Beidou satellite navigation deception detection method and system based on multi-source information

Abstract

The invention relates to the technical field of ship navigation system safety, and provides a Beidou satellite navigation deception detection method and system based on multi-source information, and the method comprises the steps: obtaining Beidou satellite navigation data, AIS information, navigation radar data and sea chart data; performing denoising processing on the Beidou satellite navigation data, and performing space-time compensation on the AIS information; clutter suppression is carried out on the navigation radar data; calculating ship body position deviation, and verifying the ship body relative position according to the ship body position deviation; calculating the real position of the ship body and the Beidou positioning deviation, and verifying the absolute position of the ship body; performing hull course consistency verification; and performing Beidou satellite navigation deception detection according to the ship body relative position verification, the ship body absolute position verification and the ship body course consistency verification. According to the invention, multi-dimensional detection is carried out on key information such as the position and course of the ship, so that the probability that the ship falls into danger due to wrong navigation signals is greatly reduced, and the navigation safety of the ship is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of ship navigation system safety, and particularly to a Beidou satellite navigation spoofing detection method and system based on multi-source information. Background Art

[0002] The Beidou satellite navigation system can provide users with high-precision and highly reliable positioning, navigation, and timing services. It adopts a passive positioning method, achieving performance comparable to that of GPS, and supports a unique short message communication service. In a complex electromagnetic environment, the Beidou signal is vulnerable to spoofing interference attacks. Once such an attack occurs, devices relying on Beidou navigation, such as ships, may obtain false position information. For ship navigation, the accuracy of position information is related to life and property safety, and false position information will undoubtedly pose a serious threat to the navigation safety of ships.

[0003] Relying solely on the Beidou signal is extremely vulnerable to environmental noise interference. In the actual marine environment, factors such as sea waves and the atmospheric ionosphere will generate noise, which will affect the reception and processing of the Beidou signal. In this case, the detection system is prone to misjudgment, misjudging normal signal fluctuations as abnormal conditions, resulting in a high false alarm rate. This will not only increase the workload of operators but also may cause real abnormal conditions to be ignored, reducing the reliability and safety of the system. Most of the existing determination mechanisms adopt a fixed threshold method. The fixed threshold determination lacks the adaptability to the ship's motion state and environment and cannot adjust the determination threshold according to the real-time motion state and environmental changes of the ship, resulting in inaccurate detection results and making it difficult to effectively guarantee the navigation safety of ships.

[0004] During the ship's navigation process, in addition to the Beidou signal, there are many other useful information sources, such as AIS neighboring ship data, radar fixed targets, etc. AIS neighboring ship data can provide information such as the position, heading, and speed of surrounding ships, and radar fixed targets can help determine the positions of surrounding fixed objects. These information all have the characteristics of cross-verification and can assist in judging the accuracy of the Beidou signal. However, the existing technology fails to fully explore and utilize these multi-source information and does not establish an effective fusion mechanism, making the information isolated from each other and unable to form an organic whole to improve the accuracy and reliability of detection, resulting in a waste of resources. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides a Beidou satellite navigation spoofing detection method and system based on multi-source information, which performs Beidou satellite navigation spoofing detection based on Beidou satellite navigation data, AIS information, navigation radar data, and chart data, effectively detects Beidou satellite navigation spoofing interference, and accurately identifies false position information. Through multi-level warning logics such as hull relative position verification, hull absolute position verification, and course consistency verification, multi-dimensional detection of key information such as the ship's position and course is carried out, greatly reducing the probability of the ship falling into danger due to incorrect navigation signals and ensuring the safety of ship navigation.

[0006] The present invention provides a Beidou satellite navigation spoofing detection method based on multi-source information, including: S1: Obtain Beidou satellite navigation data, AIS information, navigation radar data, and chart data; S2: Perform denoising processing on the Beidou satellite navigation data and perform spatio-temporal compensation on the AIS information; perform clutter suppression on the navigation radar data; S3: Calculate the hull position deviation according to the Beidou satellite navigation data and the AIS information, and perform hull relative position verification according to the hull position deviation; S4: Calculate the true position of the hull according to the chart data and the radar navigation data, calculate the Beidou positioning deviation according to the true position of the hull, and perform hull absolute position verification according to the Beidou positioning deviation; S5: Perform hull course consistency verification according to the Beidou satellite navigation data and the AIS information; S6: Perform Beidou satellite navigation spoofing detection according to the hull relative position verification, hull absolute position verification, and hull course consistency verification.

