Satellite navigation spoofing interference detection method, device, equipment, medium and program

Abstract

The application provides a satellite navigation spoofing interference detection method, device, equipment, medium and program, the method comprises the following steps: acquiring the carrier-to-noise ratio observation data and the subpoint regression period in the satellite signal, the carrier-to-noise ratio observation data includes the carrier-to-noise ratio observation value corresponding to at least one observation point in the subpoint regression period; according to the carrier-to-noise ratio observation data and the subpoint regression period, the historical carrier-to-noise ratio observation data corresponding to the satellite is acquired, and the carrier-to-noise ratio estimation change rule is determined; according to the carrier-to-noise ratio estimation change rule and the carrier-to-noise ratio observation data, the carrier-to-noise ratio change difference is determined, and the detection statistic quantity of the corresponding satellite is determined in combination with the carrier-to-noise ratio estimation change rule; when the detection statistic quantity of the satellite is greater than the detection threshold, it is determined that the corresponding satellite signal is a spoofing signal. The application can quickly identify abnormal signals, reduce the risk of misjudgment and omission, significantly improve the user spoofing defense capability of satellite navigation, and can ensure that the user safely and reliably uses the satellite navigation system.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation technology, and in particular to a method, apparatus, device, medium, and program for detecting satellite navigation deception and interference. Background Technology

[0002] The Global Navigation Satellite System (GNSS) provides users with all-day, all-weather, high-precision position, velocity, and time (PVT) information, playing a crucial role in positioning, navigation, and timing. It is currently widely used in critical infrastructure sectors such as power, transportation, and finance, providing continuous and stable spatiotemporal information references and serving as a significant driving force for industry development. GNSS spoofing and jamming exploits publicly available signal structures to create false signals, broadcasting them at high power to suppress genuine signals and induce target receivers to output incorrect positioning and timing results.

[0003] Currently, satellite navigation interference detection technology is mainly based on spectrum detection technology. It relies on professionals from radio management agencies to use handheld specialized equipment to determine whether interference exists and to locate the source of interference based on information such as the environmental spectrum displayed by the equipment, so as to scan and investigate the surrounding areas of key regions.

[0004] However, this method is labor-intensive, time-consuming, and inefficient. The accuracy of detection is highly dependent on the operator's experience and knowledge level, and the detection is not objective enough. In addition, some satellite navigation jammers have directional antennas. If the antenna is facing the sky, the ground-received signal is relatively weak, which makes the ground investigation method potentially ineffective. Summary of the Invention

[0005] This invention provides a satellite navigation spoofing interference detection method, apparatus, device, medium, and program to address the shortcomings of existing technologies, such as insufficient objectivity and poor detection accuracy. It can quickly identify abnormal signals, reduce the risk of false positives and false negatives, significantly improve the user spoofing defense capability of satellite navigation, and ensure that users can use the satellite navigation system safely and reliably.

[0006] This invention provides a method for detecting satellite navigation spoofing interference, comprising: acquiring carrier-to-noise ratio (CNR) observation data and nadir point regression period from a satellite signal; the CNR observation data includes CNR observation values ​​corresponding to at least one observation point within the nadir point regression period; wherein, the nadir point regression period characterizes the time interval during which the nadir point trajectory of the satellite completely repeats when it is in orbit, and the nadir point characterizes the projection point on the Earth's surface of the line connecting the satellite and the Earth's center; acquiring historical CNR observation data of the corresponding satellite based on the CNR observation data and the nadir point regression period, and determining the predicted CNR variation pattern; determining the CNR variation difference based on the predicted CNR variation pattern and the CNR observation data, and determining the detection statistic of the corresponding satellite in conjunction with the predicted CNR variation pattern; and determining that the corresponding satellite signal is a spoofing signal when the detection statistic of the satellite is greater than a previously acquired detection threshold.

[0007] According to a satellite navigation spoofing interference detection method provided by the present invention, the method acquires historical carrier-to-noise ratio (CNR) observation data of the corresponding satellite based on carrier-to-noise ratio (CNR) observation data and navigator regression period, and determines the predicted CNR variation law. The method includes: determining the observation time period of the CNR observation data relative to the corresponding navigator regression period based on the CNR observation data and navigator regression period; selecting a first preset number of historical navigator regression periods based on the navigator regression period corresponding to the CNR observation data, and determining the historical CNR observation value of the historical observation time period corresponding to each historical navigator regression period, thereby obtaining the historical CNR observation data corresponding to each historical navigator regression period; and determining the average CNR observation value corresponding to each observation point based on the historical CNR observation data corresponding to each historical navigator regression period, thereby obtaining the predicted CNR variation law.

[0008] According to a satellite navigation spoofing interference detection method provided by the present invention, the method for obtaining carrier-to-noise ratio (CNR) observation data in satellite signals includes: acquiring satellite signals and obtaining corresponding CNR observation values ​​based on the satellite signals; using a preset smoothing window, selecting the CNR observation values ​​and the predicted CNR variation law for forward smoothing processing, and combining a second preset quantity to filter the smoothed CNR observation values ​​to obtain CNR observation data; wherein the second preset quantity is less than or equal to the length of the preset smoothing window.

