A GNSS anti-spoofing detection method and system based on multi-dimensional consistency verification

Abstract

This invention discloses a GNSS anti-spoofing detection method and system based on multi-dimensional consistency verification, belonging to the field of satellite navigation technology. The method includes: S1. Obtaining pseudorange observations and carrier phase observations output by the global navigation satellite system at the current detection epoch; S2. Constructing a core detection quantity for each valid satellite frequency point, wherein the constructed core detection quantity is used to characterize the consistency between the pseudorange observations and the carrier phase observations; S3. Performing multi-dimensional consistency verification synchronously based on the core detection quantity; S4. Calculating a comprehensive risk score based on the trigger statistics of the multi-dimensional consistency verification, and determining the system status based on the comprehensive risk score; S5. Outputting the determination result and updating the historical status, and entering the next detection epoch. This invention constructs a "pseudorange-reconstructed phase distance difference" as a detection benchmark, and performs multi-level consistency verification in the spatial and temporal dimensions based on this benchmark, ultimately achieving reliable identification and alarm of spoofing signals through a risk assessment model.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation technology, and in particular to a GNSS anti-spoofing detection method and system based on multi-dimensional consistency verification. Background Technology

[0002] Global Navigation Satellite System (GNSS) has been widely used in critical infrastructure fields such as unmanned aerial vehicle (UAV) navigation, intelligent transportation, power grid synchronization, communication timing, and surveying and positioning. As GNSS applications develop towards higher precision and higher reliability, the security risks they face are becoming increasingly prominent.

[0003] GNSS spoofing jamming is an attack method that induces receivers to lock onto false signals and output incorrect positioning, velocity, or time information by transmitting counterfeit signals that are highly similar to real satellite signals in terms of structure, modulation, and navigation messages. Compared with traditional suppression jamming, generative spoofing is characterized by low power, high stealth, and the ability to gradually take over receivers, making it particularly difficult to detect directly in civilian and low-cost GNSS receiving equipment.

[0004] The common GNSS anti-spoofing detection methods and their shortcomings are as follows: 1. Detection methods based on single observations lack multi-dimensional physical constraints, making it difficult to identify advanced coherent spoofing: Traditional anti-spoofing algorithms often rely on signal power monitoring or pseudorange jump detection at a single frequency point, relying solely on changes in signal power, carrier-to-noise ratio, or single pseudorange anomalies for detection. While this method is effective against power-abrupt or coarse spoofing, it often struggles to effectively distinguish between real and false signals for generative spoofing signals that can smoothly adjust pseudorange or frequency parameters. This is because such methods do not fully utilize the inherent coupling relationship between pseudorange and carrier phase and other observations in geometric distance evolution, lacking multi-dimensional, cross-frequency physical consistency constraints, resulting in insufficient detection dimensions and a high risk of misjudgment.

[0005] 2. Carrier phase-based methods rely on a single criterion and are susceptible to cycle slips and environmental disturbances: Some existing technologies introduce carrier phase information for deception detection, but these mostly focus on single criteria such as cycle slip detection or phase change identification. When the receiver is in a dynamic environment, an obstructed environment, or a scenario with fluctuating signal quality, the real signal may also exhibit carrier phase discontinuities, leading to false alarms. Furthermore, relying solely on carrier phase changes without combining pseudorange observations for joint analysis makes it difficult to distinguish between natural variations caused by ionospheric disturbances and receiver oscillator noise and coherence disruptions caused by deception signals, thus limiting the stability and reliability of the detection results. 3. Existing technologies lack cross-frequency geometric consistency verification mechanisms, making it difficult to counter multi-frequency generative spoofing: With the widespread adoption of multi-frequency GNSS receivers, spoofing attacks are increasingly showing a trend of multi-frequency collaborative development. However, some existing detection methods only analyze a single frequency independently, without jointly constraining the observation results of the same satellite at different frequencies. Since pseudorange and carrier phase observations at different frequencies should correspond to the same geometric distance in the real signal, without a cross-frequency consistency verification mechanism, attackers can bypass single-frequency detection strategies and reduce detection sensitivity by introducing small but systematic phase or range deviations at different frequencies.

