Satellite navigation deception jamming detection method, device, equipment, medium and program

Abstract

The invention provides a satellite navigation deception jamming detection method, device, equipment, medium and program, and the method comprises the steps: obtaining carrier-to-noise ratio observation data in a satellite signal and a sub-satellite point regression period, the carrier-to-noise ratio observation data comprising a carrier-to-noise ratio observation value corresponding to at least one observation point in the corresponding sub-satellite point regression period; acquiring historical carrier-to-noise ratio observation data of a corresponding satellite according to the carrier-to-noise ratio observation data and the sub-satellite point regression period, and determining a carrier-to-noise ratio estimation change rule; according to the carrier-to-noise ratio estimation change rule and the carrier-to-noise ratio observation data, determining a carrier-to-noise ratio change difference, and in combination with the carrier-to-noise ratio estimation change rule, determining detection statistics of a corresponding satellite; and when it is determined that the detection statistics of the satellite is greater than a detection threshold, determining the corresponding satellite signal as a deception signal. According to the method, abnormal signals can be quickly identified, the risk of misjudgment and missed judgment is reduced, the user deception defense capability of satellite navigation is remarkably improved, and it can be guaranteed that a user safely and reliably uses the satellite navigation system.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation technology, and in particular to a satellite navigation deception interference detection method, device, equipment, medium and program. Background Art

[0002] The Global Navigation Satellite System (GNSS) provides users with high-precision position, velocity, and time (PVT) information around the clock, in all weather conditions. It plays a vital role in positioning, navigation, and timing. It is widely used in critical infrastructure such as power, transportation, finance, and national defense, providing continuous and stable spatiotemporal information references and serving as a key driver of national security and industrial development. In recent years, the increasing sophistication of covert spoofing and jamming technologies has seriously threatened the security and reliability of GNSS services. GNSS spoofing and jamming exploit public signal structures to create false signals, broadcast at high power to suppress the real signal, and trick the target receiver into outputting erroneous positioning and timing results.

[0003] At present, satellite navigation interference detection technology is mainly based on spectrum detection technology. It relies on professional personnel from radio management agencies to use handheld professional equipment to determine whether there is interference and locate the source of interference based on the environmental spectrum and other information displayed by the equipment, so as to scan and investigate around key areas.

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

[0005] The present invention provides a satellite navigation deception interference detection method, device, equipment, medium and program to address the defects of the existing technology such as insufficient objectivity and poor detection accuracy, quickly identify abnormal signals, reduce the risks of misjudgment and missed judgment, significantly improve the user deception defense capability of satellite navigation, and ensure that users can use the satellite navigation system safely and reliably.

[0006] The present invention provides a method for detecting satellite navigation spoofing interference, comprising: obtaining carrier-to-noise ratio observation data and a sub-satellite point regression period in a satellite signal, the carrier-to-noise ratio observation data including a carrier-to-noise ratio observation value corresponding to at least one observation point within the sub-satellite point regression period; wherein the sub-satellite point regression period is used to represent the time interval within which the sub-satellite point trajectory of the satellite is completely repeated when the satellite is in orbit, and the sub-satellite point is used to represent the projection point on the earth's surface of a line connecting the satellite and the center of the earth; obtaining historical carrier-to-noise ratio observation data of the corresponding satellite based on the carrier-to-noise ratio observation data and the sub-satellite point regression period, and determining a carrier-to-noise ratio estimated change pattern; determining a carrier-to-noise ratio change difference based on the carrier-to-noise ratio estimated change pattern and the carrier-to-noise ratio observation data, and determining a detection statistic of the corresponding satellite in combination with the carrier-to-noise ratio estimated change 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 the present invention, a satellite navigation spoofing interference detection method is provided. Historical carrier-to-noise ratio observation data of a corresponding satellite is obtained based on carrier-to-noise ratio observation data and a sub-satellite point regression period, and a carrier-to-noise ratio estimated change pattern is determined. The method includes: determining, based on the carrier-to-noise ratio observation data and the sub-satellite point regression period, an observation time period of the carrier-to-noise ratio observation data relative to the corresponding sub-satellite point regression period; selecting a first preset number of historical sub-satellite point regression periods based on the sub-satellite point regression period corresponding to the carrier-to-noise ratio observation data, and determining historical carrier-to-noise ratio observation values ​​of historical observation time periods corresponding to the observation time periods in each historical sub-satellite point regression period to obtain historical carrier-to-noise ratio observation data corresponding to each historical sub-satellite point regression period; and determining, based on the historical carrier-to-noise ratio observation data corresponding to each historical sub-satellite point regression period, an average carrier-to-noise ratio observation value corresponding to each observation point to obtain a carrier-to-noise ratio estimated change pattern.

[0008] According to a satellite navigation spoofing interference detection method provided by the present invention, carrier-to-noise ratio observation data in a satellite signal is obtained, including: obtaining a satellite signal and obtaining a corresponding carrier-to-noise ratio observation value based on the satellite signal; using a preset smoothing window, selecting the carrier-to-noise ratio observation value and the carrier-to-noise ratio estimated change pattern for forward smoothing processing, and filtering the smoothed carrier-to-noise ratio observation value in combination with a second preset number to obtain carrier-to-noise ratio observation data; wherein the second preset number is less than or equal to the length of the preset smoothing window.

