Eigenvector-based GNSS spoofing protection method and apparatus

Abstract

Disclosed in the present invention are an eigenvector-based GNSS spoofing protection method and apparatus. The method comprises: acquiring a GNSS signal to be detected collected by a correlator; calculating an eigenvalue matrix and an eigenvector corresponding to the GNSS signal to be detected; substituting the eigenvalue matrix and the eigenvector into a detection formula for determination; if the detection formula is not established, the GNSS signal to be detected being a normal signal; and if the detection formula is established, the GNSS signal to be detected being a spoofing signal. In the present invention, the eigenvalue matrix and the eigenvector of the GNSS signal are calculated to obtain the features of a GNSS, and then spoofing signal determination is performed on the GNSS signal on the basis of the obtained features, which is beneficial for a user to perform security maintenance on the basis of the recognized spoofing signal, improving the security of the GNSS.

Description

A GNSS spoofing prevention method and device based on feature vectors Technical Field

[0001] This invention relates to the field of global satellite navigation technology, and in particular to a GNSS deception protection method and device based on feature vectors. Background Technology

[0002] Global Navigation Satellite System (GNSS) performs exceptionally well in transportation, agricultural activities, timing and positioning services, and has become an indispensable part of people's daily lives. However, the unencrypted and fully open nature of GNSS services (GPS, GALILEO, GLONASS, BEIDOU) makes them vulnerable to spoofing by external interference sources, causing receivers to unknowingly use incorrect navigation data and resulting in loss of life and property. Furthermore, with advancements in digital signal processing and software-defined radio type spoofing devices, the risk of GNSS signal deception is constantly increasing. Current technology lacks protective measures against GNSS spoofing, resulting in low security for GNSS systems.

[0003] Therefore, there is an urgent need for a GNSS deception protection strategy to solve the problem of low security in GNSS systems. Summary of the Invention

[0004] This invention provides a GNSS spoofing protection method and apparatus based on feature vectors to improve the security of GNSS systems.

[0005] To address the aforementioned problems, one embodiment of the present invention provides a GNSS spoofing protection method based on feature vectors, comprising:

[0006] Acquire the GNSS signal to be measured, which is collected by the correlator;

[0007] Calculate the eigenvalue matrix and eigenvector corresponding to the GNSS signal under test;

[0008] Substitute the eigenvalue matrix and eigenvectors into the detection formula for judgment;

[0009] If the detection formula is not valid, then the GNSS signal to be tested is a normal signal;

[0010] If the detection formula holds true, then the GNSS signal under test is a deception signal.

[0011] As an improvement to the above scheme, the detection formula satisfies the following condition:

[0012] In the formula, Λ is the eigenvalue matrix. Let θ be the eigenvector, Θ be the standard deviation matrix, and I be the eigenvector. err Let K be the deviation matrix. fa K is the false alarm coefficient. md This is the false alarm rate. It is the pseudo-inverse of the matrix; wherein, the deviation matrix is ​​the difference between the GNSS signal to be measured and the preset first sample GNSS signal.

[0013] As an improvement to the above solution, the acquisition of the detection formula includes:

[0014] Acquire the first GNSS sample signal marked as a normal signal collected by the correlator;

[0015] A deception signal is added to the first GNSS sample signal to obtain a second GNSS sample signal marked as an anomalous signal;

[0016] Based on the first GNSS sample signal and the second GNSS sample signal, a first expression is established; wherein, the first expression is: I ewf α≥I ref α+|f(σ,α)|

[0017] In the formula, I ref The first sample signal of GNSS, I ewf Let σ be the second GNSS sample signal, σ be the standard deviation between the first and second sample signals, α be the feature vector to be determined, and f(σ,α) be the difference function caused by noise and monitoring indicators.

[0018] The first expression is rewritten to obtain the second expression; wherein the second expression is: I ewf α≥I ref α+|σ(K fa +K md )|·λα

[0019] In the formula, K fa K is the false alarm coefficient. md λ is the false alarm factor, where λ is a constant greater than 0;

[0020] The second expression is used to determine the detection expression for a single correlator, and the detection formula is determined based on the detection expression.