[0009] According to the present invention, a satellite navigation deception interference detection method includes the historical carrier-to-noise ratio (CNR) observation data of the corresponding satellite, which includes historical CNR observation data within a first preset number of historical nadir point regression periods. Based on the predicted CNR variation law and the CNR observation data, the method determines the CNR variation difference and, in conjunction with the predicted CNR variation law, determines the detection statistics of the corresponding satellite. This includes: determining the CNR variation difference based on the predicted CNR variation law and the CNR observation data; obtaining the variance based on the predicted CNR variation law; constructing a covariance matrix based on the variance, the first preset number, the length of a preset smoothing window, and the second preset number; and obtaining the detection statistics of the corresponding satellite based on the CNR variation difference, the covariance matrix, and a preset constant vector. The preset constant vector is used to represent a vector where all elements are the same preset constant.

[0010] According to the present invention, a satellite navigation deception interference detection method includes the following steps before determining that the detection statistic of the satellite is greater than a previously acquired detection threshold: acquiring a preset false alarm probability; obtaining a first probability distribution based on the preset false alarm probability using a central chi-square distribution with preset degrees of freedom; and obtaining a detection threshold based on the first probability distribution using the inverse function of the right-tailed function.

[0011] According to a satellite navigation spoofing interference detection method provided by the present invention, the method for obtaining the nadir point return cycle includes: acquiring satellite ephemeris information and space signal interface control file, and acquiring user position; determining the satellite's position vector in a geocentric inertial coordinate system at multiple discrete time points based on the satellite ephemeris information and using the orbit calculation algorithm configured in the space signal interface control file; converting the satellite's position vector in the geocentric inertial coordinate system at multiple discrete time points into position vectors in a fixed coordinate system, and converting the position vectors in each fixed coordinate system into geographic coordinates to obtain the satellite's nadir point geographic coordinate sequence; obtaining the relative distance between each nadir point and the user position based on the satellite's nadir point geographic coordinate sequence and the user position, and determining the time interval for the nadir point trajectory to repeat within a preset range of the user position to obtain the nadir point return cycle.

[0012] This invention also provides a satellite navigation spoofing interference detection device, comprising: a data acquisition module for acquiring carrier-to-noise ratio (CNR) observation data and nadir point regression period from a satellite signal; the CNR observation data includes CNR observation values ​​corresponding to at least one observation point within the nadir point regression period; wherein the nadir point regression period characterizes the time interval during which the nadir point trajectory of the satellite completely repeats itself while it is in orbit, and the nadir point characterizes the projection point of the line connecting the satellite and the Earth's center on the Earth's surface; a pattern prediction module for acquiring historical CNR observation data of the corresponding satellite based on the CNR observation data and the nadir point regression period, and determining the predicted CNR change pattern; a detection statistics module for determining the CNR change difference based on the predicted CNR change pattern and the CNR observation data, and determining the detection statistics of the corresponding satellite based on the predicted CNR change pattern; and an interference determination module for determining that the corresponding satellite signal is a spoofing signal when the detection statistics of the satellite are greater than a previously acquired detection threshold.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the satellite navigation spoofing interference detection methods described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the satellite navigation spoofing interference detection method as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements any of the satellite navigation spoofing interference detection methods described above.

[0016] The satellite navigation spoofing interference detection method, apparatus, equipment, medium, and program provided by this invention acquires carrier-to-noise ratio (CNR) observation data and nadir point regression period from satellite signals. This allows for the effective capture of CNR signal variation patterns by leveraging the periodicity of satellite trajectories, avoiding random data interference. Combined with CNR observation data, the differences in CNR variation are determined to accurately quantify the deviation between actual observation and prediction. This makes the subsequent calculation of detection statistics more precise, better distinguishing between genuine abnormal signals and noise caused by natural data fluctuations. By comparing detection statistics and detection thresholds, abnormal signals can be quickly identified, reducing the risk of misjudgment and missed detection. This significantly improves the user spoofing defense capability of satellite navigation, ensuring users can safely and reliably use the satellite navigation system. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is one of the flowcharts of the satellite navigation spoofing interference detection method provided by the present invention; Figure 2 This is the second flowchart of the satellite navigation spoofing interference detection method provided by the present invention; Figure 3 This is a schematic diagram of the carrier-to-noise ratio of each deception signal and its detection results in a generative deception scenario provided by the present invention; Figure 4 This is a schematic diagram illustrating the changing trend of detection statistics after deception injection provided by the present invention; Figure 5 This is a schematic diagram of the structure of the satellite navigation deception and interference detection device provided by the present invention; Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0020] Figure 1 This is a flowchart illustrating the satellite navigation spoofing interference detection method provided by the present invention, as shown below. Figure 1 As shown, the method includes: S11, acquire the carrier-to-noise ratio (CNR) observation data and the nadir point regression period from the satellite signal. The CNR observation data includes the CNR observation value corresponding to at least one observation point within the corresponding nadir point regression period. The nadir point regression period is used to characterize the time interval during which the nadir point trajectory of the satellite completely repeats when it is in orbit. The nadir point is used to characterize the projection point of the line connecting the satellite and the Earth's center on the Earth's surface. S12. Based on the carrier-to-noise ratio observation data and the nadir point regression period, obtain the historical carrier-to-noise ratio observation data of the corresponding satellite and determine the predicted change pattern of the carrier-to-noise ratio. S13. Based on the predicted carrier-to-noise ratio (CNR) variation pattern and the CNR observation data, determine the CNR variation difference, and combine it with the predicted CNR variation pattern to determine the corresponding satellite detection statistics. S14, when the detection statistics of the satellite are determined to be greater than the previously acquired detection threshold, the corresponding satellite signal is determined to be a spoofing signal.

[0021] It should be noted that the step number "S1N" in this manual does not represent the order of the satellite navigation spoofing interference detection methods. The following details will explain in conjunction with... Figure 2 The present invention describes a satellite navigation spoofing interference detection method.