[0006] 4. Existing methods often employ hard thresholds for instantaneous decision-making, resulting in insufficient noise resistance and engineering robustness: Some existing technologies use a single threshold for instantaneous decision-making during the detection process, classifying a measurement as deceptive once it exceeds the threshold. These methods are prone to false alarms due to sporadic anomalies in high noise levels or rapidly changing signal environments, and lack the ability to comprehensively evaluate the persistence and proportionality of anomalies. Furthermore, some algorithms fail to differentiate between the temporal continuity of the observed data, making them susceptible to continuous misjudgments due to historical state remnants during short signal interruptions or recovery, thus failing to meet the stability and reliability requirements of embedded real-time systems.

[0007] Therefore, existing technologies suffer from problems such as limited detection dimensions, poor environmental adaptability, lack of cross-frequency verification, and insufficient robustness in decision-making. There is an urgent need for a GNSS anti-spoofing detection method that can achieve multi-dimensional, high-sensitivity, and high-robustness on an embedded platform. Summary of the Invention

[0008] The purpose of this invention is to overcome the problems existing in the prior art and provide a GNSS anti-spoofing detection method and system based on multi-dimensional consistency verification. By constructing the "pseudorange-reconstructed phase distance difference" as the detection benchmark, and performing multi-level consistency verification in the spatial and temporal dimensions based on the benchmark, the reliable identification and alarm of spoofing signals are finally achieved through a risk assessment model.

[0009] The objective of this invention is achieved through the following technical solution: Firstly, a GNSS anti-spoofing detection method based on multi-dimensional consistency verification is provided, including the following steps: S1. Obtain the pseudorange observation and carrier phase observation values ​​output by the global navigation satellite system at the current detection epoch; the carrier phase observation values ​​include integer ambiguity; S2. Construct a core detection quantity for each valid satellite frequency point, wherein the constructed core detection quantity is used to characterize the consistency between pseudorange observations and carrier phase observations; S3. Based on the core detection quantity, perform multi-dimensional consistency verification simultaneously. The multi-dimensional consistency verification includes single-frequency point absolute quantity verification, cross-frequency point relative quantity verification, inter-epoch same-frequency point continuity verification, and inter-epoch cross-frequency point consistency evolution verification. S4. Calculate the comprehensive risk score based on the trigger statistics of the multi-dimensional consistency verification, and determine the system status based on the comprehensive risk score; S5. Output the judgment result and update the historical status, then proceed to the next detection epoch.

[0010] In some embodiments, constructing the core detection quantity for each valid satellite frequency point includes: The core detection quantity is calculated using the following formula: Among them, the Indicates satellite At frequency The core testing volume at the site. Indicates satellite At frequency The pseudorange observation at that location, Frequency point The corresponding carrier wavelength, Indicates satellite At frequency The carrier phase value at that location.

[0011] In some embodiments, the single-frequency point absolute quantity test includes: Determine whether the absolute value of the core detection quantity at the same frequency point exceeds the preset threshold.

[0012] In some embodiments, the cross-frequency point relative quantity test includes: Compare whether the differences in core detection quantities between different frequency points of the same satellite satisfy geometric consistency constraints.

[0013] In some embodiments, the inter-epoch frequency continuity test includes: Detect whether the changes in the core detection quantity at the same frequency point are abnormal between adjacent epochs.

[0014] In some embodiments, the inter-epoch cross-frequency consistency evolution check includes: To track whether the difference in core detection quantities between different frequency points of the same satellite remains stable over consecutive epochs.

[0015] In some embodiments, the comprehensive risk score is calculated using the following formula: in, k This represents the dimension used for multi-dimensional consistency verification. This indicates the number of times a certain dimension's validation is triggered. This represents the total number of validations for a certain dimension. This indicates the weight of the validation for a certain dimension.

