Spoofing jamming detection method based on dynamic change of spatial processing gain

Abstract

The application relates to a spoofing jamming detection method based on dynamic change of space domain processing gain. The method comprises the following steps: receiving and processing satellite navigation signals by using an array antenna, and estimating a space domain autocorrelation matrix of the signals; then, a virtual jamming with periodically changed direction is actively constructed, and an array weighting vector dynamically changing with time is calculated by combining the matrix; then, the vector is used for space domain filtering of the signals, independent tracking of the navigation signals is realized, and a carrier-to-noise ratio time sequence of each navigation signal is estimated; then, a correlation coefficient of the carrier-to-noise ratio sequences of any two signals is calculated, if the correlation coefficient exceeds a set threshold, it is determined that the two signals come from the same direction due to the same space domain gain modulation, and the two signals are classified as spoofing signals; finally, the identified spoofing signals are removed from positioning solution, the navigation result is updated, and an alarm is triggered, so that robust spoofing detection in a strong jamming environment is realized.

Description

Technical Field

[0001] This application relates to the field of satellite navigation anti-interference technology, and in particular to a deception interference detection method based on dynamic changes in spatial domain processing gain. Background Technology

[0002] Global Navigation Satellite Systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou are highly susceptible to human interference due to their openness and weak signal strength. Spoofing interference, in particular, involves broadcasting false signals that are highly similar to genuine navigation signals in carrier frequency, pseudo-random code structure, and data modulation methods. This induces receivers to lock onto incorrect signals, resulting in the output of erroneous position, velocity, or time information, making it highly covert and dangerous. In typical attack scenarios, multiple spoofing signals are synchronously generated by the same physical spoofing source and broadcast from the same spatial direction, but assigned different pseudo-random codes to simulate multiple "fake satellites." Existing methods for detecting navigation spoofing interference mainly utilize the redundancy of visible satellites (typically ≥5) and use geometric consistency checks (such as residual chi-square tests) to determine the presence of abnormal signals. If a signal causes the positioning solution to deviate significantly from other combined solutions, it is marked as an anomaly. Based on the assumption that the power of spoofing interference is usually greater than that of genuine satellite signals, spoofing detection is achieved by monitoring changes in the absolute or relative power of the received signal. Some spoofing techniques (such as synchronous relay spoofing) can cause correlation peak distortion, broadening, or double peaks. Spoofing detection is performed by analyzing the shape characteristics of the correlation function between the local pseudocode and the received signal. The direction of arrival (DOA) of each signal is estimated using the antenna array. The DOA of the real satellites located in different azimuths should satisfy celestial geometric constraints; if multiple signals have highly consistent DOAs, they are considered to be from the same source.

[0003] The above methods are effective to some extent in clean channels or in scenarios with only spoofing interference. However, in real-world environments, suppression interference often coexists with spoofing interference. The presence of suppression interference can severely affect signal quality, leading to a serious deterioration in the carrier-to-noise ratio of the received signal, inaccurate parameter estimation, and a reduction in the number of available signals. These factors cause the performance of most existing spoofing detection mechanisms to drop sharply or even fail completely.

[0004] Meanwhile, to suppress interference, existing anti-jamming receivers generally employ adaptive null array antenna technology, which creates nulls in the direction of interference through spatial filtering. However, current technologies only use the array for interference suppression and have not effectively explored its potential in spoofing detection. In particular, there is a lack of a joint processing mechanism that can robustly detect multiple spoofing signals from the same source while successfully suppressing interference. Summary of the Invention

[0005] Therefore, it is necessary to provide a deception interference detection method based on dynamic changes in spatial domain processing gain, which can robustly detect multiple deception signals from the same source while successfully suppressing and blocking interference.

[0006] A deception interference detection method based on dynamic changes in spatial domain processing gain, the method comprising:

[0007] The satellite navigation radio frequency signal is acquired, and down-converted and analog-to-digital converted to obtain the baseband digital signal vector. Based on the baseband digital signal vector, the spatial autocorrelation matrix of the satellite navigation radio frequency signal is estimated within a preset observation window.