[0022] Step S11: Obtain the carrier-to-noise ratio (CNR) observation data and the nadir point regression period from the satellite signal. The CNR observation data includes the CNR observation value corresponding to at least one observation point within the corresponding nadir point regression period. The nadir point regression period is used to characterize the time interval during which the nadir point trajectory completely repeats when the satellite is in orbit. The nadir point is used to characterize the projection point of the line connecting the satellite and the Earth's center on the Earth's surface.

[0023] In this embodiment, reference Figure 2 The method for obtaining carrier-to-noise ratio (CNR) observation data from satellite signals includes: acquiring satellite signals and obtaining corresponding CNR observation values ​​based on the satellite signals; using a preset smoothing window, selecting the CNR observation values ​​and the predicted CNR change pattern for forward smoothing processing, and combining a second preset quantity to filter the smoothed CNR observation values ​​to obtain CNR observation data; wherein the second preset quantity is less than or equal to the length of the preset smoothing window.

[0024] It should be added that the method for determining the carrier-to-noise ratio prediction variation law can be found below and will not be repeated here. Furthermore, when acquiring satellite signals, conventional satellite signal reception and processing methods can be used, such as low-noise amplification, down-conversion, and analog-to-digital conversion; no further limitations are made here. Additionally, obtaining the nadir point return period includes: acquiring satellite ephemeris information and space signal interface control files, as well as acquiring the user's position; based on the satellite ephemeris information, using the orbit calculation algorithm configured in the space signal interface control file, determining the satellite's position vector in the geocentric inertial coordinate system at multiple discrete time points; converting the satellite's position vector in the geocentric inertial coordinate system at multiple discrete time points into position vectors in a fixed coordinate system, and converting the position vectors in each fixed coordinate system into geographic coordinates to obtain the satellite's nadir point geographic coordinate sequence; based on the satellite's nadir point geographic coordinate sequence and the user's position, obtaining the relative distance between each nadir point and the user's position, and determining the time interval for the nadir point trajectory to repeat within a preset range of the user's position, thus obtaining the nadir point return period.

[0025] It should be added that satellite ephemeris information includes orbital inclination, right ascension of the ascending node, orbital eccentricity, argument of perigee, mean perigee angle, mean motion, and ephemeris time. Among these, orbital inclination represents the angle between the orbital plane and the Earth's equatorial plane; right ascension of the ascending node represents the projection of the intersection of the orbital plane and the Earth's equatorial plane onto the equator; orbital eccentricity represents the flattening of the orbital ellipse; argument of perigee represents the angle between the perigee and the ascending node; mean perigee angle represents the average position of the satellite in its orbit; mean motion represents the average period of the satellite's orbit around the Earth; and ephemeris time represents the timestamp of data generation. Satellite ephemeris information can be obtained by demodulating navigation messages from satellite navigation system broadcast signals or by obtaining the latest broadcast ephemeris data from the official website. The appropriate acquisition channel can be selected according to actual needs, and no further restrictions are made here.

[0026] In addition, the Interface Control Document (ICD) includes the format, parameters, and orbit calculation algorithm of the acquired satellite ephemeris data. The orbit calculation algorithm can be determined according to the corresponding ICD, such as Newton's iteration method or similar methods, without further limitation here.

[0027] Furthermore, based on the satellite's nadir point geographic coordinate sequence and the user's location, the relative distance between each nadir point and the user's location is obtained, and the time interval between the repeated occurrences of the nadir point trajectory within a preset range of the user's location is determined to obtain the nadir point regression period. This includes: based on the satellite's nadir point geographic coordinate sequence and the user's location, determining the great circle distance or central angle between each nadir point in the nadir point geographic coordinate sequence and the user's location to obtain the relative distance between each nadir point and the user's location; based on the relative distance, determining the time corresponding to the local minimum; based on the time corresponding to all local minimums, determining the time difference between adjacent local minimum times to obtain a time difference sequence; and identifying the periodic, minimum repetition interval in the time difference sequence to obtain the nadir point regression period.

[0028] It should be noted that precise orbital parameters are provided by satellite ephemeris information to determine the satellite's nadir trajectory, and ICD files are used to ensure that ground terminals correctly receive and process satellite signals, thereby more comprehensively determining the satellite's orbit.

[0029] It should be noted that when acquiring the carrier-to-noise ratio observation data and the nadir point regression period in the satellite signal, the satellite signals of multiple satellites and the corresponding nadir point regression period can be acquired simultaneously, and the channel-by-channel detection and identification can be performed using the method described in this paper. The number of satellites acquired is not further limited here, and can be configured according to actual design requirements.

[0030] Step S12: Based on the carrier-to-noise ratio observation data and the nadir point regression period, obtain the historical carrier-to-noise ratio observation data of the corresponding satellite and determine the predicted change pattern of the carrier-to-noise ratio.

[0031] In this embodiment, based on the carrier-to-noise ratio (CNR) observation data and the nadir point regression period, historical CNR observation data for the corresponding satellite is obtained, and the CNR prediction variation pattern is determined. This includes: determining the observation time period of the CNR observation data relative to the corresponding nadir point regression period based on the CNR observation data and the nadir point regression period; selecting a first preset number of historical nadir point regression periods based on the nadir point regression period corresponding to the CNR observation data, and determining the historical CNR observation value of the historical observation time period corresponding to the observation time period in each historical nadir point regression period, thereby obtaining the historical CNR observation data corresponding to each historical nadir point regression period; and determining the average CNR observation value corresponding to each observation point based on the historical CNR observation data corresponding to each historical nadir point regression period, thereby obtaining the CNR prediction variation pattern.