[0009] According to a satellite navigation deception interference detection method provided by the present invention, historical carrier-to-noise ratio observation data of a corresponding satellite includes historical carrier-to-noise ratio observation data within a first preset number of historical sub-satellite point regression periods; a carrier-to-noise ratio variation difference is determined based on an estimated carrier-to-noise ratio variation pattern and the carrier-to-noise ratio observation data, and a detection statistic of the corresponding satellite is determined in combination with the estimated carrier-to-noise ratio variation pattern, including: determining the carrier-to-noise ratio variation difference based on the estimated carrier-to-noise ratio variation pattern and the carrier-to-noise ratio observation data; obtaining a variance based on the estimated carrier-to-noise ratio variation pattern; constructing a covariance matrix based on the variance, a first preset number, a preset smoothing window length, and a second preset number; and obtaining a detection statistic of the corresponding satellite based on the carrier-to-noise ratio variation difference, the covariance matrix, and a preset constant vector; wherein the preset constant vector is used to represent a vector whose elements all have the same preset constant.

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

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

[0012] The present invention also provides a satellite navigation deception interference detection device, comprising: a data acquisition module, which acquires carrier-to-noise ratio observation data and a sub-satellite point regression period in a satellite signal, the carrier-to-noise ratio observation data including a carrier-to-noise ratio observation value corresponding to at least one observation point within the sub-satellite point regression period; wherein the sub-satellite point regression period is used to characterize the time interval for complete repetition of the sub-satellite point trajectory when the satellite is in orbit, and the sub-satellite point is used to characterize the projection point of the line connecting the satellite and the center of the earth on the surface of the earth; a law prediction module, which acquires historical carrier-to-noise ratio observation data of the corresponding satellite based on the carrier-to-noise ratio observation data and the sub-satellite point regression period, and determines a law of carrier-to-noise ratio estimation change; a detection statistics module, which determines a carrier-to-noise ratio variation difference based on the carrier-to-noise ratio estimation change law and the carrier-to-noise ratio observation data, and determines a detection statistic of the corresponding satellite in combination with the carrier-to-noise ratio estimation change law; and an interference determination module, which determines that the corresponding satellite signal is a deception signal when the detection statistic of the satellite is greater than a detection threshold obtained previously.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the satellite navigation deception interference detection method as described above is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the satellite navigation deception interference detection method as described in any one of the above is implemented.

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

[0016] The satellite navigation deception interference detection method, device, equipment, medium and program provided by the present invention obtain the carrier-to-noise ratio observation data and the sub-satellite point regression period in the satellite signal to utilize the periodic characteristics of the satellite trajectory to effectively capture the law of carrier-to-noise ratio signal changes and avoid random interference of the data. In combination with the carrier-to-noise ratio observation data, the carrier-to-noise ratio change difference is determined to accurately quantify the deviation between the actual observation and the prediction, so that the subsequent detection statistic calculation is more accurate, and can better distinguish between real abnormal signals and noise caused by natural fluctuations in the data. The detection statistic and the detection threshold are then compared to quickly identify abnormal signals, reduce the risk of misjudgment and missed judgment, significantly improve the user deception defense capability of satellite navigation, and ensure that users can use the satellite navigation system safely and reliably. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is one of the flow charts of the satellite navigation deception interference detection method provided by the present invention; Figure 2 This is the second flow chart of the satellite navigation deception interference detection method provided by the present invention; Figure 3 Schematic diagram of the carrier-to-noise ratios of various deceptive signals and their detection results in the generative deceptive scenario provided by the present invention; Figure 4 This is a schematic diagram of the changing trend of the detection statistics after the deceptive injection provided by the present invention; Figure 5 It is a structural diagram of the satellite navigation deception interference detection device provided by the present invention; Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] Figure 1 FIG. 1 is a flow chart of a satellite navigation deception interference detection method provided by the present invention, such as Figure 1 As shown, the method includes: S11, obtaining carrier-to-noise ratio observation data and a sub-satellite point regression period in the satellite signal, the carrier-to-noise ratio observation data including a carrier-to-noise ratio observation value corresponding to at least one observation point within the sub-satellite point regression period; wherein the sub-satellite point regression period is used to represent the time interval during which the sub-satellite point trajectory completely repeats when the satellite is in orbit, and the sub-satellite point is used to represent the projection point on the Earth's surface of a line connecting the satellite and the center of the Earth; S12, based on the carrier-to-noise ratio observation data and the sub-satellite point regression period, obtaining historical carrier-to-noise ratio observation data of the corresponding satellite and determining a change rule of the carrier-to-noise ratio estimate; S13, determining a carrier-to-noise ratio change difference based on the estimated carrier-to-noise ratio change pattern and the carrier-to-noise ratio observation data, and determining a detection statistic for the corresponding satellite in combination with the estimated carrier-to-noise ratio change pattern; S14: When it is determined that the detection statistic of the satellite is greater than the previously acquired detection threshold, it is determined that the corresponding satellite signal is a spoofing signal.

[0021] It should be noted that the step numbers "S1N" in this manual do not represent the order of the satellite navigation deception interference detection method. Figure 2 The satellite navigation deception interference detection method of the present invention is described.