[0016] In some embodiments, determining the system status based on the comprehensive risk score includes: The comprehensive risk score is linearly mapped to the integer range of 0-10 to obtain the mapped comprehensive risk score; The system status is determined as safe, suspicious, or fraudulent based on the mapped comprehensive risk score.

[0017] Secondly, a GNSS anti-spoofing detection system based on multi-dimensional consistency verification is provided, including: GNSS antenna, used to receive multi-system, multi-band GNSS signals; A GNSS receiver module, connected to the GNSS antenna, is used to acquire pseudorange and carrier phase observations output by the global navigation satellite system. The main control processing unit, connected to the GNSS receiver module, is configured to execute the method described in the first aspect; The communication display unit is connected to the main control processor and is used to output detection results and alarm information.

[0018] In some embodiments, the main control processing unit includes an STM32F407 series microcontroller; it also includes an antenna short-circuit protection circuit connected between the GNSS antenna and the GNSS receiver module.

[0019] It should be further noted that the technical features corresponding to the above-mentioned options and embodiments can be combined or substituted with each other to form new technical solutions without conflict.

[0020] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs a core detection quantity for each effective satellite frequency point, establishing a new detection benchmark based on the "pseudorange-reconstructed phase distance difference," fundamentally improving the detection sensitivity and reliability against advanced spoofing. Instead of using the traditional pseudorange-carrier phase combination quantity affected by unresolved ambiguity, this invention creatively defines a core detection quantity. This core detection quantity directly utilizes and adapts to the output characteristics of high-precision GNSS receivers such as the UM982. By taking the absolute value of the carrier phase, which has already been processed and contains ambiguity information within the receiver, it converts it into a reconstructed phase distance that can be directly compared with the pseudorange. This design fully leverages the advanced processing capabilities of hardware, enabling the core detection quantity to naturally possess ideal statistical characteristics of zero expectation value and low noise under spoofing conditions. This establishes a high-sensitivity benchmark for subsequent detection, fundamentally overcoming the shortcomings of traditional methods such as detection lag and low reliability.

[0021] 2. The method of this invention performs multi-dimensional consistency verification based on core detection quantities, constructing a multi-dimensional in-depth verification system of "single-point-cross-frequency-time domain," achieving systematic capture of multi-dimensional physical flaws in deception signals. Specifically, this invention designs a progressive comprehensive verification process: capturing significant deviations through single-frequency point absolute quantity verification, identifying geometric consistency violations through cross-frequency point relative quantity verification, and revealing time-domain jumps and slow coherent deception through epoch continuity and cross-frequency evolution verification. This constitutes a three-dimensional defense network, making it difficult for deception signals to simultaneously satisfy physical constraints in all dimensions, significantly improving the recognition rate and dimensionality of complex deception attacks.

[0022] 3. The method of this invention employs a risk fusion strategy combining dynamic weighting and continuous analysis, greatly enhancing the system's engineering robustness and environmental adaptability. In the decision-making stage, this invention abandons simple hard threshold instantaneous decision-making and introduces a comprehensive risk assessment model based on technical weights and time persistence. This model can effectively distinguish between occasional anomalies and systemic attacks, significantly reducing the false alarm rate in complex environments, while ensuring that genuine deception attacks can be reliably alerted, giving the system strong engineering practicality and environmental robustness.