[0032] It should be noted that the nadir trajectory has a regressive periodicity, which means that the satellite will pass through roughly the same position every regression cycle. Users in the same geographical location will be affected by similar path loss, atmospheric attenuation, multipath effects and other factors when observing the carrier-to-noise ratio (CNR) in different regression cycles. Therefore, by observing the periodicity of static GNSS user signal quality, historical CNR observation data of multiple historical regression cycles can be collected. By comparing the previous CNR monitoring change patterns with the real-time CNR monitored by GNSS users, the detection and identification of spoofing signals can be achieved. This can "smooth" these random disturbances and short-term fluctuations that are difficult to predict accurately to a certain extent, so that the predicted patterns can better reflect long-term and macro-level trends.

[0033] Furthermore, the predicted variation law of the carrier-to-noise ratio is expressed as follows: in, This indicates the predicted variation pattern of the carrier-to-noise ratio for the corresponding satellite; This represents the average carrier-to-noise ratio observed at the i-th observation point of the corresponding satellite; M represents the first preset quantity. This represents the carrier-to-noise ratio (CNR) observation value of the i-th observation point in the M-th sub-satellite point regression cycle of the corresponding satellite, in decibels-hertz (dB·Hz).

[0034] Step S13: Based on the predicted change pattern of carrier-to-noise ratio and the observed carrier-to-noise ratio data, determine the difference in carrier-to-noise ratio change, and in conjunction with the predicted change pattern of carrier-to-noise ratio, determine the detection statistics of the corresponding satellite.

[0035] Specifically, the historical carrier-to-noise ratio (CNR) observation data for the corresponding satellite includes historical CNR observation data within a first preset number of historical nadir point regression periods. Based on the predicted CNR variation pattern and the CNR observation data, the CNR variation difference is determined, and combined with the predicted CNR variation pattern, the detection statistics for the corresponding satellite are determined, including: determining the CNR variation difference based on the predicted CNR variation pattern and the CNR observation data; obtaining the variance based on the predicted CNR variation pattern; constructing a covariance matrix based on the variance, the first preset number, the length of the preset smoothing window, and the second preset number; and obtaining the detection statistics for the corresponding satellite based on the CNR variation difference, the covariance matrix, and a preset constant vector. The preset constant vector is used to represent a vector where all elements are the same preset constant.

[0036] It should be noted that the first preset number of historical sub-satellite point regression periods can be selected based on time sequence or can be arbitrarily extracted. The specific selection can be made according to the actual design requirements. Furthermore, the first preset number can be set according to the data storage timeliness, computational load, etc. of the actual data storage system involved. For example, the first preset number can be 7 to 10, and no further limitation is made here.

[0037] In addition, by comparing the carrier-to-noise ratio (CNR) observation data of the satellite within the first preset number of historical sub-satellite point regression cycles with the corresponding predicted CNR change patterns, the differences in CNR changes and the variance are determined to construct a covariance matrix. The covariance matrix characterizes the correlation between observations at different time points, so as to more accurately reflect the intrinsic structure of the data. This makes the subsequent calculation of the determined detection statistics more accurate and can better distinguish between real abnormal signals and noise caused by natural fluctuations in the data.

[0038] It should be added that the detection statistic is expressed as: in, This represents the detection statistics; Indicates the difference in carrier-to-noise ratio. , This represents the carrier-to-noise ratio observation data. This indicates the predicted variation pattern of the carrier-to-noise ratio; Represents the covariance matrix The element in the i-th row and j-th column, , Indicates the second preset quantity. The time limit can be set according to real-time requirements, actual design requirements, or prior experience. For example, it can be 5 seconds, 10 seconds, etc. There are no further limitations here. This represents the variance of the predicted variation of the carrier-to-noise ratio for satellite k. Indicates the first preset quantity; Indicates the length of the preset smooth window. The time can be set according to actual design requirements or prior experience, such as 30 seconds; no further limitation is made here. This represents a preset constant vector. .

[0039] Step S14: When the detection statistics of the satellite are greater than the previously acquired detection threshold, the corresponding satellite signal is determined to be a spoofing signal.

[0040] In one alternative embodiment, after determining that the corresponding satellite signal is a spoofing signal, the method includes: issuing a spoofing alarm to the user.

[0041] In an optional embodiment, the method further includes: when the detection statistics of the satellite are less than or equal to a previously acquired detection threshold, determining that the corresponding satellite signal is a real signal, storing it in a historical observation database and marking it as valid.

[0042] In an optional embodiment, before determining that the detection statistic of the satellite is greater than the previously acquired detection threshold, the method includes: acquiring a preset false alarm probability; obtaining a first probability distribution based on the preset false alarm probability using a central chi-square distribution with preset degrees of freedom; and obtaining a detection threshold based on the first probability distribution using the inverse function of the right-tailed function.

[0043] It should be added that the preset degrees of freedom can be selected based on actual design requirements or prior experience. For example, when the carrier-to-noise ratio prediction changes slowly, the degrees of freedom are usually set to 1, and the corresponding detection threshold is expressed as: in, Indicates the detection threshold; This represents the central chi-square distribution with 1 degree of freedom, i.e., the first probability distribution; It represents the inverse function of the right-tailed function of a central chi-square distribution with 1 degree of freedom; This indicates the preset false alarm probability, which can be set in advance based on actual design requirements or prior experience.

[0044] In an optional embodiment, the nadir return period of each visible satellite is determined based on satellite ephemeris information and carrier-to-noise ratio observations from the 10 days prior to the observation date (i.e., a first preset number), combined with the space signal interface control file. In this example, taking the specific satellite identifier PRN 4 as an example, the satellite nadir return period is... .