[0022] Step S11, obtaining carrier-to-noise ratio observation data and a sub-satellite point regression period in the satellite signal, wherein the carrier-to-noise ratio observation data includes a carrier-to-noise ratio observation value corresponding to at least one observation point within the sub-satellite point regression period; wherein the sub-satellite point regression period is used to represent the time interval for which the sub-satellite point trajectory is completely repeated when the satellite is in orbit, and the sub-satellite point is used to represent the projection point on the Earth's surface of the line connecting the satellite and the center of the Earth.

[0023] In this embodiment, reference Figure 2 Obtaining carrier-to-noise ratio observation data from a satellite signal includes: obtaining a satellite signal and obtaining a corresponding carrier-to-noise ratio observation value based on the satellite signal; using a preset smoothing window, selecting the carrier-to-noise ratio observation value and a carrier-to-noise ratio estimated change rule for forward smoothing, and filtering the smoothed carrier-to-noise ratio observation values ​​in combination with a second preset number to obtain the carrier-to-noise ratio observation data; wherein the second preset number is less than or equal to a length of the preset smoothing window.

[0024] It should be noted that the method for determining the estimated change law of the carrier-to-noise ratio can be described below and will not be repeated here. In addition, when acquiring satellite signals, conventional satellite signal reception and processing, such as low-noise amplification, down-conversion, and analog-to-digital conversion, can be used, which is not further limited here. In addition, obtaining the sub-satellite point regression period includes: obtaining satellite ephemeris information and a space signal interface control file, and obtaining the user position; based on the satellite ephemeris information, using the orbit calculation algorithm configured in the space signal interface control file, determining the position vector of the satellite in the geocentric inertial coordinate system at multiple discrete time points; converting the position vector of the satellite in the geocentric inertial coordinate system at multiple discrete time points into a position vector in a fixed coordinate system, and converting the position vector in each fixed coordinate system into a geographic coordinate to obtain a sequence of geographic coordinates of the satellite's sub-satellite point; based on the satellite's sub-satellite point geographic coordinate sequence and the user's position, obtaining the relative distance between each sub-satellite point and the user's position, and determining the time interval at which the sub-satellite point trajectory recurs within a preset range of the user's position to obtain the sub-satellite point regression period.

[0025] It should be added that the satellite ephemeris information includes orbital inclination, right ascension of the ascending node, orbital eccentricity, argument of perigee, mean anomaly, mean motion and ephemeris time, among which the orbital inclination represents the angle between the orbital plane and the Earth's equatorial plane, the right ascension of the ascending node represents the projection of the intersection of the orbital plane and the Earth's equatorial plane on the equator, the orbital eccentricity represents the flatness of the orbital ellipse, the argument of perigee represents the angle between the orbital perigee and the ascending node, the mean anomaly represents the average position of the satellite in orbit, the mean motion represents the average period of the satellite orbiting the Earth, and the ephemeris time represents the timestamp of data generation; satellite ephemeris information can be obtained by demodulating the navigation message from the broadcast signal of the satellite navigation system, or obtaining the latest broadcast ephemeris data from the official website. The specific acquisition channel can be selected according to the actual acquisition needs, and no further limitation is made here.

[0026] In addition, the space signal 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 the Newton iteration method or a similar method, and is not further defined here.

[0027] Furthermore, based on the satellite's sub-satellite point geographic coordinate sequence and the user's position, the relative distance between each sub-satellite point and the user's position is obtained, and the time interval at which the sub-satellite point trajectory recurs within a preset range of the user's position is determined to obtain the sub-satellite point regression period, including: based on the satellite's sub-satellite point geographic coordinate sequence and the user's position, determining the great circle distance or central angle between each sub-satellite point in the sub-satellite point geographic coordinate sequence and the user's position to obtain the relative distance between each sub-satellite point and the user's position; determining the time corresponding to the local minimum value based on the relative distance; determining the time difference between adjacent local minimum value moments based on the time corresponding to all local minimum values ​​to obtain a time difference sequence; identifying the periodic and minimum repetition interval in the time difference sequence to obtain the sub-satellite point regression period.

[0028] It should be noted that the satellite ephemeris information provides accurate orbital parameters to determine the satellite's sub-satellite point trajectory, and the ICD file is used to ensure that the ground terminal correctly receives and processes satellite signals, thereby more comprehensively determining the satellite orbit.

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

[0030] Step S12: According to the carrier-to-noise ratio observation data and the sub-satellite point regression period, the historical carrier-to-noise ratio observation data of the corresponding satellite is obtained, and the estimated change rule of the carrier-to-noise ratio is determined.

[0031] In this embodiment, historical carrier-to-noise ratio observation data of a corresponding satellite is obtained based on the carrier-to-noise ratio observation data and the sub-satellite point regression period, and a carrier-to-noise ratio estimated change pattern is determined, including: determining, based on the carrier-to-noise ratio observation data and the sub-satellite point regression period, an observation time period of the carrier-to-noise ratio observation data relative to the corresponding sub-satellite point regression period; selecting a first preset number of historical sub-satellite point regression periods based on the sub-satellite point regression period corresponding to the carrier-to-noise ratio observation data, and determining historical carrier-to-noise ratio observation values ​​of historical observation time periods corresponding to the observation time periods in each historical sub-satellite point regression period, to obtain historical carrier-to-noise ratio observation data corresponding to each historical sub-satellite point regression period; and determining an average carrier-to-noise ratio observation value corresponding to each observation point based on the historical carrier-to-noise ratio observation data corresponding to each historical sub-satellite point regression period, to obtain a carrier-to-noise ratio estimated change pattern.