[0008] A virtual disturbance with a periodically changing incident direction is constructed, and the array weighting vector that dynamically changes with time is calculated based on the spatial autocorrelation matrix and the parameters of the virtual disturbance.

[0009] The received signal is spatially filtered using a dynamically changing array weighting vector, and each navigation signal is tracked independently. The carrier-to-noise ratio sequence of each tracked navigation signal is estimated within the observation time window.

[0010] Calculate the correlation coefficient between the carrier-to-noise ratio sequences of any two tracked navigation signals; set a detection threshold, and if the correlation coefficient between the carrier-to-noise ratio sequences of the two navigation signals is higher than the threshold, then determine that the two are subject to the same spatial gain modulation and classify them as deception interference signals;

[0011] Navigation signals deemed as deception or interference will be excluded from the positioning calculation, the navigation calculation results will be updated, and a safety alarm will be triggered.

[0012] The aforementioned deception interference detection method based on dynamic changes in spatial domain processing gain first receives the signal through an array antenna and performs down-conversion and analog-to-digital conversion. Then, it estimates the spatial autocorrelation matrix based on the baseband digital signal vector, which is the basis for subsequent spatial domain processing. This method can preserve the spatial characteristics of the real signal, suppressed interference, and deception interference, providing data support for interference suppression and deception detection. It also solves the problem of inaccurate parameter estimation caused by signal quality deterioration in traditional methods. Secondly, a virtual interference with a periodically changing incident direction is constructed. Combined with the spatial autocorrelation matrix, a dynamically changing array weighting vector is calculated. On the one hand, the spatial filtering capability of the array antenna can be used to form nulls in the direction of suppressing interference, effectively suppressing the suppression interference and ensuring the normal tracking of real and deceptive signals. On the other hand, by actively adjusting the array weighting vector, deceptive signals from the same source are subjected to the same spatial gain modulation, and their carrier-to-noise ratio (CNR) sequences exhibit highly similar time-varying patterns. In contrast, real satellite signals, due to their distribution in different azimuths, exhibit different CNR sequence patterns. This design breaks through the limitations of traditional methods that rely on single features such as signal power and geometric consistency. Even in a strong suppression interference environment, it can endow deceptive signals with unique identification features and is not affected by the absolute value of signal power, significantly improving the robustness of deception detection. Then, by calculating the correlation coefficient of the carrier-to-noise ratio sequence and setting a detection threshold for decision-making, it can accurately identify clusters of spoofing signals from the same source, solving the problem of inaccurate estimation in traditional DOA consistency detection under suppressed interference. Simultaneously, it fully reuses the existing array processing architecture, implementing virtual interference injection and correlation analysis only through algorithm modification, without additional hardware investment. This reduces engineering implementation and expansion costs while ensuring detection performance, solving the problems of high hardware complexity and difficulty in widespread adoption associated with traditional joint processing mechanisms. Finally, by excluding spoofing signals from the positioning calculation and triggering a safety alarm, it effectively ensures the positioning integrity of the navigation receiver, completely resolving the core pain point of traditional methods failing to detect and guarantee navigation safety when suppressed interference and spoofing interference coexist. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating a deception interference detection method based on dynamic changes in spatial domain processing gain in one embodiment.

[0014] Figure 2 This is a diagram showing the navigation signal capture result obtained after processing according to this application in one embodiment;

[0015] Figure 3 This is a diagram showing the carrier-to-noise ratio result obtained after processing according to this application in one embodiment. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0017] In one embodiment, such as Figure 1 As shown, a deception interference detection method based on dynamic changes in spatial domain processing gain is provided, including the following steps:

[0018] Step 102: Acquire satellite navigation radio frequency signals, perform down-conversion and analog-to-digital conversion on the satellite navigation radio frequency signals to obtain baseband digital signal vectors; based on the baseband digital signal vectors, estimate the spatial autocorrelation matrix of the satellite navigation radio frequency signals within a preset observation window.

[0019] Satellite navigation radio frequency signals consist of N The array antenna of each element receives the signal. This signal may contain navigation signals from real satellites, jamming signals, and multiple spoofing signals that may originate from the same spoofing source. By down-converting and analog-to-digital converting the received signal, a baseband digital signal vector is obtained, which forms the basis for all subsequent processing.