[0021] As an improvement to the above scheme, determining the detection formula based on the detection expression includes:

[0022] The first GNSS sample signal is converted into the first output matrix of the multiple correlator, and the second GNSS sample signal is converted into the second output matrix of the multiple correlator.

[0023] Based on the first output matrix and the second output matrix of the multiple correlator, the detection expression is expanded in matrix form to obtain the detection matrix; wherein, the detection matrix is:

[0024] In the formula, I ewf' I is the first output matrix of the multiple correlator. ref' Let Θ be the second output matrix of the multiple correlator, Θ be the standard deviation matrix, and Λ' be the eigenvalue matrix corresponding to the difference between the first and second output matrices of the multiple correlator. This is the eigenvector corresponding to the difference between the first and second output matrices of the multiple correlator;

[0025] Multiply by [Θ(K) on both sides of the detection matrix fa +K md The pseudo-inverse of the detection matrix is ​​obtained by left-multiplying the pseudo-inverse, and the false alarm coefficient and the missed alarm coefficient are calculated.

[0026] The detection formula is determined based on the false alarm coefficient and the missed alarm coefficient.

[0027] As an improvement to the above scheme, the acquisition of the GNSS signal to be measured collected by the correlator includes:

[0028] Receive the collected data transmitted by the correlator;

[0029] The GNSS normalized correlation peak data in the collected data are extracted to obtain the GNSS signal to be measured.

[0030] Accordingly, one embodiment of the present invention also provides a GNSS spoofing protection device based on feature vectors, including: a data acquisition module, a data calculation module, a data judgment module, a normal signal module, and a spoofing signal module;

[0031] The data acquisition module is used to acquire the GNSS signal to be measured collected by the correlator;

[0032] The data calculation module is used to calculate the eigenvalue matrix and eigenvector corresponding to the GNSS signal under test;

[0033] The data judgment module is used to substitute the feature value matrix and feature vector into the detection formula for judgment;

[0034] The normal signal module is used to determine if the GNSS signal under test is a normal signal if the detection formula does not hold true.

[0035] The deception signal module is used to determine if the GNSS signal under test is a deception signal if the detection formula is true.

[0036] As an improvement to the above scheme, the detection formula satisfies the following condition:

[0037] In the formula, Λ is the eigenvalue matrix. Let θ be the eigenvector, Θ be the standard deviation matrix, and I be the eigenvector. err Let K be the deviation matrix. fa K is the false alarm coefficient. md This is the false alarm rate. It is the pseudo-inverse of the matrix; wherein, the deviation matrix is ​​the difference between the GNSS signal to be measured and the preset first sample GNSS signal.

[0038] As an improvement to the above solution, the acquisition of the detection formula includes:

[0039] Acquire the first GNSS sample signal marked as a normal signal collected by the correlator;

[0040] A deception signal is added to the first GNSS sample signal to obtain a second GNSS sample signal marked as an anomalous signal;

[0041] Based on the first GNSS sample signal and the second GNSS sample signal, a first expression is established; wherein, the first expression is: I ewf α≥I ref α+|f(σ,α)|

[0042] In the formula, I ref The first sample signal of GNSS, I ewf Let σ be the second GNSS sample signal, σ be the standard deviation between the first and second sample signals, α be the feature vector to be determined, and f(σ,α) be the difference function caused by noise and monitoring indicators.

[0043] The first expression is rewritten to obtain the second expression; wherein the second expression is: I ewf α≥I ref α+|σ(K fa +K md )|·λα

[0044] In the formula, K fa K is the false alarm coefficient. md λ is the false alarm factor, where λ is a constant greater than 0;

[0045] The second expression is used to determine the detection expression for a single correlator, and the detection formula is determined based on the detection expression.

[0046] As an improvement to the above scheme, determining the detection formula based on the detection expression includes:

[0047] The first GNSS sample signal is converted into the first output matrix of the multiple correlator, and the second GNSS sample signal is converted into the second output matrix of the multiple correlator.

