal distribution is taken to further statistically verify the hypothesis. In the theory of mathematical statistics, skewness (S) and kurtosis (K) are often used to test the normality of the distribution of the test quantity.

[0047] Skewness is used to measure the symmetry of the PDF of a random variable: when S>0, its PDF is right-skewed, otherwise it is left-skewed; when S=0, the PDF is symmetric about the mean. The number of actual sampled signals is limited, and sample skewness is often used instead of theoretical skewness, expressed as in, is the sample point, is the mean of sample U.

[0048] Kurtosis reflects the thickness of the tail of the PDF of a random variable. Compared with the PDF of a standard normal distribution, when K<3, the PDF of the random variable exhibits a fat tail, and vice versa; when K=3, the PDF exhibits the tail of a standard normal distribution. In practice, sample kurtosis is often used instead of theoretical kurtosis, expressed as The standard error of sample skewness and sample kurtosis are expressed as The Z-score (ZS) of sample skewness and sample kurtosis are further introduced as The normal distribution is tested based on sample skewness and sample kurtosis. The higher the absolute value of the Z score, the higher the accuracy of the identification test. When the Z score is greater than 2.575, the accuracy is no less than 99%.

[0049] Without loss of generality, we randomly select 10 satellite signals (i.e., clean signals) during the period without deceptive signal injection in TEXBAT, and statistically analyze the normality index of TLAP detection quantity. The results are shown in Table 1: In Table 1, all satellites <0.3, indicating that the distribution of TLAP detection is approximately symmetrical; Close to 3, indicating that the envelope of the distribution of TLAP detection is close to the standard normal distribution envelope. The absolute value of the Z score is much greater than 2.575, and the accuracy of the test is close to 100%. Based on the above statistical results, it can be seen that the TLAP detection quantity is nearly subject to the normal distribution, and its mean is , the variance is , Z = MTLAP, then the PDF of Z can be expressed as Taking the No. 1 satellite in the clean DS in Table 1 as an example, the mean statistical result of the TLAP detection amount is The variance statistics are: .

[0050] When there is no multipath interference, deception detection can be regarded as a binary hypothesis testing problem. For no deception, To deceive, that is Considering the change of detection amount, the deception detection problem generally adopts the double threshold method and the detection probability They are in, and are the upper and lower limits of detection, respectively, expressed as in, is the complementary error function.

[0051] Based on the Neyman-Pearson (NP) criterion, the , and then further evaluate the detection threshold In practice, the parameters of the deception signal are time-varying and unknown, and the theoretical results of Pd cannot be obtained, so a statistical method is used to calculate Among them, q is the total number of samples in the window, and N() is the number of samples that meet the conditions.

[0052] Based on the above analysis, the deception detection and evaluation process is as follows: , combine the above formula to find the detection threshold, and bring the threshold into the calculation to calculate the value in each sliding window .

[0053] In order to evaluate the generalization detection performance of the algorithm, multiple satellites and multiple Under the condition of value , and the average detection probability is , expressed as Among them, I is the number of satellites, J is the number of satellites set The number of For a satellite, given Under the value .

[0054] In the specific implementation, this embodiment discloses a GNSS deception interference detection method, which aims to solve the problem that the existing technology cannot effectively distinguish multipath interference from deception attacks. The method includes: collecting GNSS signals from multiple satellites, obtaining the autocorrelation power information of each channel through a tracking loop; constructing a tracking loop autocorrelation power (TLAP) detection quantity based on the outputs of the leading, immediate, and lagging correlators; performing a sliding average (MA) filter on the TLAP to generate a TLAP-MA detection quantity; combining the TLAP-MA detection quantities of multiple satellites to construct a multi-satellite joint (MSC) matrix, and dynamically classifying and judging through a preset decision threshold to distinguish multipath interference from deception attacks. The TLAP-MA-MSC algorithm significantly improves the detection probability and accuracy, and exhibits excellent robustness and immediacy in dynamic scenes. Experiments have verified that its detection probability in the TEXBAT dataset exceeds 85%, and it can effectively distinguish deception attacks from multipath interference in complex environments.

[0055] This embodiment collects GNSS signals from multiple satellites and obtains the autocorrelation power information of each channel through a tracking loop; obtains the output of the leading, immediate, and lagging correlators of each channel from the autocorrelation power information of each channel, and constructs a tracking loop autocorrelation power detection amount of a single satellite; performs sliding average filtering on the tracking loop autocorrelation detection amount to generate a TLAP-MA detection amount; combines the TLAP-MA detection amounts of multiple satellites to construct a multi-satellite joint matrix, and performs dynamic classification and judgment based on a preset judgment threshold; distinguishes multipath interference from deception attacks based on the dynamic classification and judgment results, and outputs the detection results. The technical effect of effectively detecting and distinguishing GNSS deception interference and multipath interference is achieved.

[0056] In addition, an embodiment of the present application further proposes 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.

[0057] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the GNSS spoofing interference detection system of the present application.

[0058] like Figure 6 As shown, the GNSS spoofing interference detection system proposed in the embodiment of the present application includes: The information acquisition module 10 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 20 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 tracking loop autocorrelation power detection amount of a single satellite; A detection amount calculation module 30, used for performing sliding average filtering on the tracking loop autocorrelation detection amount to generate a TLAP-MA detection amount; A joint decision module 40 is used to combine the TLAP-MA detection quantities of multiple satellites, construct a multi-satellite joint matrix, and perform dynamic classification and decision based on a preset decision threshold; The output result module 50 is used to distinguish multipath interference from deception attack according to the dynamic classification judgment result and output the detection result.

[0059] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present application. In specific applications, technicians in this field can make settings as needed, and the present application does not impose any limitation on this.

[0060] This embodiment collects GNSS signals from multiple satellites and obtains the autocorrelation power information of each channel through a tracking loop; obtains the output of the leading, immediate, and lagging correlators of each channel from the autocorrelation power information of each channel, and constructs a tracking loop autocorrelation power detection amount of a single satellite; performs sliding average filtering on the tracking loop autocorrelation detection amount to generate a TLAP-MA detection amount; combines the TLAP-MA detection amounts of multiple satellites to construct a multi-satellite joint matrix, and performs dynamic classification and judgment based on a preset judgment threshold; distinguishes multipath interference from deception attacks based on the dynamic classification and judgment results, and outputs the detection results. The technical effect of effectively detecting and distinguishing GNSS deception interference and multipath interference is achieved.

[0061] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present application. In practical applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0062] In addition, for technical details not described in detail in this embodiment, please refer to the GNSS spoofing interference detection method provided in any embodiment of the present application, which will not be repeated here.

[0063] In addition, it should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0064] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0065] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0066] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also 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, multipath interference and deception attack are distinguished, and the detection result is output.

2. The method according to claim 1, characterized in that 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: 。 3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. A GNSS spoofing interference detection system, characterized in that: 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.

9. 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 7.

10. 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 7.

Images