[0023] 4. This invention forms a complete, lightweight, and real-time embedded solution, fully leveraging the synergistic effect of hardware and software. The entire method has a clear flow, low computational complexity, and can be fully integrated into mainstream embedded platforms (such as STM32 series MCUs). Through deep collaborative design between the algorithm and a high-precision GNSS receiver (such as UM982), the system achieves real-time online processing of the entire process from data acquisition and processing to alarm output under low-cost resource constraints. It features low power consumption and small size, providing built-in, highly reliable security protection capabilities for terminals such as drones and vehicle navigation systems. Attached Figure Description

[0024] Figure 1 This is a flowchart of a GNSS anti-spoofing detection method based on multi-dimensional consistency verification according to the present invention. Figure 2 This is a schematic diagram of a GNSS anti-spoofing detection system based on multi-dimensional consistency verification according to the present invention; Figure 3 This is a schematic diagram of the power management principle of the present invention; Figure 4 This is a schematic diagram of the dual-antenna protection circuit of the present invention; Figure 5 This is a schematic diagram of the Ethernet interface of the present invention. Detailed Implementation

[0025] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It should be noted that the defects in the solutions in the prior art are all the results of the inventors' practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of this application in the following text should be the inventors' contributions to this application in the process of invention and creation, and should not be understood as technical content known to those skilled in the art.

[0027] In view of the technical problems pointed out in the background art, the present invention provides the following embodiments: In one exemplary embodiment, a GNSS anti-spoofing detection method based on multi-dimensional consistency verification is provided, such as... Figure 1 As shown, it includes the following steps: S1. Obtain the pseudorange observation and carrier phase observation values ​​output by the global navigation satellite system at the current detection epoch; the carrier phase observation values ​​include integer ambiguity; S2. Construct a core detection quantity for each valid satellite frequency point, wherein the constructed core detection quantity is used to characterize the consistency between pseudorange observations and carrier phase observations; S3. Based on the core detection quantity, perform multi-dimensional consistency verification simultaneously. The multi-dimensional consistency verification includes single-frequency point absolute quantity verification, cross-frequency point relative quantity verification, inter-epoch same-frequency point continuity verification, and inter-epoch cross-frequency point consistency evolution verification. S4. Calculate the comprehensive risk score based on the trigger statistics of the multi-dimensional consistency verification, and determine the system status based on the comprehensive risk score; S5. Output the judgment result and update the historical status, then proceed to the next detection epoch.

[0028] Specifically, for each set of valid observations (satellite number i, frequency point number j), its pseudorange value P[i][j] and carrier phase value are read. (This value already includes integer information processed internally by the receiver); Query its carrier wavelength based on the frequency type (e.g., GPS L1). ; Calculate the core detection quantity according to the formula: Among them, the Indicates satellite At frequency The core testing volume at the site. Indicates satellite At frequency The pseudorange observation at that location, Frequency point The corresponding carrier wavelength, Indicates satellite At frequency The carrier phase value at that location. This quantity characterizes the consistency difference between pseudorange observation and phase reconstruction distance, approaches zero and has low noise under deception-free conditions, providing a high-sensitivity benchmark for multi-dimensional verification.

[0029] In step S3, physical consistency verification is performed based on the core detection quantities in the following four dimensions: 1. Single-frequency absolute value test (spatial dimension: self-consistency): Determines whether the core detection quantity at the same frequency point exceeds a reasonable range. Specifically, checks whether its absolute value exceeds the preset single-frequency absolute threshold. (For example, 10 meters). If the following conditions are met: If a significant transient observation anomaly is detected on that frequency point, the single_triggers counter is incremented by 1. After the traversal is complete, the total number of valid frequency points (total_valid_signals) is recorded.

[0030] 2. Single-epoch cross-frequency relative quantity test (spatial dimension: inter-frequency consistency): Compare whether the difference in core detection quantities between different frequencies of the same satellite satisfies the geometric consistency constraint; specifically, for each satellite i with at least two effective frequencies, calculate the difference in core detection quantities between any two different frequencies a and b: like If the cross-frequency difference exceeds the preset threshold T_cross (e.g., 8 meters), it is determined that the satellite's observations across different frequencies have lost geometric consistency, triggering the counter inter_triggers to increment by 1. Simultaneously, the total number of valid frequency pairs total_pairs is accumulated.