[0048] Based on the first output matrix and the second output matrix of the multiple correlator, the detection expression is expanded in matrix form to obtain the detection matrix; wherein, the detection matrix is:

[0049] In the formula, I ewf' I is the first output matrix of the multiple correlator. ref' Let Θ be the second output matrix of the multiple correlator, Θ be the standard deviation matrix, and Λ' be the eigenvalue matrix corresponding to the difference between the first and second output matrices of the multiple correlator. This is the eigenvector corresponding to the difference between the first and second output matrices of the multiple correlator;

[0050] Multiply by [Θ(K) on both sides of the detection matrix fa +K md The pseudo-inverse of the detection matrix is ​​obtained by left-multiplying the pseudo-inverse, and the false alarm coefficient and the missed alarm coefficient are calculated.

[0051] The detection formula is determined based on the false alarm coefficient and the missed alarm coefficient.

[0052] As an improvement to the above solution, the data acquisition module includes: a receiving unit and an extraction unit;

[0053] The receiving unit is used to receive the collected data transmitted by the correlator;

[0054] The extraction unit is used to extract the GNSS normalized correlation peak data from the collected data to obtain the GNSS signal to be measured.

[0055] As can be seen from the above, the present invention has the following beneficial effects:

[0056] This invention provides a GNSS spoofing protection method based on eigenvectors. The method involves acquiring a GNSS signal to be tested, collected by a correlator; calculating the eigenvalue matrix and eigenvectors corresponding to the GNSS signal; substituting the eigenvalue matrix and eigenvectors into a detection formula for judgment; if the detection formula does not hold, the GNSS signal is considered normal; if the detection formula holds, the GNSS signal is considered spoofed. This invention obtains GNSS characteristics by calculating the eigenvalue matrix and eigenvectors of the GNSS signal, and then uses these characteristics to determine if the GNSS signal is spoofed. This facilitates security maintenance by users based on identified spoofed signals, thus improving the security of the GNSS system. Attached Figure Description

[0057] Figure 1 is a flowchart illustrating a GNSS spoofing protection method based on feature vectors provided in an embodiment of the present invention;

[0058] Figure 2 is a schematic diagram of the structure of a GNSS deception protection device based on feature vectors provided in an embodiment of the present invention;

[0059] Figure 3 is a schematic diagram of a deception signal provided in an embodiment of the present invention;

[0060] Figure 4 is a schematic diagram of deception signal detection provided by an embodiment of the present invention;

[0061] Figure 5 is a schematic diagram of the detection results of different deception signals provided in an embodiment of the present invention;

[0062] Figure 6 is a schematic diagram of a terminal device structure provided in an embodiment of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Example 1

[0065] Referring to Figure 1, which is a flowchart illustrating a GNSS spoofing prevention method based on feature vectors according to an embodiment of the present invention, this embodiment includes steps 101 to 105, and the specific steps are as follows:

[0066] Step 101: Acquire the GNSS signal to be measured by the correlator.

[0067] In this embodiment, acquiring the GNSS signal to be measured collected by the correlator includes:

[0068] Receive the collected data transmitted by the correlator;

[0069] The GNSS normalized correlation peak data in the collected data are extracted to obtain the GNSS signal to be measured.

[0070] In one specific embodiment, the correlation peak data collected by multiple correlators are arranged into a matrix, which is then used as the GNSS signal to be measured.

[0071] In this embodiment, the pull-type spoofing attack inevitably causes synchronous distortion of the correlation peak. As can be seen from the slowly pulled code phase in Figure 3, during the pull-type spoofing loop takeover process, the correlator calculation result is simultaneously affected by both the true and false correlation peaks. Therefore, from an overall perspective, the signal correlation peak in the code ring undergoes shape distortion. At the same time, the EPL correlator output no longer satisfies the symmetry relationship under normal conditions. Therefore, the correlation parameters of the correlation peaks can be used to determine whether the signal has been spoofed. This method can be used to determine this in any scenario subject to a pull-type spoofing attack.