[0032] It should be noted that the sub-satellite point trajectory has a regression periodicity, which means that the satellite will pass through roughly the same position every other regression period. The carrier-to-noise ratio observations observed by users in the same geographical location in different regression periods will be affected by similar factors such as path loss, atmospheric attenuation, and multipath effects. Therefore, through the static GNSS user signal quality observation periodicity, historical carrier-to-noise ratio observation data of multiple historical regression periods are collected. By utilizing the previous carrier-to-noise ratio monitoring change pattern and comparing it with the real-time carrier-to-noise ratio monitored by GNSS users, the detection and identification of spoofing signals can be achieved, thereby "smoothing" these random disturbances and short-term fluctuations that are difficult to accurately predict to a certain extent, so that the estimation law can better reflect the long-term and macro-change trends.

[0033] In addition, the estimated change rule of the carrier-to-noise ratio is expressed as: in, Indicates the estimated change pattern of the carrier-to-noise ratio of the corresponding satellite; represents the average observed value of the carrier-to-noise ratio corresponding to the i-th observation point of the corresponding satellite; M represents the first preset number; It represents the carrier-to-noise ratio observation value of each observation point of the Mth sub-satellite point regression cycle, in decibel-Hertz (dB·Hz).

[0034] Step S13: determining the carrier-to-noise ratio variation difference based on the carrier-to-noise ratio estimated variation rule and the carrier-to-noise ratio observation data, and determining the detection statistic of the corresponding satellite in combination with the carrier-to-noise ratio estimated variation rule.

[0035] Specifically, the historical carrier-to-noise ratio observation data of the corresponding satellite includes historical carrier-to-noise ratio observation data within a first preset number of historical sub-satellite point regression cycles; the carrier-to-noise ratio change difference is determined based on the estimated carrier-to-noise ratio change law and the carrier-to-noise ratio observation data, and the detection statistic of the corresponding satellite is determined in combination with the estimated carrier-to-noise ratio change law, including: determining the carrier-to-noise ratio change difference based on the estimated carrier-to-noise ratio change law and the carrier-to-noise ratio observation data; obtaining the variance based on the estimated carrier-to-noise ratio change law; constructing a covariance matrix based on the variance, the first preset number, the length of the preset smoothing window and the second preset number; obtaining the detection statistic of the corresponding satellite based on the carrier-to-noise ratio change difference, the covariance matrix and the preset constant vector; wherein the preset constant vector is used to represent a vector whose elements are all 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 the time series sequence or can be arbitrarily extracted. The specific selection can be based on actual design requirements, and the first preset number can be set according to the data storage timeliness, computing power, etc. of the actual data storage system involved. For example, the first preset number can be 7~10, which is not further limited here.

[0037] In addition, by comparing the satellite's carrier-to-noise ratio observation data within a first preset number of historical sub-satellite point regression cycles with the corresponding carrier-to-noise ratio estimated change pattern, the carrier-to-noise ratio change difference and variance are determined to construct a covariance matrix. The covariance matrix is ​​used to characterize the correlation between observations at different time points to more accurately reflect the inherent structure of the data, making the subsequent detection statistic calculation more accurate and better able to distinguish between real abnormal signals and noise caused by natural fluctuations in the data.

[0038] It should be added that the test statistic is expressed as: in, represents the test statistic; Indicates the difference in carrier-to-noise ratio change, , represents the carrier-to-noise ratio observation data, Indicates the estimated change pattern of the carrier-to-noise ratio; Represents the covariance matrix The element in row i and column j in , Indicates the second preset quantity, The timeout period can be set based on real-time requirements, actual design requirements, or prior experience, for example, 5 seconds, 10 seconds, etc., and is not further limited here. represents the variance of the estimated variation law of the carrier-to-noise ratio of corresponding satellite k; Indicates a first preset quantity; Indicates the length of the preset smoothing window, It can be set according to actual design requirements or prior experience, such as 30 seconds, and is not further limited here; represents a preset constant vector, .

[0039] Step S14: When it is determined that the detection statistic of the satellite is greater than the previously acquired detection threshold, it is determined that the corresponding satellite signal is a spoofing signal.

[0040] In an optional embodiment, after determining that the corresponding satellite signal is a spoofing signal, the method includes: issuing a spoofing alert to the user.

[0041] In an optional embodiment, the method further includes: when it is determined that the detection statistic of the satellite is 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 a previously obtained detection threshold, the method includes: obtaining 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 an inverse function of a right-tail 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 estimation law changes slowly, the degree of freedom is usually set to 1. The corresponding detection threshold is expressed as: in, represents the detection threshold; represents the central chi-square distribution with 1 degree of freedom, i.e. the first probability distribution; Represents the inverse function of the right tail of the central chi-square distribution with 1 degree of freedom; 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 sub-satellite point regression period of each visible satellite is determined based on the satellite ephemeris information and carrier-to-noise ratio observations 10 days before the observation date (i.e., the first preset number) in combination with the space signal interface control file. In this example, taking the specific satellite identification code PRN 4 as an example, the satellite sub-satellite point regression period is .