[0031] 3. Continuity test of co-frequency points between epochs (time dimension: self-evolution): Detects whether the changes in the core detection quantity of the same frequency point are abnormal between adjacent epochs; specifically, for each frequency point (i, j) with valid data in the previous epoch (historical cache last_comb), calculates the epoch difference of its core detection quantity: If |δ| exceeds the preset epoch change threshold T_epoch (e.g., 5 meters), an abnormal time jump is determined for the observation value at that frequency point, triggering the counter epoch_same_triggers to increment by 1. The total number of valid continuous signals is accumulated as total_continuous_signals.

[0032] 4. Inter-epoch Cross-Frequency Consistency Evolution Verification (Time Dimension: Relationship Evolution): Tracking whether the changes in the core detection quantity differences between different frequencies of the same satellite are stable over consecutive epochs. Specifically, for frequency pair (a, b) of the same satellite i that are valid in consecutive epochs, tracking the changes in their cross-frequency difference relationship. Calculating the deviation between the cross-frequency difference value of the current epoch and the cross-frequency difference value of the previous epoch: If ϵ exceeds the preset cross-frequency relationship stability threshold T_stable (e.g., 3 meters), it is determined that the cross-frequency consistency relationship of the satellite has undergone abnormal evolution, indicating the possible existence of advanced slow spoofing, and triggering the counter epoch_inter_triggers to increment by 1. The total number of valid continuous frequency pairs is accumulated as total_continuous_pairs.

[0033] Furthermore, the test results of the above four dimensions are statistically analyzed, a comprehensive risk score is calculated according to preset weights, and the system status is determined as "safe," "suspicious," or "deceptive confirmation" based on the score threshold, achieving robust decision-making. In addition, the system cyclically executes the detection method according to the observation epoch cycle to achieve online real-time detection of deceptive interference.

[0034] For example, the comprehensive risk score is calculated using the following formula: in, k The dimension represents the multi-dimensional consistency check (4 in this example). This indicates the number of times a certain dimension's validation is triggered (single_triggers / inter_triggers / epoch_same_triggers / epoch_inter_triggers). This represents the total number of validations for a certain dimension. This represents the weight of a certain dimension's verification, preset according to the reliability and importance of each dimension's detection (e.g., set to 4.0, 3.0, 2.0, 1.0 respectively).

[0035] Furthermore, the score is linearly mapped to the integer range of 0-10 to obtain the final risk score, risk_score. If risk_score ≥ SPOOF_SCORE (e.g., 8), it is judged as "scam confirmation".

[0036] If SUSPICIOUS_SCORE ≤ risk_score < SPOOF_SCORE (e.g., 6), it is judged as "suspicious".

[0037] Otherwise, it is judged as "safe".

[0038] Finally, the judgment result (safe / suspicious / deceptive) and key statistics are output in real time through the communication interface. The detection data of the current epoch (judgment status, risk_score, trigger counts of each dimension, timestamp) is copied to the historical cache last_comb, frame_counter is updated, temporary statistical variables are cleared and the new data frame is awaited, in order to prepare for the detection of the next epoch.

[0039] In another exemplary embodiment, based on the same inventive concept as the above-described method embodiments, a GNSS anti-spoofing detection system based on multi-dimensional consistency verification is provided, comprising: The GNSS antenna uses dual antennas to receive multi-system, multi-band GNSS signals such as GPS L1 / L2 / L5. A GNSS receiver module, connected to the GNSS antenna, is used to acquire pseudorange and carrier phase observations output by the global navigation satellite system. The main control processing unit, connected to the GNSS receiver module, is configured to execute the detection algorithm and output the decision result; The communication display unit is connected to the main control processor and is used to output detection results and alarm information.

[0040] Specifically, the GNSS receiver module uses a high-precision positioning module that supports multiple systems and multiple frequencies, such as the Unicore UM982. This module connects to the antenna and is responsible for signal acquisition, tracking, and raw observation data generation, and outputs a raw observation data stream containing pseudorange, carrier phase, and tracking status words via the network port.