[0045] The received satellite signals are amplified with low noise, down-converted, and converted from analog to digital to obtain digital intermediate frequency (IF) signals. The IF signals are then acquired, tracked, and their carrier-to-noise ratio (CNR) is estimated. The CNR measurements of each visible satellite signal are then extracted. .

[0046] Set the second preset quantity Preset smooth window length Carrier-to-noise ratio estimation period , utilizing the past The carrier-to-noise ratio (CNR) observations of the satellite within that time period are subjected to forward smoothing filtering, and the smoothed CNR observations are then filtered according to a second preset number to obtain the smoothed result. .

[0047] Using the satellite sub-satellite point regression period as T 4 The corresponding historical carrier-to-noise ratio (CNR) observation data are extracted, and the average CNR observation value for each observation point is determined to obtain the predicted CNR variation pattern. ,variance and covariance matrix as follows: Based on the above method, the difference in carrier-to-noise ratio variation is obtained. Therefore, the corresponding satellite detection statistics Additionally, take , to obtain the detection threshold .

[0048] Detection statistics Greater than the detection threshold Therefore, PRN 4 was identified as a spoofing signal. This demonstrates that the aforementioned satellite navigation spoofing interference detection method can effectively detect spoofing signals injected with abnormal quality.

[0049] In one alternative embodiment, to verify the effectiveness of the above method, a generative spoofing experiment was conducted using the L1 carrier-to-noise ratio (C / A) signal of the Global Navigation Satellite System (GPS). The specific experimental scenario included a spoofing source antenna, a signal amplifier, a spoofing source, a receiving antenna, and a processing terminal. The spoofing source recorded a spoofing signal generated by a simulator, which was then relayed and used to estimate the carrier-to-noise ratio using a GNSS receiver (Ublox M8T) module. During the experiment, based on the timing results of the collected spoofing signals and the orbital return periods of each satellite, the C / A ratio patterns previously monitored by the local receiving antenna were compared with the observed values.

[0050] To mimic the operating mechanism of traditional generative spoofing sources, the spoofing source calculates pseudorange based on the known user location and determines the relative power of the signal for each channel accordingly. To ensure the power of the relayed signal is slightly higher than the real signal, facilitating more effective capture by the receiving antenna, an additional low-noise amplifier is used in the signal path. Furthermore, since spoofing interference detection is based on comparison and verification using the timing information of the received spoofed signal, a real-time spoofing interference scenario is effectively simulated, enabling a thorough evaluation of the application's performance under such interference.

[0051] After receiving the signal processed by the RF front-end, the GNSS receiver processes the baseband signal and outputs the carrier-to-noise ratio (CNR) of each satellite. When spoofing occurs, since the receiver's position, velocity, and time (PVT) results are completely controlled by the spoofer, it will compare the CNR data obtained from the receiver's previous return period with the time the spoof signal was recorded. For GPS, the return period of each satellite is approximately one sidereal day (≈23 hours and 56 minutes).

[0052] Experimental results and deception signal detection results in generative deception scenarios are as follows: Figure 3 As shown. In the experiment, the module collected carrier-to-noise ratio data at a rate of 1Hz and calculated the false alarm probability. Figure 3 The image shows the algorithm's results over approximately 1000 seconds, with each sub-image corresponding to the signal status of a single GPS satellite. The accumulated data for the true signal carrier-to-noise ratio over eight cycles is represented by a gray curve, while the spoofing signal carrier-to-noise ratio is represented by a colored curve. Pink marks indicate successful detection of the spoofing signal at that moment, while blue marks indicate failure. The results demonstrate that the satellite navigation spoofing interference detection method proposed in this application can effectively and stably identify spoofing signals that deviate from the true signal carrier-to-noise ratio pattern by 1 dB or more.

[0053] Figure 4 The paper further presents the trend of detection statistics changes for all deception channels after injection, under the aforementioned generative deception real-time injection scenario. The results demonstrate that the satellite navigation deception interference detection method proposed in this application can rapidly and effectively detect and identify deception signals that cause abnormal deviations in the carrier-to-noise ratio.

[0054] In summary, this invention acquires carrier-to-noise ratio (CNR) observation data and nadir point regression period from satellite signals. This allows for the effective capture of CNR signal variation patterns by leveraging the periodicity of satellite trajectories, avoiding random data interference. By combining CNR observation data with the CNR variation data, the differences in CNR variation are determined, accurately quantifying the deviation between actual observation and prediction. This makes the subsequent calculation of detection statistics more precise, better distinguishing between genuine abnormal signals and noise caused by natural data fluctuations. Furthermore, by comparing detection statistics and detection thresholds, abnormal signals can be quickly identified, reducing the risk of misjudgment and missed detection. This significantly enhances the user deception defense capability of satellite navigation, ensuring users can safely and reliably use the satellite navigation system.

[0055] The satellite navigation deception and interference detection device provided by the present invention is described below. The satellite navigation deception and interference detection device described below can be referred to in correspondence with the satellite navigation deception and interference detection method described above.