[0007] Further, the Beidou satellite navigation data includes longitude, latitude, and Beidou course; the AIS information includes the positions of adjacent ships, the speeds of adjacent ships, and the courses of adjacent ships; the navigation radar data includes the distances to surrounding targets and the target azimuth angles; the chart data includes the coordinates of fixed targets.

[0008] Further, in step S2, the wavelet decomposition adaptive threshold denoising algorithm is used to eliminate the multipath effect of the Beidou satellite navigation data, including: S211: Perform 3-layer decomposition using the db4 wavelet basis to obtain high-frequency detail coefficients and low-frequency approximation coefficients; S212: Apply a soft threshold to perform denoising processing on the high-frequency detail coefficients; S213: Reconstruct the Beidou satellite navigation data through the denoised high-frequency detail coefficients and low-frequency approximation coefficients.

[0009] Further, in step S2, the AIS information is spatio-temporally compensated through transmission delay compensation and course mutation detection, including: S221: Calculate the signal transmission delay time according to the position of adjacent ships; S222: Correct the time stamps of adjacent ships according to the signal transmission delay time; S223: Freeze the AIS data source when the sudden change of the speed of an adjacent ship is greater than 45° / s or the speed of an adjacent ship is greater than 30 kn seconds.

[0010] Furthermore, in step S2, the sea clutter of radar data is suppressed by polarization filtering and median filtering in a sliding window, including: S231: Separate the horizontal polarization echo and the vertical polarization echo in the radar data echo by polarization filtering to eliminate cross-interference; S232: Remove the noise spikes in the radar data by median filtering in a sliding window; S233: Calculate the chi-square statistic for consecutive times of radar ranging. If the chi-square statistic is greater than 35, the anti-interference mode is enabled.

[0011] Furthermore, step S3 includes: S31: Calculate the theoretical distances between the hull and the adjacent ships according to the AIS information of the adjacent ships and the Beidou satellite navigation data; S32: Calculate the difference between the actually measured radar distance and the theoretical distances between the hull and the adjacent ships to obtain the distance deviation; S33: Calculate the dynamic threshold according to the speed and the standard deviation of the original radar noise; S34: If the distance deviation is greater than or equal to the dynamic threshold, the relative position between the hull and the adjacent ship is abnormal; If the distance deviation is less than the dynamic threshold, the relative position between the hull and the adjacent ship is normal.

[0012] Furthermore, step S4 includes: S41: Select a fixed target from the chart data; S42: Calculate the true position of the hull according to the radar navigation data and the fixed target data. The calculation formula is: where is the true longitude of the hull, is the longitude of the fixed target, is the radar ranging, is the radar measurement azimuth angle, is the true longitude of the hull, is the latitude of the fixed target, is the radius of the earth; S43: Calculate the Beidou positioning deviation based on the actual position of the hull. The calculation formula is: where, is the Beidou positioning deviation, is the Beidou navigation latitude, is the deviation between the actual longitude of the hull and the longitude output by Beidou, is the deviation between the actual latitude of the hull and the latitude output by Beidou; S44: Repeat steps S41 to S43 to obtain multiple Beidou positioning deviations; S45: Set the positioning deviation threshold. If the number of Beidou positioning deviations greater than the positioning deviation threshold is more than 2, the hull position is abnormal; If the number of Beidou positioning deviations greater than the positioning deviation threshold is less than or equal to 2, the hull position is normal.

[0013] Further, step S5 includes: S51: Calculate the cosine similarity between the Beidou course and the AIS reported course. The calculation formula is: where, is the course cosine similarity, is the Beidou navigation course, is the AIS reported course; S52: Calculate the difference between the Beidou navigation course and the AIS reported course to obtain the course deviation; S53: If the course cosine similarity is less than 0.7 and lasts for more than 30 seconds, or the course deviation is greater than 45°, the hull course is abnormal.

[0014] Further, in step S6, If any one of the hull relative position verification, hull absolute position verification, and hull course consistency verification is abnormal, there is Beidou satellite navigation spoofing, and a prompt warning is given; If any two of the hull relative position verification, hull absolute position verification, and hull course consistency verification are abnormal, or all three are abnormal, a first-level alarm is given.