[0020] Step 104: Construct a virtual disturbance with a periodically changing incident direction, and calculate the array weighting vector that dynamically changes with time based on the spatial autocorrelation matrix and the parameters of the virtual disturbance.

[0021] By actively constructing a virtual interference signal and incorporating its parameters (primarily direction) into the calculation of the adaptive beamformer, the array weighting vector is forced to dynamically adjust as the virtual interference direction changes. This adjustment differentially modulates the spatial gain of signals from different spatial directions.

[0022] Step 106: Spatial filtering of the received signal is performed using a dynamically changing array weighting vector, and each navigation signal is tracked independently. The carrier-to-noise ratio sequence of each tracked navigation signal is estimated within the observation time window.

[0023] By using the time-varying weighted vector calculated in the previous step to perform a weighted summation on the array's received signals, interference can be suppressed while each navigation signal is independently acquired and tracked. Since the weighted vector is time-varying, its gain for signals in different directions is also time-varying, and this change is reflected in the carrier-to-noise ratio (CNR) of the tracked signal. Through continuous monitoring, a sequence of CNR changes over time for each signal can be obtained.

[0024] Step 108: Calculate the correlation coefficient between the carrier-to-noise ratio sequences of any two tracked navigation signals; set a detection threshold. If the correlation coefficient between the carrier-to-noise ratio sequences of the two navigation signals is higher than the threshold, then it is determined that the two signals are subjected to the same spatial gain modulation and are classified as deception interference signals.

[0025] It is understandable that spoofing signals from the same direction, due to being modulated by the same time-varying spatial gain, will exhibit highly similar trends in their carrier-to-noise ratio (CNR) sequences, resulting in high correlation coefficients. In contrast, real satellite signals from different directions have different gain variation patterns, leading to lower correlation in their CNR sequences. By calculating the Pearson correlation coefficients of pairwise CNR sequences and comparing them with a preset threshold, highly correlated signals can be clustered and identified as spoofing signals from the same source.

[0026] Step 110: Remove navigation signals judged as deception interference from the positioning calculation, update the navigation calculation results, and trigger a safety alarm.

[0027] Understandably, after identifying clusters of spoofing signals, the observations of these signals are removed from the navigation and positioning equations, and the remaining real signals are used for calculation to obtain accurate and reliable positioning, velocity measurement, and time synchronization results. Simultaneously, the system should trigger audio, visual, or data interface alarms to alert the user that they are currently under a spoofing interference attack.

[0028] The aforementioned deception interference detection method based on dynamic changes in spatial domain processing gain first receives the signal through an array antenna and performs down-conversion and analog-to-digital conversion. Then, it estimates the spatial autocorrelation matrix based on the baseband digital signal vector, which is the basis for subsequent spatial domain processing. This method can preserve the spatial characteristics of the real signal, suppressed interference, and deception interference, providing data support for interference suppression and deception detection. It also solves the problem of inaccurate parameter estimation caused by signal quality deterioration in traditional methods. Secondly, a virtual interference with a periodically changing incident direction is constructed. Combined with the spatial autocorrelation matrix, a dynamically changing array weighting vector is calculated. On the one hand, the spatial filtering capability of the array antenna can be used to form nulls in the direction of suppressing interference, effectively suppressing the suppression interference and ensuring the normal tracking of real and deceptive signals. On the other hand, by actively adjusting the array weighting vector, deceptive signals from the same source are subjected to the same spatial gain modulation, and their carrier-to-noise ratio (CNR) sequences exhibit highly similar time-varying patterns. In contrast, real satellite signals, due to their distribution in different azimuths, exhibit different CNR sequence patterns. This design breaks through the limitations of traditional methods that rely on single features such as signal power and geometric consistency. Even in a strong suppression interference environment, it can endow deceptive signals with unique identification features and is not affected by the absolute value of signal power, significantly improving the robustness of deception detection. Then, by calculating the correlation coefficient of the carrier-to-noise ratio sequence and setting a detection threshold for decision-making, it can accurately identify clusters of spoofing signals from the same source, solving the problem of inaccurate estimation in traditional DOA consistency detection under suppressed interference. Simultaneously, it fully reuses the existing array processing architecture, implementing virtual interference injection and correlation analysis only through algorithm modification, without additional hardware investment. This reduces engineering implementation and expansion costs while ensuring detection performance, solving the problems of high hardware complexity and difficulty in widespread adoption associated with traditional joint processing mechanisms. Finally, by excluding spoofing signals from the positioning calculation and triggering a safety alarm, it effectively ensures the positioning integrity of the navigation receiver, completely resolving the core pain point of traditional methods failing to detect and guarantee navigation safety when suppressed interference and spoofing interference coexist.