[0045] Perform low-noise amplification, down-conversion, and analog-to-digital conversion on the received satellite signal to obtain a digital intermediate frequency signal; capture, track, and estimate the carrier-to-noise ratio of the digital intermediate frequency signal to extract the carrier-to-noise ratio observation value of each visible satellite signal. .

[0046] Set the second preset quantity , preset smoothing window length , carrier-to-noise ratio estimation period , using the past The carrier-to-noise ratio observation value of the satellite within the period is forward smoothed and filtered, and the carrier-to-noise ratio observation value after smoothing is filtered according to the second preset number, and the smoothing result is obtained. .

[0047] The satellite sub-satellite point regression period is T 4 , extract the corresponding historical carrier-to-noise ratio observation data, and determine the average carrier-to-noise ratio observation value corresponding to each observation point, and obtain the estimated change law of the carrier-to-noise ratio ,variance , and the covariance matrix as follows: According to the above method, the difference in carrier-to-noise ratio change is obtained , so the detection statistic of the corresponding satellite is In addition, take , and get the detection threshold .

[0048] Detection statistic Greater than the detection threshold , so PRN 4 is determined to be a spoofing signal. It can be seen that the above satellite navigation spoofing interference detection method can be effectively detected when the spoofing signal is injected with abnormal quality.

[0049] In an optional embodiment, to verify the effectiveness of the aforementioned approach, a generative spoofing experiment was conducted using the L1 carrier-to-noise (C / A) signal from the Global Navigation Satellite System (GPS). The specific test scenario included a spoofing source antenna, signal amplifier, spoofing source, receiving antenna, and processing terminal. The spoofing source recorded a spoofing signal generated by a simulator, which was then forwarded and used to estimate the C / A using a GNSS receiver (Ublox M8T). During the experiment, based on the timing of the collected spoofing signal and the orbital regression period of each satellite, the C / A pattern previously monitored by the local receiving antenna was compared with the observed value.

[0050] To mimic the operating mechanism of a traditional generative spoofing source, the spoofing source calculates pseudoranges based on known user locations and determines the relative power of the signals in each channel accordingly. To ensure that the power of the forwarded signal is slightly higher than the real signal so that it can be more effectively captured by the receiving antenna, an additional low-noise amplifier is used in the signal path. Furthermore, since the spoofing interference detection is based on the timing information of the received spoofing signal for comparison and verification, it effectively simulates a real-time spoofing interference scenario, enabling an in-depth evaluation of the performance of this application when subjected to such interference.

[0051] After receiving the signal processed by the RF front-end, the GNSS receiver performs baseband signal processing and outputs the carrier-to-noise ratio (CNR) of each satellite. When spoofing is injected, since the receiver's position, velocity, and time (PVT) data are completely controlled by the spoofing party, it compares the CNR data obtained by the receiver during the previous regression period based on the time of the recorded spoofing signal. For GPS, the regression period of each satellite is approximately one sidereal day (≈23 hours and 56 minutes).

[0052] The experimental results of generative deception scenarios and the results of deception signal detection are as follows: Figure 3 In the experiment, the module collects the carrier-to-noise ratio data at a rate of 1 Hz, and takes the false alarm probability as Figure 3 The figure shows the algorithm's results over approximately 1000 seconds, with each subgraph corresponding to the signal conditions of a single GPS satellite. The gray curve represents the carrier-to-noise ratio (CNR) of the true signal accumulated over the previous eight cycles, while the CNR of the spoofed signal is represented by a colored curve. Pink marks indicate successful detection of the spoofed signal at that moment, while blue marks indicate unsuccessful detection. These results demonstrate that the proposed satellite navigation spoofing jamming detection method can effectively and stably detect spoofed signals that deviate from the true CNR by 1dB or more.

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

[0054] In summary, the embodiments of the present invention obtain the carrier-to-noise ratio observation data and the sub-satellite point regression period in the satellite signal, so as to utilize the periodic characteristics of the satellite trajectory, effectively capture the law of the carrier-to-noise ratio signal change, avoid random interference of the data, and combine the carrier-to-noise ratio observation data to determine the carrier-to-noise ratio change difference to accurately quantify the deviation between the actual observation and the prediction, so that the subsequent detection statistic calculation is more accurate, and can better distinguish between real abnormal signals and noise caused by natural fluctuations in the data, and then compare the detection statistics and the detection threshold to quickly identify abnormal signals, reduce the risk of misjudgment and missed judgment, significantly improve the user deception defense capability of satellite navigation, and ensure that users can use the satellite navigation system safely and reliably.

[0055] The satellite navigation deception interference detection device provided by the present invention is described below. The satellite navigation deception interference detection device described below and the satellite navigation deception interference detection method described above can be referenced to each other.