[0072] Step 102: Calculate the eigenvalue matrix and eigenvector corresponding to the GNSS signal to be measured.

[0073] In one specific embodiment, after listing the correlation peak data collected by multiple correlators into a matrix, the eigenvalue matrix and eigenvector of the matrix are calculated; the calculation steps satisfy the following conditions:

[0074] Let matrix A be n×n The total eigenvalues ​​of the matrix described in this invention (i.e., the correlation peak data collected by the multiple correlators described in this invention) are λ. i (i = 1, 2, ..., n), with identity matrix I, then by the formula |λI - A| = 0

[0075] All eigenvalues ​​λ of A can be found. Then, using the system of equations (λ... i IA)x=0

[0076] λ can be calculated i The corresponding feature vector.

[0077] Step 103: Substitute the eigenvalue matrix and eigenvector into the detection formula for judgment.

[0078] In this embodiment, the detection formula satisfies the following conditions:

[0079] In the formula, Λ is the eigenvalue matrix. Let θ be the eigenvector, Θ be the standard deviation matrix, and I be the eigenvector. err Let K be the deviation matrix. fa K is the false alarm coefficient. md This is the false alarm rate. It is the pseudo-inverse of the matrix; wherein, the deviation matrix is ​​the difference between the GNSS signal to be measured and the preset first sample GNSS signal.

[0080] In one specific embodiment, K fa False alarm coefficient and K md The false alarm rate is set according to the actual detection requirements, and the standard deviation matrix is ​​also set in advance.

[0081] In this embodiment, obtaining the detection formula includes:

[0082] Acquire the first GNSS sample signal marked as a normal signal collected by the correlator;

[0083] A deception signal is added to the first GNSS sample signal to obtain a second GNSS sample signal marked as an anomalous signal;

[0084] Based on the first GNSS sample signal and the second GNSS sample signal, a first expression is established; wherein, the first expression is: I ewf α≥I ref α+|f(σ,α)|

[0085] In the formula, I ref The first sample signal of GNSS, I ewf Let σ be the second GNSS sample signal, σ be the standard deviation between the first and second sample signals, α be the feature vector to be determined, and f(σ,α) be the difference function caused by noise and monitoring indicators.

[0086] The first expression is rewritten to obtain the second expression; wherein the second expression is: I ewf α≥I ref α+|σ(K fa +K md )|·λα

[0087] In the formula, K fa K is the false alarm coefficient. md λ is the false alarm factor, where λ is a constant greater than 0;

[0088] The second expression is used to determine the detection expression for a single correlator, and the detection formula is determined based on the detection expression.

[0089] In one specific embodiment, the minimum detectable error defines a minimum detection threshold, which, while tolerating noise characteristics, also needs to satisfy a corresponding false alarm rate P. fa and false alarm rate P md For false alarms and missed alarms caused by multipath effects, satisfying P fa and P md The minimum detectable error threshold can be obtained by multiplying σ by the corresponding coefficient K. fa and K md This is expressed as follows. For thermal noise in a Gaussian-distributed noise model, its effect on α can be modeled as λα, where λ is a constant greater than 0. Therefore, |f(σ,α)| can be transformed into |σ(K... fa +K md )|·λα.

[0090] In one specific embodiment, f(σ,α) determines the difference between the first GNSS sample signal and the second GNSS sample signal. It is understood that since the second sample signal adds a spoofing signal, f(σ,α) is a value caused by noise and monitoring indicators.

[0091] In this embodiment, determining the detection formula based on the detection expression includes:

[0092] The first GNSS sample signal is converted into the first output matrix of the multiple correlator, and the second GNSS sample signal is converted into the second output matrix of the multiple correlator.