[0029] In one embodiment, the satellite navigation radio frequency signal is down-converted and analog-to-digital converted to obtain a baseband digital signal vector, including:

[0030] By including N The array antenna with each element receives satellite navigation radio frequency signals. The received signals undergo down-conversion and analog-to-digital conversion to obtain the baseband digital signal vector:

[0031] ;

[0032] in, The representation vector is N A complex vector with 1 row and 1 column. Indicates the first N The baseband digital signal vector of each array element.

[0033] Specifically, the array antenna can have any geometric configuration, such as a uniform linear array, circular array, or area array. This step is the starting point of signal processing, converting the spatial analog signal into a baseband signal vector that can be digitally processed.

[0034] In one embodiment, based on the parameters of the spatial autocorrelation matrix and the virtual interference, a time-varying array weighting vector is calculated, including:

[0035] Calculate its steering vector based on the incident direction of the virtual interference;

[0036] Based on the preset virtual interference power, the spatial autocorrelation matrix is ​​corrected to obtain the corrected spatial autocorrelation matrix.

[0037] Based on the corrected spatial correlation matrix and the preset constraint vector, the array weighting vector is calculated.

[0038] Specifically, by injecting virtual interference, the array nulls or gain dips are actively guided to scan spatially, thereby generating the desired time-varying gain characteristics. This transforms the deception detection problem into the identification of spatial gain modulation patterns, improving the method's robustness and environmental adaptability.

[0039] In one embodiment, calculating the steering vector based on the incident direction of the virtual interference includes:

[0040] Construct a virtual jamming signal with its incident direction set in a low elevation angle region (e.g., elevation angle <15°). This design ensures that the virtual jamming mainly affects the low elevation angle region, while the influence on the real navigation satellite signal (which usually appears at medium to high elevation angles) is minimized. The azimuth angle changes periodically over time (e.g., rotating at a constant speed).

[0041] Calculate the steering vector of the virtual interference:

[0042] For including N An array of n elements, assuming the element position vector is... ( n =1,2,…, N The steering vector of the virtual interference signal is referenced to the array's geometric center. The calculation formula is as follows:

[0043] The incident direction of virtual interference is determined by the azimuth angle. and elevation angle The corresponding unit direction vector is determined to be:

[0044] ;

[0045] Then the first n The phase delay of each element relative to the reference point is:

[0046] ;

[0047] in, For satellite navigation radio frequency signal carrier wavelength, For the first n The position vector of each array element relative to the array reference point, superscript T Indicates the transpose operation;

[0048] The steering vector of the virtual interference is then:

[0049] ;

[0050] in, The representation vector is N A complex vector with 1 row and 1 column.

[0051] Specifically, the steering vector precisely describes the phase distribution of the virtual interference signal on each element of the array.

[0052] In one embodiment, the spatial autocorrelation matrix is ​​corrected according to a preset virtual interference power to obtain a corrected spatial autocorrelation matrix, including:

[0053] Based on the preset virtual interference power, the spatial autocorrelation matrix is ​​corrected to obtain the corrected spatial autocorrelation matrix:

[0054] ;

[0055] in, For satellite navigation radio frequency signal carrier wavelength, spatial autocorrelation matrix, The spatial correlation matrix of the virtual interference. The power of the virtual interference. The steering vector for virtual interference. The conjugate transpose of the steering vector representing virtual interference.