[0015] According to a Beidou satellite navigation spoofing detection system based on multi-source information provided by the present invention, which is used to execute any one of the above-mentioned Beidou satellite navigation spoofing detection methods based on multi-source information, includes: An acquisition module, where the acquisition module is used to acquire Beidou satellite navigation data, AIS information, navigation radar data, and nautical chart data; A data processing module, which is used to denoise Beidou satellite navigation data, perform spatio-temporal compensation on AIS information, and suppress clutter in navigation radar data; A first verification module, which calculates the hull position deviation based on the Beidou satellite navigation data and AIS information, and verifies the relative position of the hull according to the hull position deviation; A second verification module, which calculates the true position of the hull based on the chart data and radar navigation data, calculates the Beidou positioning deviation according to the true position of the hull, and verifies the absolute position of the hull according to the Beidou positioning deviation; A third verification module, which checks the consistency of the hull heading according to the Beidou satellite navigation data and AIS information; A spoofing detection module, which performs Beidou satellite navigation spoofing detection based on the verification of the relative position of the hull, the verification of the absolute position of the hull, and the verification of the consistency of the hull heading.

[0016] One or more of the above technical solutions in the embodiments of the present invention have at least one of the following technical effects: Based on Beidou satellite navigation data, AIS information, navigation radar data, and chart data, Beidou satellite navigation spoofing detection is carried out, effectively detecting Beidou satellite navigation spoofing interference and accurately identifying false position information. Through multi-level warning logics such as AIS-Beidou cross-verification, fixed target absolute verification, and heading consistency verification, multi-dimensional detection of key information such as ship position and heading is carried out, greatly reducing the probability of the ship falling into danger due to incorrect navigation signals and ensuring the safety of ship navigation.

[0017] The additional aspects and advantages of the present invention will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present invention. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic flowchart of a method for detecting Beidou satellite navigation spoofing based on multi-source information provided by the present invention.

[0020] Figure 2 It is a schematic structural diagram of a system for detecting Beidou satellite navigation spoofing based on multi-source information provided by the present invention.

[0021] Reference Signs: 101, Acquisition Module; 102, Data Processing Module; 103, First Verification Module; 104, Second Verification Module; 105, Third Verification Module; 106, Deception Detection Module. Detailed Implementation Manner

[0022] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention. The following embodiments are used to illustrate the present invention but cannot be used to limit the scope of the present invention.

[0023] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art can combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0024] The following will be combined with Figures 1 to 2 Describe a Beidou satellite navigation deception detection method and system based on multi-source information of the present invention.

[0025] As Figure 1 shown, a Beidou satellite navigation deception detection method based on multi-source information includes: S1: Obtain Beidou satellite navigation data, AIS information, navigation radar data, and nautical chart data; The Beidou satellite navigation data includes output longitude, output latitude, and Beidou course; The AIS information includes the position of adjacent ships, the speed of adjacent ships, and the course of adjacent ships; The navigation radar data includes the distance to surrounding targets and the target azimuth angle; The nautical chart data includes fixed target coordinates.

[0026] S2: Denoise the Beidou satellite navigation data, perform spatio-temporal compensation on the AIS information; suppress clutter on the navigation radar data; Adopt the wavelet decomposition adaptive threshold denoising algorithm to eliminate the multipath effect of the Beidou satellite navigation data, including: Taking the output longitude as an example, the output longitude is , where is the longitude output by the Beidou satellite navigation, is the first longitude value, is the second longitude value, is the th longitude value, and the output longitude contains multipath noise; S211: Use the db4 wavelet basis to decompose the output longitude into 3 layers to obtain the first high-frequency detail coefficient , the second high-frequency detail coefficient , the third high-frequency detail coefficient and the low-frequency approximation coefficient ; S212: Apply the soft threshold to for denoising; S213: Reconstruct the output longitude through , , the denoised and the low-frequency approximation coefficient to obtain the denoised longitude.

[0027] The output latitude adopts the same processing method and will not be elaborated here.

[0028] Perform spatio-temporal compensation on the AIS information through transmission delay compensation and course mutation detection, including: S221: Calculate the signal transmission delay time according to the position of the adjacent ship, and the calculation formula is: where is the signal transmission delay time, is the theoretical distance between the hull and the th adjacent ship, is the speed of light, .

[0029] S222: Correct the timestamp of the adjacent ship according to the signal transmission delay time, and the calculation formula is: where is the corrected timestamp, is the timestamp of the adjacent ship.

[0030] S223: When the course mutation of the adjacent ship > 45° / s or the speed of the adjacent ship > 30 kn, freeze the AIS data source seconds.

[0031] In some specific embodiments of the present invention, .