[0093] Based on the first output matrix and the second output matrix of the multiple correlator, the detection expression is expanded in matrix form to obtain the detection matrix; wherein, the detection matrix is:

[0094] In the formula, I ewf' I is the first output matrix of the multiple correlator. ref' Let Θ be the second output matrix of the multiple correlator, Θ be the standard deviation matrix, and Λ' be the eigenvalue matrix corresponding to the difference between the first and second output matrices of the multiple correlator. This is the eigenvector corresponding to the difference between the first and second output matrices of the multiple correlator;

[0095] Multiply by [Θ(K) on both sides of the detection matrix fa +K md The pseudo-inverse of the detection matrix is ​​obtained by left-multiplying the pseudo-inverse, and the false alarm coefficient and the missed alarm coefficient are calculated.

[0096] The detection formula is determined based on the false alarm coefficient and the missed alarm coefficient.

[0097] In one specific embodiment, under certain circumstances, The deceptive information contained within may be difficult to detect; for example, all standard deviations may be very small, leading to false negatives. To amplify the characteristics of the deceptive signal, the matrix from which the eigenvectors need to be calculated can be optimized. The following three methods can be used:

[0098] The three optimization methods mentioned above are multiplying by the standard signal matrix (corresponding to optimization method 1), the distorted signal matrix (corresponding to optimization method 2), and the error signal matrix (corresponding to optimization method 3). Since the latter two multiply by matrices related to distortion information, they amplify the detection sensitivity for distorted signals, thus achieving higher detection accuracy. As shown in the test results (Table 1), the latter two methods have comparable detection accuracy, outperforming the first method and the unoptimized method.

[0099] Table 1

[0100] Step 104: If the detection formula is not valid, then the GNSS signal to be tested is a normal signal.

[0101] Step 105: If the detection formula is valid, then the GNSS signal to be tested is a deception signal.

[0102] For a better illustration, see the following example:

[0103] First, let's take two examples to illustrate the judgment criteria of this method. Two correlation peaks from the TMA and TMB distortion models are selected, and the correlation peaks of two normal signals are used to calculate the threshold. The result obtained from the eigenvector method is shown in Figure 4. Orange dots represent distorted signals, blue dots represent standard signals, and three lines parallel to the horizontal axis represent, from top to bottom, the highest detection threshold, the baseline corresponding to the standard signal (calculated as the dot product of the correlation matrix and the eigenvector), and the lowest detection threshold (the threshold value is set according to your needs). When the dot product of the measured data and the calculated eigenvector falls between the two parallel lines, the signal is considered normal; otherwise, distortion is considered to have occurred. Analysis of Figure 4 shows that when a signal is distorted, the dot product of its correlation matrix and eigenvector falls outside the threshold, i.e., it is greater than the highest detection threshold or less than the lowest detection threshold.

[0104] Based on the algorithm in this embodiment, a dataset was selected for testing. The dataset contains 3000 normalized correlation peak data points of standard signals, 3000 TMA distortion data points (Threat modeal A, or TMA for short), 6000 TMB distortion data points (Threat modeal B, or TMB for short), and 6000 TMC distortion data points (Threat modeal C, or TMC for short). The detection results are shown in Figure 5. The classification accuracy rates for the three types of distorted signals are 81.2%, 80.3%, and 83.8%, respectively.

[0105] In this embodiment,

[0106] Referring to Figure 2, which is a schematic diagram of the structure of a GNSS deception protection device based on feature vectors according to an embodiment of the present invention, the device includes: a data acquisition module 201, a data calculation module 202, a data judgment module 203, a normal signal module 204, and a deception signal module 205.

[0107] The data acquisition module is used to acquire the GNSS signal to be measured collected by the correlator;

[0108] The data calculation module is used to calculate the eigenvalue matrix and eigenvector corresponding to the GNSS signal under test;

[0109] The data judgment module is used to substitute the feature value matrix and feature vector into the detection formula for judgment;

[0110] The normal signal module is used to determine if the GNSS signal under test is a normal signal if the detection formula does not hold true.

[0111] The deception signal module is configured to determine if the GNSS signal under test is a deception signal if the detection formula is true.