[0056] Specifically, by setting the virtual interference power to be much higher than the noise, it can be ensured that it dominates the correlation matrix, thereby effectively driving the dynamic changes of the weighting vector and forming a significant spatial gain modulation effect.

[0057] In one embodiment, based on the corrected spatial correlation matrix and a preset constraint vector, an array weighting vector is calculated, including:

[0058] Based on the corrected spatial correlation matrix and the preset constraint vector, the array weighting vector is calculated as follows:

[0059] ;

[0060] in, This represents the corrected spatial correlation matrix. Represents the constraint vector. The representation vector is N A complex vector with 1 row and 1 column.

[0061] Specifically, this formula is the optimal solution for a linearly constrained minimum variance beamformer, which suppresses interference while ensuring signal gain in the constrained direction.

[0062] In one embodiment, the spatial autocorrelation matrix of the satellite navigation radio frequency signal is estimated within a preset observation window based on the baseband digital signal vector, including:

[0063] Based on the baseband digital signal vector, the spatial autocorrelation matrix of the satellite navigation radio frequency signal is estimated within a preset observation window as follows:

[0064] ;

[0065] in, This indicates the conjugate transpose. The representation vector is N OK N A complex vector of columns, This represents the number of snapshots used to estimate the correlation matrix. Indicates at time t The baseband digital signal vector, Indicates at time t The conjugate transpose of the baseband digital signal vector.

[0066] In one embodiment, estimating the carrier-to-noise ratio sequence for each tracked navigation signal within an observation time window includes:

[0067] Using dynamically changing array weighted vectors The received signal is spatially filtered, and each navigation signal (real satellite signal or spoofing interference) is tracked independently. For the... k The navigation signal being tracked, within the observation time window T The internally estimated carrier-to-noise ratio sequence is as follows:

[0068] ;

[0069] in, L The number of carrier-to-noise ratio sampling points within the observation time window. Indicates the first k The navigation signal at the first L The carrier-to-noise ratio at each sampling time.

[0070] In one embodiment, calculating the correlation coefficient between any two carrier-to-noise ratio sequences of tracked navigation signals includes:

[0071] The correlation coefficient between any two carrier-to-noise ratio sequences of tracked navigation signals is calculated as follows:

[0072] ;

[0073] in, and Signals i and signal j In the l The carrier-to-noise ratio at each sampling time. L This represents the number of carrier-to-noise ratio sampling points within the observation time window.

[0074] Specifically, this coefficient quantitatively characterizes the similarity of the carrier-to-noise ratio variation trends of two signals, providing a clear and reliable mathematical basis for clustering decisions of deceptive signals.

[0075] In one embodiment, the update period of the array weighted vector is less than the change period of the virtual interference azimuth angle.

[0076] Specifically, this is to ensure that the array weighting can be adjusted in a timely manner at each stage of the change in the direction of virtual interference, thereby producing a smooth and effective spatial gain change curve.

[0077] In a specific embodiment, the BeiDou B3I signal is taken as an example. The hardware adopts a 4-element circular array with a radius of half a wavelength. The scenario includes 3 real satellite signals, 1 broadband Gaussian suppression jammer, and 2 deceptive jammers incident from the same direction. The virtual jammer is set at an elevation angle of 10°, and the azimuth angle increases by 20° every 100ms starting from 0°. The array weighting is updated every 1ms, and the carrier-to-noise ratio is sampled at a rate of 10Hz, accumulating to obtain a sequence of 20 sampling points over 2 seconds.

[0078] The implementation process of this invention is described in detail using a B3I signal reception scenario. These embodiments are not intended to limit the scope of protection of this invention. The hardware platform is a 4-element circular array receiver (one element at the center, and the other three elements evenly distributed around the circumference), with a circular array radius of half a wavelength, a receiver sampling rate of 62MHz, and a center frequency of 1268.52MHz.

[0079] Real satellite signals: Three real satellite signals will be broadcast, with satellite numbers (pseudo-random code sequence numbers) of 1, 2, and 3, respectively. The carrier-to-noise ratio of each signal is 45dBHz, and the incident elevation angles are 80°, 70°, and 75°, respectively, and the azimuth angles are 320°, 120°, and 200°, respectively.