[0032] Suppress sea clutter in radar data through polarization filtering and sliding window median filtering, including: The original radar ranging sequence is: Among them, is the original radar ranging sequence, is the first original radar ranging, is the second original radar ranging, is the th original radar ranging; S231: Separate the horizontal polarization echo and vertical polarization echo in the radar data echo through polarization filtering to eliminate cross-interference; There are two polarization modes in the radar echo signal, horizontal polarization (HH) and vertical polarization (VV). Echoes of different polarization modes may produce cross-interference. By separating these two polarization echoes and calculating the covariance matrix, the correlation between signals can be analyzed and processed, thereby eliminating cross-interference, making the signal received by the radar purer, and reducing the impact of interference on subsequent processing; S232: Remove the noise spikes in the radar data through sliding window median filtering; In the radar ranging sequence, with the current radar ranging as the center, select two radar rangings before and after it respectively, that is to form a sliding window, and take the median value of the data in this window as the filtered radar ranging , and the calculation formula is: Among them, is the th radar ranging, is the th radar ranging, is the th radar ranging, is the th radar ranging, is the sliding window median filtering operation; Sliding window median filtering can effectively suppress outliers such as pulse noise because the median is insensitive to individual extreme values.

[0033] S233: Calculate the chi-square statistic for consecutive radar rangings. If the chi-square statistic is greater than 35, the anti-interference mode is enabled In some specific embodiments of the present invention, .

[0034] After sliding window median filtering, the ranging standard deviation can be reduced from 25 m to 12 m.

[0035] S3: Calculate the hull position deviation based on the Beidou satellite navigation data and AIS information, and verify the relative position of the hull according to the hull position deviation; including: S31: According to the AIS information of adjacent vessels and the Beidou satellite navigation data, calculate the theoretical distance between the hull and the adjacent vessels. The calculation expression is: where, is the theoretical distance between the hull and the th adjacent vessel, is the radius of the earth, is the longitude output by Beidou, is the th adjacent vessel longitude, is the latitude output by Beidou, is the th adjacent vessel latitude.

[0036] In some specific embodiments of the present invention, .

[0037] S32: Calculate the difference between the actually measured radar distance and the theoretical distance between the hull and the adjacent vessels to obtain the distance deviation; where, is the distance deviation between the th adjacent vessel and the hull, is the th actually measured radar distance; S33: Calculate the dynamic threshold according to the ship speed and the standard deviation of the original radar noise. The calculation expression is: where, is the dynamic threshold, is the ship speed, is the standard deviation of the original radar noise; S34: If the distance deviation is greater than or equal to the dynamic threshold, the relative position of the hull and the adjacent vessel is abnormal; If the distance deviation is less than the dynamic threshold, the relative position of the hull and the adjacent vessel is normal.

[0038] S4: Calculate the true position of the hull according to the chart data and the radar navigation data, calculate the Beidou positioning deviation according to the true position of the hull, and verify the absolute position of the hull according to the Beidou positioning deviation; S41: Select a fixed target from the chart data, S42: Calculate the true position of the hull based on the radar navigation data and the fixed target data. The calculation formula is: where is the true longitude of the hull, is the longitude of the fixed target, is the radar ranging, is the azimuth angle measured by the radar, is the true longitude of the hull, is the latitude of the fixed target, is the radius of the earth; S43: Calculate the Beidou positioning deviation based on the true position of the hull. The calculation formula is: where is the Beidou positioning deviation, is the latitude of the Beidou navigation, is the deviation between the true longitude of the hull and the longitude output by Beidou, is the deviation between the true latitude of the hull and the latitude output by Beidou; S44: Repeat steps S41 to S43 to obtain multiple Beidou positioning deviations; S45: Set the positioning deviation threshold. If the number of Beidou positioning deviations greater than the positioning deviation threshold is more than 2, the hull position is abnormal; If the number of Beidou positioning deviations greater than the positioning deviation threshold is less than or equal to 2, the hull position is normal.

[0039] S5: Perform hull course consistency verification based on the Beidou satellite navigation data and the AIS information; S51: Calculate the cosine similarity between the Beidou course and the AIS reported course. The calculation formula is: where is the course cosine similarity, is the Beidou navigation course, is the AIS reported course; S52: Calculate the difference between the Beidou navigation course and the AIS reported course to obtain the course deviation; S53: If the course cosine similarity is less than 0.7 and lasts for more than 30 seconds, or the course deviation is greater than 45°, the hull course is abnormal.