[0056] Figure 5 A schematic diagram of the structure of a satellite navigation deception interference detection device is shown, which includes: A data acquisition module 51 acquires carrier-to-noise ratio observation data and a sub-satellite point regression period from satellite signals. The carrier-to-noise ratio observation data includes a carrier-to-noise ratio observation value corresponding to at least one observation point within the sub-satellite point regression period. The sub-satellite point regression period represents the time interval during which the sub-satellite point trajectory of the satellite in orbit completely repeats. The sub-satellite point represents the projection point on the Earth's surface of a line connecting the satellite and the center of the Earth. The law estimation 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 sub-satellite point regression period, and determines the carrier-to-noise ratio estimation change law; The detection statistics module 53 determines the difference in carrier-to-noise ratio variation based on the estimated carrier-to-noise ratio variation pattern and the carrier-to-noise ratio observation data, and determines the detection statistics of the corresponding satellite in combination with the estimated carrier-to-noise ratio variation pattern; The interference determination module 54 determines that the corresponding satellite signal is a spoofing signal when it determines that the detection statistic of the satellite is greater than the previously acquired detection threshold.

[0057] In this embodiment, the data acquisition module 51 includes: a signal acquisition unit that acquires satellite signals and obtains corresponding carrier-to-noise ratio observations based on the satellite signals; a smoothing processing unit that uses a preset smoothing window to select the carrier-to-noise ratio observations and the carrier-to-noise ratio estimated change pattern for forward smoothing, and filters the smoothed carrier-to-noise ratio observations based on a second preset number to obtain carrier-to-noise ratio observation data; wherein the second preset number 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, which acquires satellite ephemeris information and a space signal interface control file, and acquires the user position; a position determination unit, which determines the position vector of the satellite in the geocentric inertial coordinate system at multiple discrete time points based on the satellite ephemeris information and the orbit calculation algorithm configured in the space signal interface control file; a coordinate conversion unit, which converts the position vector of the satellite in the geocentric inertial coordinate system at multiple discrete time points into a position vector in a fixed coordinate system, and converts the position vector in each fixed coordinate system into a geographic coordinate to obtain a sequence of geographic coordinates of the satellite's sub-satellite point; a period determination unit, which obtains the relative distance between each sub-satellite point and the user position based on the sequence of geographic coordinates of the satellite's sub-satellite point and the user position, and determines the time interval for the sub-satellite point trajectory to recur within a preset range of the user position to obtain a sub-satellite point regression period.

[0059] Furthermore, the period determination unit includes: a relative distance determination subunit, which determines the great circle distance or central angle between each sub-satellite point in the sub-satellite point geographic coordinate sequence and the user position according to the satellite's sub-satellite point geographic coordinate sequence and the user position, and obtains the relative distance between each sub-satellite point and the user position; a time determination subunit, which determines the time corresponding to the local minimum value according to the relative distance; a time difference determination subunit, which determines the time difference between adjacent local minimum moments according to the times corresponding to all local minimum values, and obtains a time difference sequence; and a period determination subunit, which identifies the periodic and minimum repetition interval in the time difference sequence, and obtains the sub-satellite point regression period.

[0060] The pattern estimation module 52 includes: a time period determination unit, which determines, based on the carrier-to-noise ratio observation data and the sub-satellite point regression period, an observation time period of the carrier-to-noise ratio observation data relative to the corresponding sub-satellite point regression period; a historical data acquisition unit, which selects a first preset number of historical sub-satellite point regression periods based on the sub-satellite point regression period corresponding to the carrier-to-noise ratio observation data, and determines historical carrier-to-noise ratio observation values ​​of historical observation time periods corresponding to the observation time periods in each historical sub-satellite point regression period, thereby obtaining historical carrier-to-noise ratio observation data corresponding to each historical sub-satellite point regression period; and a pattern estimation unit, which determines, based on the historical carrier-to-noise ratio observation data corresponding to each historical sub-satellite point regression period, an average carrier-to-noise ratio observation value corresponding to each observation point, thereby obtaining a pattern of carrier-to-noise ratio variation estimation.

[0061] The detection statistics module 53 includes: a difference determination unit, which determines the carrier-to-noise ratio change difference based on the estimated carrier-to-noise ratio change law and the carrier-to-noise ratio observation data; a variance determination unit, which obtains the variance based on the estimated carrier-to-noise ratio change law; a covariance matrix determination unit, which constructs the covariance matrix based on the variance, a first preset number, the length of the preset smoothing window and a second preset number; and a detection statistics unit, which obtains the detection statistics of the corresponding satellite based on the carrier-to-noise ratio change difference, the covariance matrix and the preset constant vector; wherein the preset constant vector is used to represent a vector whose elements are all the same preset constant.

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

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

[0064] In an optional embodiment, the device further includes: a probability acquisition module, which obtains a preset false alarm probability before determining that the detection statistic of the satellite is greater than a detection threshold obtained previously; 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 an inverse function of a right-tail function.

[0065] In summary, the embodiments of the present invention obtain the carrier-to-noise ratio observation data and the sub-satellite point regression period in the satellite signal, so as to utilize the periodic characteristics of the satellite trajectory, effectively capture the law of the carrier-to-noise ratio signal change, avoid random interference of the data, and combine the carrier-to-noise ratio observation data to determine the carrier-to-noise ratio change difference to accurately quantify the deviation between the actual observation and the prediction, so that the subsequent detection statistic calculation is more accurate, and can better distinguish between real abnormal signals and noise caused by natural fluctuations in the data, and then compare the detection statistics and the detection threshold to quickly identify abnormal signals, reduce the risk of misjudgment and missed judgment, significantly improve the user deception defense capability of satellite navigation, and ensure that users can use the satellite navigation system safely and reliably.