[0041] The main control processing unit employs an embedded microcontroller, such as the STMicroelectronics STM32F407VET6. This unit is responsible for receiving and parsing the data stream from the GNSS receiver module, executing all the anti-spoofing detection algorithms described in this invention within the chip, and making the final decision.

[0042] The communication display unit is used to output the detection results. For example, using the LAN8720A Ethernet PHY chip, the alarm information and status log generated by the main control processing unit are encapsulated into UDP data packets and sent to the host computer for display and recording via the RJ45 interface.

[0043] Furthermore, the system also includes a power management module, which is responsible for providing stable power to each module. For example, an LM76002 chip is used to convert the externally input 5V voltage to 3.3V to power the aforementioned core modules.

[0044] For example, such as Figure 2 As shown, the dual antennas receive multi-band satellite signals from GPS, BDS, and GLONASS and input them into the UM982 module; the UM982 module outputs raw observation data to the main control processing unit; after the main control processing unit runs the algorithm, the results are broadcast via UDP through the LAN8720A; the power supply is provided by the LM76002 with a 3.3V voltage regulation; the antenna port is protected against short circuits by the FPF2004.

[0045] For example, the power management circuit principle of the system is as follows: Figure 3 As shown, the external 5V input is converted to 3.3V via an LM76002 switching regulator chip. Its peripheral circuitry includes input / output filter capacitors, a feedback resistor network (used to set the output voltage), and a power inductor, ensuring efficient, low-noise power supply for digital and RF circuits. The dual-antenna protection circuitry is as follows... Figure 4 As shown, a load switch chip FPF2004 is connected in series in the antenna signal path. When a short circuit to ground or overcurrent is detected at the antenna port, the current limiting circuit inside the FPF2004 is activated, and its FLAG pin is pulled low to notify the main control processing unit. At the same time, the signal path is cut off, effectively preventing surge or short circuit accidents from damaging the expensive GNSS receiver module.

[0046] For example, the principle of an Ethernet interface is as follows: Figure 5As shown, the LAN8720A is used as the physical layer (PHY) chip. The main control unit, STM32F407, is connected to the MAC layer interface of the LAN8720A via the RMII interface. After electrical isolation and signal shaping via a network transformer, the LAN8720A is connected to the RJ45 interface. On the software side, a lightweight TCP / IP protocol stack (such as LwIP) is used to encapsulate the decision result into a UDP broadcast packet, enabling high-speed and reliable communication with the host computer.

[0047] Specifically, the following describes the software implementation steps and processing logic of the detection algorithm of the present invention on the STM32F407 main control processing unit: Phase 1: System Initialization and Data Preparation After the main control processing unit is powered on, it configures the serial port to receive GNSS module data and initializes all variables required by the algorithm, including: clearing the historical observation buffer last_comb, resetting the frame count frame_counter, and setting each detection threshold and weight coefficient.

[0048] Phase Two: Data Analysis and Combinatorial Calculation The main control processing unit cyclically reads and parses each complete observation data frame from the GNSS receiver module. For the data parsed in the current epoch, the following operations are performed: Observation filtering: Based on the tracking status word (such as tracking_status) in the observations, filter out observation pairs that simultaneously have the "effective pseudorange" and "effective carrier phase" flags.

[0049] Core detection volume calculation: The calculation result is stored in the two-dimensional array curr_comb[][] of the current epoch.

[0050] Phase 3: Multi-dimensional Consistency Parallel Verification Based on the curr_comb array obtained from the second phase calculation, the main control unit synchronously performs physical consistency checks in the above four dimensions.

[0051] Phase Four: Risk Integration and Judgment The main control unit conducts a comprehensive risk assessment based on the statistical results of the third phase.

[0052] Phase 5: Outputting Results and Updating Status Output results: The judgment results (safe / suspicious / deceptive) and key statistics are output in real time through a communication interface (such as Ethernet UDP).

[0053] State update: Copy the current epoch's curr_comb array to the history cache last_comb, update frame_counter, and prepare for the detection in the next epoch.