[0040] S6: Perform Beidou satellite navigation spoofing detection based on the verification of the relative position of the hull, the verification of the absolute position of the hull, and the hull course consistency verification; If any one of the hull relative position verification, hull absolute position verification, and hull course consistency check is abnormal, there is Beidou satellite navigation spoofing, and a prompt warning is given; If any two of the hull relative position verification, hull absolute position verification, and hull course consistency check are abnormal, or all three are abnormal, a first-level alarm is given.

[0041] As Figure 2 shown, a Beidou satellite navigation spoofing detection system based on multi-source information, which is used to execute the above-mentioned Beidou satellite navigation spoofing detection method based on multi-source information, includes: The acquisition module 101 is used to acquire Beidou satellite navigation data, AIS information, navigation radar data, and nautical chart data; The data processing module 102 is used to denoise the Beidou satellite navigation data and perform spatio-temporal compensation on the AIS information; clutter suppression will be performed on the navigation radar data; The first verification module 103 calculates the hull position deviation according to the Beidou satellite navigation data and the AIS information, and performs hull relative position verification according to the hull position deviation; The second verification module 104 calculates the true hull position according to the nautical chart data and the radar navigation data, calculates the Beidou positioning deviation according to the true hull position, and performs hull absolute position verification according to the Beidou positioning deviation; The third verification module 105 performs hull course consistency check according to the Beidou satellite navigation data and the AIS information; The spoofing detection module 106 performs Beidou satellite navigation spoofing detection according to the hull relative position verification, hull absolute position verification, and hull course consistency check.

[0042] Through the collaborative work of the above modules, Beidou satellite navigation spoofing detection is carried out based on Beidou satellite navigation data, AIS information, navigation radar data, and nautical chart data, effectively detecting Beidou satellite navigation spoofing interference and accurately identifying false position information. Through multi-level alarm logics such as hull relative position verification, hull absolute position verification, and course consistency check, multi-dimensional detection of key information such as ship position and course is carried out, greatly reducing the probability of the ship falling into danger due to navigation signal errors and ensuring the safety of ship navigation.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A Beidou satellite navigation spoofing detection method based on multi-source information, characterized in that, Including: S1: Obtain Beidou satellite navigation data, AIS information, navigation radar data, and nautical chart data; S2: Denoise the Beidou satellite navigation data, perform spatio-temporal compensation on the AIS information; suppress clutter for the navigation radar data; S3: Calculate the hull position deviation based on the Beidou satellite navigation data and AIS information, and verify the relative position of the hull according to the hull position deviation; S4: Calculate the true position of the hull based on the nautical chart data and radar navigation data, calculate the Beidou positioning deviation according to the true position of the hull, and verify the absolute position of the hull according to the Beidou positioning deviation; S5: Perform hull course consistency verification based on the Beidou satellite navigation data and AIS information; S6: Perform Beidou satellite navigation spoofing detection based on the verification of the relative position of the hull, the verification of the absolute position of the hull, and the hull course consistency verification.

2. The Beidou satellite navigation spoofing detection method based on multi-source information according to claim 1, wherein The Beidou satellite navigation data includes longitude, latitude, and Beidou course; the AIS information includes the position of adjacent ships, the speed of adjacent ships, and the course of adjacent ships; the navigation radar data includes the distance to surrounding targets and the target azimuth angle; the nautical chart data includes fixed target coordinates.

3. The Beidou satellite navigation spoofing detection method based on multi-source information according to claim 2, characterized in that, In step S2, the wavelet decomposition adaptive threshold denoising algorithm is used to eliminate the multipath effect of the Beidou satellite navigation data, including: S211: Perform 3-layer decomposition using the db4 wavelet basis to obtain high-frequency detail coefficients and low-frequency approximation coefficients; S212: Apply soft threshold to denoise the high-frequency detail coefficients; S213: Reconstruct the Beidou satellite navigation data through the denoised high-frequency detail coefficients and low-frequency approximation coefficients.

4. A Beidou satellite navigation spoofing detection method based on multi-source information according to claim 2, characterized in that, In step S2, spatio-temporal compensation is performed on the AIS information through transmission delay compensation and course mutation detection, including: S221: Calculate the signal transmission delay time based on the position of adjacent ships; S222: Correct the timestamp of adjacent ships according to the signal transmission delay time; S223: Freeze the AIS data source if the sudden change in the speed of the adjacent ship is greater than 45° / s or the speed of the adjacent ship is greater than 30 kn seconds.