[0112] As an improvement to the above scheme, the detection formula includes:

[0113] In the formula, Λ is the eigenvalue matrix. Let θ be the eigenvector, Θ be the standard deviation matrix, and I be the eigenvector. err Let K be the deviation matrix. fa K is the false alarm coefficient. md This is the false alarm rate. It is the pseudo-inverse of the matrix; wherein, the deviation matrix is ​​the difference between the GNSS signal to be measured and the preset first sample GNSS signal.

[0114] As an improvement to the above solution, the acquisition of the detection formula includes:

[0115] Acquire the first GNSS sample signal marked as a normal signal collected by the correlator;

[0116] A deception signal is added to the first GNSS sample signal to obtain a second GNSS sample signal marked as an anomalous signal;

[0117] Based on the first GNSS sample signal and the second GNSS sample signal, a first expression is established; wherein, the first expression is: I ewf α≥I ref α+|f(σ,α)|

[0118] In the formula, I ref The first sample signal of GNSS, I ewf Let σ be the second GNSS sample signal, σ be the standard deviation between the first and second sample signals, α be the feature vector to be determined, and f(σ,α) be the difference function caused by noise and monitoring indicators.

[0119] The first expression is rewritten to obtain the second expression; wherein the second expression is: I ewf α≥I refα+|σ(K fa +K md )|·λα

[0120] In the formula, K fa K is the false alarm coefficient. md λ is the false alarm factor, where λ is a constant greater than 0;

[0121] The second expression is used to determine the detection expression for a single correlator, and the detection formula is determined based on the detection expression.

[0122] As an improvement to the above scheme, determining the detection formula based on the detection expression includes:

[0123] The first GNSS sample signal is converted into the first output matrix of the multiple correlator, and the second GNSS sample signal is converted into the second output matrix of the multiple correlator.

[0124] Based on the first output matrix and the second output matrix of the multiple correlator, the detection expression is expanded in matrix form to obtain the detection matrix; wherein, the detection matrix is:

[0125] In the formula, I ewf' I is the first output matrix of the multiple correlator. ref' Let Θ be the second output matrix of the multiple correlator, Θ be the standard deviation matrix, and Λ' be the eigenvalue matrix corresponding to the difference between the first and second output matrices of the multiple correlator. This is the eigenvector corresponding to the difference between the first and second output matrices of the multiple correlator;

[0126] Multiply by [Θ(K) on both sides of the detection matrix fa +K md The pseudo-inverse of the detection matrix is ​​obtained by left-multiplying the pseudo-inverse, and the false alarm coefficient and the missed alarm coefficient are calculated.

[0127] The detection formula is determined based on the false alarm coefficient and the missed alarm coefficient.

[0128] As an improvement to the above solution, the data acquisition module includes: a receiving unit and an extraction unit;

[0129] The receiving unit is used to receive the collected data transmitted by the correlator;

[0130] The extraction unit is used to extract the GNSS normalized correlation peak data from the collected data to obtain the GNSS signal to be measured.

[0131] This embodiment acquires the GNSS signal to be tested collected by a correlator; substitutes the GNSS signal to be tested into a preset detection formula to calculate the eigenvalue matrix and eigenvector corresponding to the GNSS signal; calculates the dot product between the eigenvalue matrix and the eigenvector to obtain the detection result of the GNSS signal to be tested; and determines whether the detection result is between the preset highest and lowest detection thresholds. If yes, the GNSS signal to be tested is a normal signal; otherwise, the GNSS signal to be tested is a spoofing signal. This invention obtains the characteristics of GNSS by calculating the eigenvalue matrix and eigenvector of the GNSS signal, and then judges the GNSS signal as a spoofing signal based on the preset detection threshold. This is beneficial for users to perform security maintenance based on the identified spoofing signals, thus improving the security of the GNSS system.

[0132] Example 2

[0133] Referring to Figure 6, Figure 6 is a schematic diagram of the terminal device structure provided in an embodiment of the present invention.