[0056] Figure 5 A schematic diagram of a satellite navigation spoofing interference detection device is shown. The device includes: The data acquisition module 51 acquires carrier-to-noise ratio (CNR) observation data and nadir point regression period from the satellite signal. The CNR observation data includes the CNR observation value corresponding to at least one observation point within the corresponding nadir point regression period. The nadir point regression period is used to characterize the time interval during which the nadir point trajectory of the satellite completely repeats when it is in orbit. The nadir point is used to characterize the projection point on the Earth's surface of the line connecting the satellite and the Earth's center. The pattern prediction module 52 obtains the historical carrier-to-noise ratio observation data of the corresponding satellite based on the carrier-to-noise ratio observation data and the nadir point regression period, and determines the carrier-to-noise ratio prediction change pattern. The detection statistics module 53 determines the difference in carrier-to-noise ratio change based on the predicted carrier-to-noise ratio change pattern and the observed carrier-to-noise ratio data, and determines the detection statistics for the corresponding satellite based on the predicted carrier-to-noise ratio change pattern. The interference determination module 54 determines that the corresponding satellite signal is a spoofing signal when the detection statistics of the satellite are greater than the previously acquired detection threshold.

[0057] In this embodiment, the data acquisition module 51 includes: a signal acquisition unit, which acquires satellite signals and obtains corresponding carrier-to-noise ratio (CNR) observation values ​​based on the satellite signals; and a smoothing processing unit, which uses a preset smoothing window to select the CNR observation values ​​and the predicted CNR change pattern for forward smoothing processing, and combines a second preset quantity to filter the smoothed CNR observation values ​​to obtain CNR observation data; wherein the second preset quantity is less than or equal to the length of the preset smoothing window.

[0058] In addition, the data acquisition module 51 also includes: an information acquisition unit for acquiring satellite ephemeris information and space signal interface control files, as well as acquiring the user's location; a location determination unit for determining the satellite's position vector in a geocentric inertial coordinate system at multiple discrete time points based on the satellite ephemeris information and using the orbit calculation algorithm configured in the space signal interface control file; a coordinate transformation unit for converting the satellite's position vector in a geocentric inertial coordinate system at multiple discrete time points into a position vector in a fixed coordinate system, and converting the position vectors in each fixed coordinate system into geographic coordinates to obtain the satellite's nadir point geographic coordinate sequence; and a period determination unit for obtaining the relative distance between each nadir point and the user's location based on the satellite's nadir point geographic coordinate sequence and the user's location, and determining the time interval for the nadir point trajectory to repeat within a preset range of the user's location to obtain the nadir point return period.

[0059] Further, the period determination unit includes: a relative distance determination subunit, which determines the great circle distance or central angle between each nadir point in the satellite's geographic coordinate sequence and the user's location, based on the satellite's nadir point geographic coordinate sequence and the user's location, to obtain the relative distance between each nadir point and the user's location; a time determination subunit, which determines the time corresponding to the local minimum based on the relative distance; a time difference determination subunit, which determines the time difference between adjacent local minimum times based on the times corresponding to all local minimum times, to obtain a time difference sequence; and a period determination subunit, which identifies the periodic, minimum repeating interval in the time difference sequence to obtain the nadir point regression period.

[0060] The pattern prediction module 52 includes: a time period determination unit, which determines the observation time period of the carrier-to-noise ratio (CNR) observation data relative to the corresponding nadir point regression period based on the carrier-to-noise ratio (CNR) observation data and the nadir point regression period; a historical data acquisition unit, which selects a first preset number of historical nadir point regression periods based on the nadir point regression period corresponding to the CNR observation data, and determines the historical CNR observation value of the historical observation time period corresponding to each historical nadir point regression period, thereby obtaining the historical CNR observation data corresponding to each historical nadir point regression period; and a pattern prediction unit, which determines the average CNR observation value corresponding to each observation point based on the historical CNR observation data corresponding to each historical nadir point regression period, thereby obtaining the predicted CNR change pattern.

[0061] The detection statistics module 53 includes: a difference determination unit, which determines the difference in carrier-to-noise ratio (CNR) changes based on the predicted CNR change pattern and the observed CNR data; a variance determination unit, which obtains the variance based on the predicted CNR change pattern; a covariance matrix determination unit, which constructs a covariance matrix based on the variance, a first preset quantity, the length of a preset smoothing window, and a second preset quantity; and a detection statistics unit, which obtains the detection statistics for the corresponding satellite based on the CNR change difference, the covariance matrix, and a preset constant vector; wherein, the preset constant vector is used to represent a vector in which all elements are the same preset constant.

[0062] In an optional embodiment, the interference determination module 54 is further configured to: determine that the corresponding satellite signal is a real signal when the detection statistic of the satellite is less than or equal to the previously acquired detection threshold, store it in the historical observation database and mark it as valid.

[0063] In an optional embodiment, the device further includes an alarm module that, after determining that the corresponding satellite signal is a spoofing signal, sends a spoofing alarm to the user.

[0064] In an optional embodiment, the device further includes: a probability acquisition module, which acquires a preset false alarm probability before determining that the detection statistic of the satellite is greater than a previously acquired detection threshold; a probability distribution module, which obtains a first probability distribution based on the preset false alarm probability using a central chi-square distribution with preset degrees of freedom; and a threshold determination module, which obtains a detection threshold based on the first probability distribution using the inverse function of the right-tailed function.

[0065] In summary, this invention acquires carrier-to-noise ratio (CNR) observation data and nadir point regression period from satellite signals. This allows for the effective capture of CNR signal variation patterns by leveraging the periodicity of satellite trajectories, avoiding random data interference. By combining CNR observation data with the CNR variation data, the differences in CNR variation are determined, accurately quantifying the deviation between actual observation and prediction. This makes the subsequent calculation of detection statistics more precise, better distinguishing between genuine abnormal signals and noise caused by natural data fluctuations. Furthermore, by comparing detection statistics and detection thresholds, abnormal signals can be quickly identified, reducing the risk of misjudgment and missed detection. This significantly enhances the user deception defense capability of satellite navigation, ensuring users can safely and reliably use the satellite navigation system.