5. The Beidou satellite navigation spoofing detection method based on multi-source information according to claim 2, wherein In step S2, sea clutter of radar data is suppressed through polarization filtering and sliding window median filtering, including: S231: Separate the horizontal polarization echo and vertical polarization echo in the radar data echo through polarization filtering to eliminate cross-interference; S232: Remove noise spikes in the radar data through sliding window median filtering; S233: For consecutive times, calculate the chi-square statistic for radar ranging. If the chi-square statistic is greater than 35, enable the anti-interference mode.

6. The Beidou satellite navigation spoofing detection method based on multi-source information according to claim 1, wherein, Step S3 includes: S31: Calculate the theoretical distance between the hull and the adjacent adjacent ships based on the AIS information of adjacent ships and the Beidou satellite navigation data; S32: Calculate the difference between the actually measured radar distance and the theoretical distance between the hull and the adjacent ships to obtain the distance deviation; S33: Calculate the dynamic threshold according to the speed and the standard deviation of the original radar noise; S34: If the distance deviation is greater than or equal to the dynamic threshold, the relative position of the hull and the adjacent ship is abnormal; If the distance deviation is less than the dynamic threshold, the relative position of the hull and the adjacent ship is normal.

7. A Beidou satellite navigation spoofing detection method based on multi-source information according to claim 1, characterized in that, Step S4 includes: S41: Select a fixed target from the nautical chart data; S42: Calculate the true position of the hull according to the radar navigation data and the fixed target data, and the calculation expression is: Wherein, is the true longitude of the hull, is the longitude of the fixed target, is the radar ranging, is the azimuth angle measured by the radar, is the true longitude of the hull, is the latitude of the fixed target, is the radius of the earth; S43: Calculate the Beidou positioning deviation according to the true position of the hull, and the calculation expression is: Among them, is the Beidou positioning deviation, is the Beidou navigation latitude, is the deviation between the true longitude of the hull and the longitude output by Beidou, is the deviation between the true latitude of the hull and the latitude output by Beidou; S44: Repeat steps S41 to S43 to obtain multiple Beidou positioning deviations; S45: Set the positioning deviation threshold. If the number of Beidou positioning deviations greater than the positioning deviation threshold is greater than 2, the hull position is abnormal; If the number of Beidou positioning deviations greater than the positioning deviation threshold is less than or equal to 2, the hull position is normal.

8. A Beidou satellite navigation spoofing detection method based on multi-source information according to claim 1, characterized in that, Step S5 includes: S51: Calculate the cosine similarity between the Beidou course and the AIS reported course, and the calculation expression is: Among them, is the heading cosine similarity, is the Beidou navigation heading, is the AIS reported heading; S52: Calculate the difference between the Beidou navigation course and the AIS-reported course to obtain the course deviation; S53: If the course cosine similarity is less than 0.7 and lasts for more than 30 seconds, or the course deviation is greater than 45°, then the hull course is abnormal.

9. A Beidou satellite navigation spoofing detection method based on multi-source information according to claim 1, characterized in that, In step S6, If any one of the hull relative position verification, hull absolute position verification, and hull course consistency verification is abnormal, there is Beidou satellite navigation spoofing, and a prompt warning is given; If any two of the hull relative position verification, hull absolute position verification, and hull course consistency verification are abnormal, or all three are abnormal, a first-level alarm is given.

10. A Beidou satellite navigation spoofing detection system based on multi-source information, characterized in that, Used to execute a Beidou satellite navigation spoofing detection method according to any one of claims 1 to 9, including: An acquisition module, the acquisition module is used to acquire Beidou satellite navigation data, AIS information, navigation radar data, and nautical chart data; A data processing module, the data processing module is used to denoise the Beidou satellite navigation data, perform spatio-temporal compensation on the AIS information, and suppress clutter on the navigation radar data; A first verification module, the first verification module calculates the hull position deviation according to the Beidou satellite navigation data and the AIS information, and performs hull relative position verification according to the hull position deviation; A second verification module, the second verification module calculates the true hull position according to the nautical chart data and the radar navigation data, calculates the Beidou positioning deviation according to the true hull position, and performs hull absolute position verification according to the Beidou positioning deviation; A third verification module, the third verification module performs hull course consistency verification according to the Beidou satellite navigation data and the AIS information; A spoofing detection module, the spoofing detection module performs Beidou satellite navigation spoofing detection according to the hull relative position verification, hull absolute position verification, and hull course consistency verification.

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