[0134] One terminal device in this embodiment includes a processor 601, a memory 602, and a computer program stored in the memory 602 and executable on the processor 601. When the processor 601 executes the computer program, it implements the steps of the various feature vector-based GNSS spoofing protection methods described above in the embodiments, such as all the steps of the feature vector-based GNSS spoofing protection method shown in FIG. 1. Alternatively, when the processor executes the computer program, it implements the functions of each module in the various device embodiments described above, such as all the modules of the feature vector-based GNSS spoofing protection device shown in FIG. 2.

[0135] In addition, embodiments of the present invention also provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the GNSS spoofing protection method based on feature vectors as described in any of the above embodiments.

[0136] Those skilled in the art will understand that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the diagram, or combine certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0137] The processor 601 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor 601 is the control center of the terminal device, connecting various parts of the terminal device through various interfaces and lines.

[0138] The memory 602 can be used to store the computer programs and / or modules. The processor 601 implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0139] Wherein, if the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0140] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0141] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A GNSS spoofing prevention method based on feature vectors, characterized in that, include: Acquire the GNSS signal to be measured, which is collected by the correlator; Calculate the eigenvalue matrix and eigenvector corresponding to the GNSS signal under test; Substitute the eigenvalue matrix and eigenvectors into the detection formula for judgment; If the detection formula is not valid, then the GNSS signal to be tested is a normal signal; If the detection formula holds true, then the GNSS signal under test is a deception signal.

2. The GNSS spoofing prevention method based on feature vectors according to claim 1, characterized in that, The detection formula satisfies the following conditions: In the formula, Λ is the eigenvalue matrix. Let θ be the eigenvector, Θ be the standard deviation matrix, and I be the eigenvector. err Let K be the deviation matrix. fa K is the false alarm coefficient. md This is the false alarm rate. It is the pseudo-inverse of the matrix; wherein, the deviation matrix is ​​the difference between the GNSS signal to be measured and the preset first sample GNSS signal.

3. The GNSS spoofing prevention method based on feature vectors according to claim 2, characterized in that, The acquisition of the detection formula includes: Acquire the first GNSS sample signal marked as a normal signal collected by the correlator; A deception signal is added to the first GNSS sample signal to obtain a second GNSS sample signal marked as an anomalous signal; Based on the first GNSS sample signal and the second GNSS sample signal, a first expression is established; wherein, the first expression is: I ewf a≥I ref α+|f(σ,α)| In the formula, I ref The first sample signal of GNSS, I ewf Let σ be the second GNSS sample signal, σ be the standard deviation between the first and second sample signals, α be the feature vector to be determined, and f(σ,α) be the difference function caused by noise and monitoring indicators. The first expression is rewritten to obtain the second expression; wherein the second expression is: I ewf a≥I ref α+|σ(K fa +K md )|·la In the formula, K fa K is the false alarm coefficient. md λ is the false alarm factor, where λ is a constant greater than 0; The second expression is used to determine the detection expression for a single correlator, and the detection formula is determined based on the detection expression.

4. The GNSS spoofing prevention method based on feature vectors according to claim 3, characterized in that, Determining the detection formula based on the detection expression includes: The first GNSS sample signal is converted into the first output matrix of the multiple correlator, and the second GNSS sample signal is converted into the second output matrix of the multiple correlator. Based on the first output matrix and the second output matrix of the multiple correlator, the detection expression is expanded in matrix form to obtain the detection matrix; wherein, the detection matrix is: In the formula, I ewf' I is the first output matrix of the multiple correlator. ref' Let Θ be the second output matrix of the multiple correlator, Θ be the standard deviation matrix, and Λ' be the eigenvalue matrix corresponding to the difference between the first and second output matrices of the multiple correlator. This is the eigenvector corresponding to the difference between the first and second output matrices of the multiple correlator; Multiply by [Θ(K) on both sides of the detection matrix fa +K md The pseudo-inverse of the detection matrix is ​​obtained by left-multiplying the pseudo-inverse, and the false alarm coefficient and the missed alarm coefficient are calculated. The detection formula is determined based on the false alarm coefficient and the missed alarm coefficient.