[0080] Suppression jamming: Broadcast one suppression jamming signal. The jamming type is broadband Gaussian jamming with a jamming bandwidth of 20MHz, a jamming center frequency of 1268.52MHz, a jamming signal-to-interference ratio of 70dB, a jamming incident elevation angle of 35°, and an azimuth angle of 30°.

[0081] Spoofing jamming: Two spoofing jamming signals were broadcast, with carrier-to-noise ratios of 50dBHz and 53dBHz, respectively, and satellite numbers 6 and 7, respectively. Both spoofing jamming signals were incident from an elevation angle of 25° and an azimuth angle of 120°.

[0082] Virtual interference parameter settings: pitch angle The initial azimuth angle is 0°, increasing by 20° every 100ms, exhibiting periodic changes. The virtual interference power is 10°. Set to 100 times the noise power.

[0083] Array weighted vector update period: updated every 1ms .

[0084] Carrier-to-noise ratio (CNR) sampling: The CNR is recorded at a rate of 10Hz, i.e., once every 100ms, with a cumulative duration of T=2 seconds, meaning the number of CNR sampling points is [number missing]. L= 20.

[0085] Detection threshold: (Determined through Monte Carlo simulation under the condition of false alarm rate <1%)

[0086] Deception criterion: If any two signals in a certain set satisfy... If the signal is false, it will be marked as a deception signal and removed.

[0087] Figure 2 The image shows the navigation signal acquisition results obtained after processing by this application. As can be seen from the image, a total of 5 satellite signals were acquired, with satellite numbers 1, 2, 3, 6 and 7. The maximum correlation peak of satellite numbers 6 and 7 divided by the second largest correlation peak is greater than that of the other 3 satellite signals. This is because the power of the deception interference (i.e., satellite 6 and satellite 7) is stronger than that of the real satellite signals. Figure 2 This demonstrates that the signal processed by the method of this invention can successfully capture real satellite signals even in interference-suppressing scenarios.

[0088] Figure 3This is a chart showing the carrier-to-noise ratio (CNR) results obtained after processing according to this application. As can be seen from the chart, at most sampling times, satellite 7 has the highest CNR, followed by satellite 6, while satellites 1, 2, and 3 have the lowest CNR, which is consistent with the experimental parameter settings. Furthermore, the CNR of satellites 7 and 6 exhibits similar variation patterns, while the CNR of satellites 1, 2, and 3 exhibits different variation patterns. The table below further provides the correlation coefficients of the five navigation signal CNR sequences:

[0089]

[0090] As shown in the table, the carrier-to-noise ratio (CNR) sequence correlation coefficient between satellite 6 and satellite 7 reached 0.98, exceeding the threshold. The CNR sequence correlation coefficients of any other two satellites did not exceed the threshold. Therefore, satellite 6 and satellite 7 were determined to be spoofing interference and were excluded from the positioning calculation. The navigation calculation results were updated, and the corresponding safety alarm mechanism was triggered. These results demonstrate that this application can successfully detect spoofing signals in scenarios where both suppression and spoofing interference coexist, proving the effectiveness of this application.

[0091] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0092] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A deception interference detection method based on dynamic changes in spatial domain processing gain, characterized in that, The method includes: The satellite navigation radio frequency signal is acquired, and the satellite navigation radio frequency signal is down-converted and analog-to-digital converted to obtain a baseband digital signal vector; based on the baseband digital signal vector, the spatial autocorrelation matrix of the satellite navigation radio frequency signal is estimated within a preset observation window; A virtual interference with a periodically changing incident direction is constructed, and the array weighting vector that dynamically changes with time is calculated based on the spatial autocorrelation matrix and the parameters of the virtual interference. The received signal is spatially filtered using the dynamically changing array weighting vector, and each navigation signal is tracked independently. The carrier-to-noise ratio sequence of each tracked navigation signal is estimated within the observation time window. Calculate the correlation coefficient between the carrier-to-noise ratio sequences of any two tracked navigation signals; set a detection threshold, and if the correlation coefficient between the carrier-to-noise ratio sequences of the two navigation signals is higher than the threshold, then determine that the two signals are subject to the same spatial gain modulation and are classified as deception interference signals. Navigation signals deemed as deception or interference will be excluded from the positioning calculation, the navigation calculation results will be updated, and a safety alarm will be triggered. Based on the spatial autocorrelation matrix and the parameters of the virtual interference, the array weighting vector that dynamically changes over time is calculated, including: Calculate its steering vector based on the incident direction of the virtual interference; Based on the preset virtual interference power, the spatial autocorrelation matrix is ​​corrected to obtain the corrected spatial autocorrelation matrix. Based on the corrected spatial correlation matrix and the preset constraint vector, the array weighting vector is calculated.