[0066] Figure 6 An example of a physical structure diagram of an electronic device is shown below. Figure 6As shown, the electronic device may include: a processor 610 , a communications interface 620 , a memory 630 and a communication bus 640 , wherein the processor 610 , the communications interface 620 and the memory 630 communicate with each other via the communication bus 640 . The processor 610 can call logic instructions in the memory 630 to execute a satellite navigation spoofing interference detection method, which includes: obtaining carrier-to-noise ratio observation data and a sub-satellite point regression period in a satellite signal, the carrier-to-noise ratio observation data including a carrier-to-noise ratio observation value corresponding to at least one observation point within the sub-satellite point regression period; wherein the sub-satellite point regression period is used to represent the time interval in which the sub-satellite point trajectory of the satellite is completely repeated when the satellite is in orbit, and the sub-satellite point is used to represent the projection point on the Earth's surface of a line connecting the satellite and the center of the Earth; based on the carrier-to-noise ratio observation data and the sub-satellite point regression period, obtaining historical carrier-to-noise ratio observation data of the corresponding satellite and determining an estimated carrier-to-noise ratio change pattern; determining a carrier-to-noise ratio change difference based on the estimated carrier-to-noise ratio change pattern and the carrier-to-noise ratio observation data, and determining a detection statistic for the corresponding satellite in combination with the estimated carrier-to-noise ratio change pattern; and determining that the corresponding satellite signal is a spoofing signal when the detection statistic of the satellite is greater than a previously obtained detection threshold.

[0067] Furthermore, the logic 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, or the portion that contributes to the prior art, or a portion 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0068] On the other hand, the present invention also provides a computer program product, comprising 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-mentioned methods, the method comprising: obtaining carrier-to-noise ratio observation data and a sub-satellite point regression period in a satellite signal, the carrier-to-noise ratio observation data comprising a carrier-to-noise ratio observation value corresponding to at least one observation point within the sub-satellite point regression period; wherein the sub-satellite point regression period is used to represent the time interval within which the sub-satellite point trajectory of the satellite is completely repeated when the satellite is in orbit, and the sub-satellite point is used to represent the projection point on the Earth's surface of a line connecting the satellite and the center of the Earth; based on the carrier-to-noise ratio observation data and the sub-satellite point regression period, obtaining historical carrier-to-noise ratio observation data of the corresponding satellite and determining an estimated carrier-to-noise ratio change pattern; determining a carrier-to-noise ratio change difference based on the estimated carrier-to-noise ratio change pattern and the carrier-to-noise ratio observation data, and determining a detection statistic for the corresponding satellite in combination with the estimated carrier-to-noise ratio change pattern; and determining that the corresponding satellite signal is a spoofing signal when the detection statistic of the satellite is greater than a previously obtained detection threshold.

[0069] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the satellite navigation spoofing interference detection method provided by the above-mentioned methods, the method comprising: obtaining carrier-to-noise ratio observation data and a sub-satellite point regression period in a satellite signal, the carrier-to-noise ratio observation data comprising a carrier-to-noise ratio observation value corresponding to at least one observation point within the sub-satellite point regression period; wherein the sub-satellite point regression period is used to represent the time interval in which the sub-satellite point trajectory is completely repeated when the satellite is in orbit, and the sub-satellite point is used to represent the projection point on the earth's surface of a line connecting the satellite and the center of the earth; based on the carrier-to-noise ratio observation data and the sub-satellite point regression period, obtaining historical carrier-to-noise ratio observation data of the corresponding satellite, and determining an estimated carrier-to-noise ratio change law; based on the estimated carrier-to-noise ratio change law and the carrier-to-noise ratio observation data, determining a carrier-to-noise ratio change difference, and determining a detection statistic of the corresponding satellite in combination with the estimated carrier-to-noise ratio 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 obtained detection threshold.

[0070] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0071] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion 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, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions 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, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for detecting satellite navigation deception interference, characterized in that: include: Obtaining carrier-to-noise ratio observation data and a sub-satellite point regression period from a satellite signal, the carrier-to-noise ratio observation data including a carrier-to-noise ratio observation value corresponding to at least one observation point within the sub-satellite point regression period; wherein the sub-satellite point regression period represents the time interval within which the sub-satellite point trajectory completely repeats when the satellite is in orbit, and the sub-satellite point represents the projection point on the Earth's surface of a line connecting the satellite and the center of the Earth; According to the carrier-to-noise ratio observation data and the sub-satellite point regression period, historical carrier-to-noise ratio observation data of the corresponding satellite is obtained, and a carrier-to-noise ratio estimated change rule is determined; Determining a carrier-to-noise ratio variation difference based on the estimated carrier-to-noise ratio variation law and the carrier-to-noise ratio observation data, and determining a detection statistic corresponding to the satellite in combination with the estimated carrier-to-noise ratio variation law; When it is determined that the detection statistic of the satellite is greater than a previously acquired detection threshold, it is determined that the corresponding satellite signal is a spoofing signal.