[0066] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communications bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other through the communications bus 640. The processor 610 can call logic instructions in the memory 630 to execute a satellite navigation spoofing interference detection method. This method includes: acquiring carrier-to-noise ratio (CNR) observation data and nadir point regression period from a satellite signal; the CNR observation data includes CNR observation values ​​corresponding to at least one observation point within the nadir point regression period; wherein the nadir point regression period characterizes the time interval during which the nadir point trajectory completely repeats when the satellite is in orbit, and the nadir point characterizes the projection point of the line connecting the satellite and the Earth's center on the Earth's surface; acquiring historical CNR observation data for the corresponding satellite based on the CNR observation data and the nadir point regression period, and determining the predicted CNR variation pattern; determining the CNR variation difference based on the predicted CNR variation pattern and the CNR observation data, and determining the detection statistic for the corresponding satellite based on the predicted CNR variation pattern; and determining that the corresponding satellite signal is a spoofing signal when the satellite's detection statistic is greater than a previously acquired detection threshold.

[0067] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0068] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the satellite navigation spoofing interference detection method provided by the above methods. The method includes: acquiring carrier-to-noise ratio (CNR) observation data and nadir point regression period in the satellite signal; the CNR observation data includes the CNR observation value corresponding to at least one observation point within the corresponding nadir point regression period; wherein, the nadir point regression period is used to characterize the time interval during which the nadir point trajectory of the satellite completely repeats when it is running in orbit, and the nadir point is used to characterize the projection point of the line connecting the satellite and the Earth's center on the Earth's surface; acquiring historical CNR observation data of the corresponding satellite based on the CNR observation data and the nadir point regression period, and determining the CNR prediction change law; determining the CNR change difference based on the CNR prediction change law and the CNR observation data, and determining the detection statistic of the corresponding satellite in combination with the CNR prediction change law; and determining that the corresponding satellite signal is a spoofing signal when the detection statistic of the satellite is greater than a previously acquired detection threshold.

[0069] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program performs the satellite navigation spoofing interference detection method provided by the above methods. The method includes: acquiring carrier-to-noise ratio (CNR) observation data and nadir point regression period in a satellite signal. The CNR observation data includes the CNR observation value corresponding to at least one observation point within the corresponding nadir point regression period. The nadir point regression period characterizes the time interval during which the nadir point trajectory of the satellite completely repeats when it is in orbit, and the nadir point characterizes the projection point of the line connecting the satellite and the Earth's center on the Earth's surface. Based on the CNR observation data and the nadir point regression period, the present invention acquires historical CNR observation data of the corresponding satellite and determines the predicted CNR variation pattern. Based on the predicted CNR variation pattern and the CNR observation data, the present invention determines the CNR variation difference and, in conjunction with the predicted CNR variation pattern, determines the detection statistic of the corresponding satellite. When the detection statistic of the satellite is greater than a previously acquired detection threshold, the present invention determines that the corresponding satellite signal is a spoofing signal.

[0070] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. 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. Those skilled in the art can understand and implement this without any creative effort.

[0071] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting satellite navigation spoofing interference, characterized in that, include: The carrier-to-noise ratio (CNR) observation data and nadir point regression period are obtained from the satellite signal. The CNR observation data includes the CNR observation value corresponding to at least one observation point within the corresponding nadir point regression period. The nadir point regression period is used to characterize the time interval during which the nadir point trajectory of the satellite completely repeats when it is in orbit. The nadir point is used to characterize the projection point of the line connecting the satellite and the Earth's center on the Earth's surface. Based on the carrier-to-noise ratio observation data and the nadir point regression period, the historical carrier-to-noise ratio observation data of the corresponding satellite is obtained, and the carrier-to-noise ratio prediction variation pattern is determined. Based on the predicted carrier-to-noise ratio (CNR) variation pattern and the observed CNR data, the CNR variation differences are determined, and combined with the predicted CNR variation pattern, the detection statistics for the corresponding satellites are determined. When the detection statistics of the satellite are determined to be greater than the previously acquired detection threshold, the corresponding satellite signal is determined to be a spoofing signal. Based on the carrier-to-noise ratio (CNR) observation data and the nadir point regression period, historical CNR observation data for the corresponding satellite are obtained, and the predicted CNR variation pattern is determined, including: Based on the carrier-to-noise ratio observation data and the nadir point regression period, the observation time period of the carrier-to-noise ratio observation data relative to the corresponding nadir point regression period is determined; Based on the nadir point regression period corresponding to the carrier-to-noise ratio observation data, a first preset number of historical nadir point regression periods are selected, and the historical carrier-to-noise ratio observation value corresponding to the historical observation time period in each historical nadir point regression period is determined, so as to obtain the historical carrier-to-noise ratio observation data corresponding to each historical nadir point regression period. Based on the historical carrier-to-noise ratio (CNR) observation data corresponding to the historical sub-satellite point regression period, the average CNR observation value corresponding to each observation point is determined, and the CNR prediction variation law is obtained. Acquire carrier-to-noise ratio (CNR) observation data from satellite signals, including: Acquire satellite signals and obtain the corresponding carrier-to-noise ratio observation value based on the satellite signals; Using a preset smoothing window, the carrier-to-noise ratio (CNR) observations and the predicted CNR variation patterns are selected for forward smoothing. Combined with a second preset quantity, the smoothed CNR observations are filtered to obtain CNR observation data. The second preset quantity is less than or equal to the length of the preset smoothing window. The historical carrier-to-noise ratio (CNR) observation data for the corresponding satellite includes historical CNR observation data within a first preset number of historical nadir point regression periods; based on the predicted CNR variation pattern and the CNR observation data, the CNR variation difference is determined, and combined with the predicted CNR variation pattern, the detection statistics for the corresponding satellite are determined, including: Based on the predicted carrier-to-noise ratio (CNR) variation pattern and the observed CNR data, the CNR variation difference is determined. Based on the predicted variation law of the carrier-to-noise ratio, the variance is obtained; Construct a covariance matrix based on the variance, the first preset quantity, the length of the preset smoothing window, and the second preset quantity; Based on the difference in carrier-to-noise ratio, the covariance matrix, and the preset constant vector, the detection statistics of the corresponding satellite are obtained; wherein, the preset constant vector is used to characterize a vector in which all elements are the same preset constant.