2. The method according to claim 1, characterized in that, The satellite navigation radio frequency signal is down-converted and analog-to-digital converted to obtain a baseband digital signal vector, including: By including N The array antenna with each element receives satellite navigation radio frequency signals. The received signals undergo down-conversion and analog-to-digital conversion to obtain a baseband digital signal vector. in, The representation vector is N A complex vector with 1 row and 1 column. Indicates the first N The baseband digital signal vector of each array element.

3. The method according to claim 1, characterized in that, The steering vector of the virtual interference is calculated based on its incident direction, including: The incident direction of virtual interference is determined by the azimuth angle. and elevation angle The corresponding unit direction vector is determined to be: Then the first n The phase delay of each element relative to the reference point is: in, For satellite navigation radio frequency signal carrier wavelength, For the first n The position vector of each array element relative to the array reference point, superscript T Indicates the transpose operation; The steering vector of the virtual interference is then: in, The representation vector is N A complex vector with 1 row and 1 column.

4. The method according to claim 1, characterized in that, Based on a preset virtual interference power, the spatial autocorrelation matrix is ​​corrected to obtain a corrected spatial autocorrelation matrix, including: Based on the preset virtual interference power, the spatial autocorrelation matrix is ​​corrected to obtain the corrected spatial autocorrelation matrix. in, For satellite navigation radio frequency signal carrier wavelength, The spatial autocorrelation matrix is... The spatial correlation matrix of the virtual interference. The power of the virtual interference. The steering vector for virtual interference. The conjugate transpose of the steering vector representing virtual interference.

5. The method according to claim 1, characterized in that, Based on the corrected spatial correlation matrix and the preset constraint vector, the array weighting vector is calculated, including: Based on the corrected spatial correlation matrix and the preset constraint vector, the array weighting vector is calculated as follows: in, This represents the corrected spatial correlation matrix. Represents the constraint vector. The representation vector is N A complex vector with 1 row and 1 column.

6. The method according to claim 1, characterized in that, Based on the baseband digital signal vector, the spatial autocorrelation matrix of the satellite navigation radio frequency signal is estimated within a preset observation window, including: Based on the baseband digital signal vector, the spatial autocorrelation matrix of the satellite navigation radio frequency signal is estimated within a preset observation window as follows: in, This indicates the conjugate transpose. The representation vector is N OK N A complex vector of columns, This represents the number of snapshots used to estimate the correlation matrix. Indicates at time t The baseband digital signal vector, Indicates at time t The conjugate transpose of the baseband digital signal vector.

7. The method according to claim 1, characterized in that, Estimate the carrier-to-noise ratio sequence for each tracked navigation signal within the observation time window, including: The carrier-to-noise ratio sequence for each tracked navigation signal is estimated within the observation time window as follows: in, L The number of carrier-to-noise ratio sampling points within the observation time window. Indicates the first k The navigation signal at the first L The carrier-to-noise ratio at each sampling time.

8. The method according to claim 1, characterized in that, Calculate the correlation coefficient between any two carrier-to-noise ratio sequences of tracked navigation signals, including: The correlation coefficient between any two carrier-to-noise ratio sequences of tracked navigation signals is calculated as follows: in, and Signals i and signal j In the l The carrier-to-noise ratio at each sampling time. L This represents the number of carrier-to-noise ratio sampling points within the observation time window.

9. The method according to claim 1, characterized in that, The update period of the array weighted vector is less than the change period of the virtual interference azimuth angle.

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