2. The satellite navigation deception interference detection method according to claim 1, characterized in that: According to the carrier-to-noise ratio observation data and the sub-satellite point regression period, historical carrier-to-noise ratio observation data of the corresponding satellite is obtained, and a carrier-to-noise ratio estimated change rule is determined, including: determining, according to the carrier-to-noise ratio observation data and the sub-satellite point regression period, an observation time period of the carrier-to-noise ratio observation data relative to the corresponding sub-satellite point regression period; selecting a first preset number of historical sub-satellite point regression cycles based on the sub-satellite point regression cycle corresponding to the carrier-to-noise ratio observation data, and determining historical carrier-to-noise ratio observation values ​​of historical observation time periods corresponding to the observation time period in each of the historical sub-satellite point regression cycles, to obtain historical carrier-to-noise ratio observation data corresponding to each of the historical sub-satellite point regression cycles; According to the historical carrier-to-noise ratio observation data corresponding to the regression period of each historical sub-satellite point, the average carrier-to-noise ratio observation value corresponding to each observation point is determined, and the estimated change law of the carrier-to-noise ratio is obtained.

3. The satellite navigation deception interference detection method according to claim 2, characterized in that: Obtain carrier-to-noise ratio observation data from satellite signals, including: Acquire satellite signals, and obtain corresponding carrier-to-noise ratio observation values ​​based on the satellite signals; Using a preset smoothing window, the carrier-to-noise ratio observation value and the carrier-to-noise ratio estimated change law are selected for forward smoothing, and the smoothed carrier-to-noise ratio observation value is filtered in combination with a second preset number to obtain carrier-to-noise ratio observation data; wherein the second preset number is less than or equal to the length of the preset smoothing window.

4. The satellite navigation deception interference detection method according to claim 3, characterized in that: The historical carrier-to-noise ratio observation data of the corresponding satellite includes historical carrier-to-noise ratio observation data within a first preset number of historical sub-satellite point regression periods; determining a carrier-to-noise ratio change difference based on the estimated carrier-to-noise ratio change pattern and the carrier-to-noise ratio observation data, and determining a detection statistic of the corresponding satellite in combination with the estimated carrier-to-noise ratio change pattern, including: determining a carrier-to-noise ratio change difference based on the estimated carrier-to-noise ratio change rule and the carrier-to-noise ratio observation data; Obtaining a variance according to the estimated variation rule of the carrier-to-noise ratio; Constructing a covariance matrix according to the variance, the first preset number, the length of the preset smoothing window, and the second preset number; A detection statistic of the corresponding satellite is obtained according to the carrier-to-noise ratio change difference, the covariance matrix and a preset constant vector; wherein the preset constant vector is used to represent a vector whose elements are all the same preset constants.

5. The satellite navigation deception interference detection method according to claim 1, characterized in that: Before determining that the detection statistic of the satellite is greater than a previously acquired detection threshold, the method further includes: Get the preset false alarm probability; According to the preset false alarm probability, a first probability distribution is obtained using a central chi-square distribution with preset degrees of freedom; According to the first probability distribution, the detection threshold is obtained by using the inverse function of the right tail function.

6. The satellite navigation deception interference detection method according to claim 1, characterized in that: Get the sub-satellite point return period, including: Obtain satellite ephemeris information and space signal interface control files, as well as user location; Determine, based on the satellite ephemeris information, the position vector of the satellite in the geocentric inertial coordinate system at multiple discrete time points using the orbit calculation algorithm configured in the space signal interface control file; Converting the position vector of the satellite in the geocentric inertial coordinate system at multiple discrete time points into a position vector in a fixed coordinate system, and converting each position vector in the fixed coordinate system into a geographic coordinate to obtain a sequence of geographic coordinates of the sub-satellite point of the satellite; According to the geographic coordinate sequence of the sub-satellite points of the satellite and the user position, the relative distance between each sub-satellite point and the user position is obtained, and the time interval at which the sub-satellite point trajectory recurs within a preset range of the user position is determined to obtain the sub-satellite point regression period.

7. A satellite navigation deception interference detection device, characterized in that: include: a data acquisition module for acquiring carrier-to-noise ratio observation data and a sub-satellite point regression period from satellite signals, wherein the carrier-to-noise ratio observation data includes a carrier-to-noise ratio observation value corresponding to at least one observation point within the sub-satellite point regression period; wherein the sub-satellite point regression period represents the time interval during which the sub-satellite point trajectory completely repeats when the satellite is in orbit, and the sub-satellite point represents the projection point on the Earth's surface of a line connecting the satellite and the center of the Earth; a law prediction module, which obtains historical carrier-to-noise ratio observation data of the corresponding satellite based on the carrier-to-noise ratio observation data and the sub-satellite point regression period, and determines a law of carrier-to-noise ratio estimation change; a detection statistics module, which determines a carrier-to-noise ratio change difference based on the carrier-to-noise ratio estimated change law and the carrier-to-noise ratio observation data, and determines a detection statistic for the corresponding satellite in combination with the carrier-to-noise ratio estimated change law; The interference determination module determines that the corresponding satellite signal is a spoofing signal when it determines that the detection statistic of the satellite is greater than a previously acquired detection threshold.

8. 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, the satellite navigation deception interference detection method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the satellite navigation deception interference detection method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the satellite navigation deception interference detection method according to any one of claims 1 to 6 is implemented.

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