2. The satellite navigation spoofing interference detection method according to claim 1, characterized in that, Before determining that the detection statistics of the satellite are greater than the previously acquired detection threshold, the process includes: Obtain the preset false alarm probability; Based on the preset false alarm probability, the first probability distribution is obtained using the central chi-square distribution with preset degrees of freedom; Based on the first probability distribution, the detection threshold is obtained using the inverse function of the right-tailed function.

3. The satellite navigation spoofing interference detection method according to claim 1, characterized in that, Obtain the sub-satellite point regression period, including: Acquire satellite ephemeris information and space signal interface control files, as well as obtain user location; Based on the satellite ephemeris information, the orbit calculation algorithm configured in the space signal interface control file is used to determine the position vector of the satellite in the geocentric inertial coordinate system at multiple discrete time points; The position vectors of the satellite in the geocentric inertial coordinate system at multiple discrete time points are converted into position vectors in the fixed coordinate system, and the position vectors in each of the fixed coordinate systems are converted into geographic coordinates to obtain the geographic coordinate sequence of the satellite's nadir point. Based on the geographic coordinate sequence of the satellite's nadir points and the user's location, the relative distance between each nadir point and the user's location is obtained, and the time interval at which the nadir point trajectory repeats within a preset range of the user's location is determined to obtain the nadir point regression cycle.

4. A satellite navigation spoofing interference detection device, characterized in that, include: The data acquisition module acquires carrier-to-noise ratio (CNR) observation data and nadir point regression period from the satellite signal. The CNR observation data includes the CNR observation value corresponding to at least one observation point within the corresponding nadir point regression period. The nadir point regression period is used to characterize the time interval during which the nadir point trajectory of the satellite completely repeats when it is in orbit. The nadir point is used to characterize the projection point of the line connecting the satellite and the Earth's center on the Earth's surface. The pattern prediction module obtains the historical carrier-to-noise ratio (CNR) observation data of the corresponding satellite based on the carrier-to-noise ratio (CNR) observation data and the nadir point regression period, and determines the CNR prediction change pattern. The detection and statistics module determines the difference in carrier-to-noise ratio change based on the predicted carrier-to-noise ratio change pattern and the observed carrier-to-noise ratio data, and determines the detection statistics for the corresponding satellite based on the predicted carrier-to-noise ratio change pattern. The interference determination module determines that when the detection statistics of the satellite are greater than the previously acquired detection threshold, the corresponding satellite signal is a spoofing signal. The pattern prediction module includes: The time period determination unit determines the observation time period of the carrier-to-noise ratio observation data relative to the corresponding nadir point regression period based on the carrier-to-noise ratio observation data and the nadir point regression period. The historical data acquisition unit selects a first preset number of historical nadir point regression periods based on the nadir point regression period corresponding to the carrier-to-noise ratio observation data, and determines the historical carrier-to-noise ratio observation value of the historical observation time period corresponding to the observation time period in each of the historical nadir point regression periods, thereby obtaining the historical carrier-to-noise ratio observation data corresponding to each of the historical nadir point regression periods. The pattern prediction unit determines the average observed value of the carrier noise ratio corresponding to each observation point based on the historical carrier noise ratio observation data corresponding to the regression period of each historical sub-satellite point, and obtains the predicted change pattern of the carrier noise ratio. The data acquisition module includes: The signal acquisition unit acquires satellite signals and obtains corresponding carrier-to-noise ratio observations based on the satellite signals. The smoothing unit uses a preset smoothing window to select the observed carrier-to-noise ratio (CNR) value and the predicted CNR change pattern for forward smoothing, and combines a second preset quantity to filter the smoothed CNR observed value to obtain CNR observed data; wherein, the second preset quantity is less than or equal to the length of the preset smoothing window. The historical carrier-to-noise ratio (CNR) observation data for the corresponding satellite includes historical CNR observation data within a first preset number of historical nadir point regression periods; the detection and statistics module includes: The difference determination unit determines the difference in carrier-to-noise ratio change based on the predicted carrier-to-noise ratio change pattern and the observed carrier-to-noise ratio data. The variance determination unit obtains the variance based on the predicted variation law of the carrier-to-noise ratio; The covariance matrix determination unit constructs a covariance matrix based on the variance, the first preset quantity, the length of the preset smoothing window, and the second preset quantity. The detection statistics unit obtains the detection statistics of the corresponding satellite based on the carrier-to-noise ratio variation difference, the covariance matrix, and the preset constant vector; wherein, the preset constant vector is used to characterize a vector in which all elements are the same preset constant.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the satellite navigation spoofing interference detection method as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the satellite navigation spoofing interference detection method as described in any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the satellite navigation spoofing interference detection method as described in any one of claims 1 to 3.

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