5. The GNSS spoofing prevention method based on feature vectors according to claim 4, characterized in that, The acquisition of the GNSS signal to be measured, collected by the correlator, includes: Receive the collected data transmitted by the correlator; The GNSS normalized correlation peak data in the collected data are extracted to obtain the GNSS signal to be measured.

6. A GNSS spoofing protection device based on feature vectors, characterized in that, include: Data acquisition module, data calculation module, data judgment module, normal signal module, and deceptive signal module; The data acquisition module is used to acquire the GNSS signal to be measured collected by the correlator; The data calculation module is used to calculate the eigenvalue matrix and eigenvector corresponding to the GNSS signal under test; The data judgment module is used to substitute the feature value matrix and feature vector into the detection formula for judgment; The normal signal module is used to determine if the GNSS signal under test is a normal signal if the detection formula does not hold true. The deception signal module is used to determine if the GNSS signal under test is a deception signal if the detection formula is true.

7. The GNSS spoofing protection device based on feature vectors according to claim 6, characterized in that, The detection formula satisfies the following conditions: In the formula, Λ is the eigenvalue matrix. Let θ be the eigenvector, Θ be the standard deviation matrix, and I be the eigenvector. err Let K be the deviation matrix. fa K is the false alarm coefficient. md This is the false alarm rate. It is the pseudo-inverse of the matrix; wherein, the deviation matrix is ​​the difference between the GNSS signal to be measured and the preset first sample GNSS signal.

8. The GNSS spoofing protection device based on feature vectors according to claim 7, characterized in that, The acquisition of the detection formula includes: Acquire the first GNSS sample signal marked as a normal signal collected by the correlator; A deception signal is added to the first GNSS sample signal to obtain a second GNSS sample signal marked as an anomalous signal; Based on the first GNSS sample signal and the second GNSS sample signal, a first expression is established; wherein, the first expression is: I ewf a≥I ref α+|f(σ,α)| In the formula, I ref The first sample signal of GNSS, I ewf Let σ be the second GNSS sample signal, σ be the standard deviation between the first and second sample signals, α be the feature vector to be determined, and f(σ,α) be the difference function caused by noise and monitoring indicators. The first expression is rewritten to obtain the second expression; wherein the second expression is: I ewf a≥I ref α+|σ(K fa +K md )|·la In the formula, K fa K is the false alarm coefficient. md λ is the false alarm factor, where λ is a constant greater than 0; The second expression is used to determine the detection expression for a single correlator, and the detection formula is determined based on the detection expression.

9. The GNSS spoofing protection device based on feature vectors according to claim 8, characterized in that, Determining the detection formula based on the detection expression includes: The first GNSS sample signal is converted into the first output matrix of the multiple correlator, and the second GNSS sample signal is converted into the second output matrix of the multiple correlator. Based on the first output matrix and the second output matrix of the multiple correlator, the detection expression is expanded in matrix form to obtain the detection matrix; wherein, the detection matrix is: In the formula, I ewf' I is the first output matrix of the multiple correlator. ref' Let Θ be the second output matrix of the multiple correlator, Θ be the standard deviation matrix, and Λ' be the eigenvalue matrix corresponding to the difference between the first and second output matrices of the multiple correlator. This is the eigenvector corresponding to the difference between the first and second output matrices of the multiple correlator; Multiply by [Θ(K) on both sides of the detection matrix fa +K md The pseudo-inverse of the detection matrix is ​​obtained by left-multiplying the pseudo-inverse, and the false alarm coefficient and the missed alarm coefficient are calculated. The detection formula is determined based on the false alarm coefficient and the missed alarm coefficient.

10. The GNSS spoofing prevention method based on feature vectors according to claim 9, characterized in that, The data acquisition module includes: a receiving unit and an extraction unit; The receiving unit is used to receive the collected data transmitted by the correlator; The extraction unit is used to extract the GNSS normalized correlation peak data from the collected data to obtain the GNSS signal to be measured.

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