[0054] Performance tests show that the processing time of this invention on the STM32F407 embedded platform is less than 5 milliseconds, achieving millisecond-level real-time response to spoofing signals, which fully demonstrates the efficiency and real-time performance of the algorithm on low-cost embedded platforms. In simulated attack tests, the risk score can jump from the normal low position (0-3 points) to the spoofing threshold (≥8 points) within one epoch, proving high detection sensitivity. In continuous dynamic road tests, the system is stable and has no false alarms in complex environments, and can converge quickly after signal recovery, verifying its strong engineering robustness.

[0055] The above detailed embodiments are a description of the present invention. It should not be considered that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the protection scope of the present invention.

Claims

1. A GNSS anti-spoofing detection method based on multi-dimensional consistency verification, characterized in that, Includes the following steps: S1. Obtain the pseudorange observation value and carrier phase observation value output by the global navigation satellite system at the current detection epoch, wherein the carrier phase observation value contains integer ambiguity; S2. Construct a core detection quantity for each valid satellite frequency point, wherein the constructed core detection quantity is used to characterize the consistency between pseudorange observations and carrier phase observations; S3. Based on the core detection quantity, perform multi-dimensional consistency verification simultaneously. The multi-dimensional consistency verification includes single-frequency point absolute quantity verification, cross-frequency point relative quantity verification, inter-epoch same-frequency point continuity verification, and inter-epoch cross-frequency point consistency evolution verification. S4. Calculate the comprehensive risk score based on the trigger statistics of the multi-dimensional consistency verification, and determine the system status based on the comprehensive risk score; S5. Output the judgment result and update the historical status, then proceed to the next detection epoch.

2. The GNSS anti-spoofing detection method based on multi-dimensional consistency verification according to claim 1, characterized in that, The construction of core detection quantities for each effective satellite frequency point includes: The core detection quantity is calculated using the following formula: Among them, the Indicates satellite At frequency The core testing volume at the site. Indicates satellite At frequency The pseudorange observation at that location, Frequency point The corresponding carrier wavelength, Indicates satellite At frequency The carrier phase value at that location.

3. The GNSS anti-spoofing detection method based on multi-dimensional consistency verification according to claim 2, characterized in that, The single-frequency point absolute quantity test includes: Determine whether the absolute value of the core detection quantity at the same frequency point exceeds the preset threshold.

4. The GNSS anti-spoofing detection method based on multi-dimensional consistency verification according to claim 2, characterized in that, The cross-frequency point relative quantity test includes: Compare whether the differences in core detection quantities between different frequency points of the same satellite satisfy geometric consistency constraints.

5. The GNSS anti-spoofing detection method based on multi-dimensional consistency verification according to claim 2, characterized in that, The interepochal frequency continuity test includes: Detect whether the changes in the core detection quantity at the same frequency point are abnormal between adjacent epochs.

6. The GNSS anti-spoofing detection method based on multi-dimensional consistency verification according to claim 2, characterized in that, The inter-epoch cross-frequency consistency evolution test includes: To track whether the difference in core detection quantities between different frequency points of the same satellite remains stable over consecutive epochs.

7. The GNSS anti-spoofing detection method based on multi-dimensional consistency verification according to claim 1, characterized in that, The comprehensive risk score is calculated using the following formula: in, k This represents the dimension used for multi-dimensional consistency verification. This indicates the number of times a certain dimension's validation is triggered. This represents the total number of validations for a certain dimension. This indicates the weight of the validation for a certain dimension.

8. The GNSS anti-spoofing detection method based on multi-dimensional consistency verification according to claim 7, characterized in that, The process of determining the system status based on the comprehensive risk score includes: The comprehensive risk score is linearly mapped to the integer range of 0-10 to obtain the mapped comprehensive risk score; The system status is determined as safe, suspicious, or fraudulent based on the mapped comprehensive risk score.

9. A GNSS anti-spoofing detection system based on multi-dimensional consistency verification, characterized in that, include: GNSS antenna, used to receive multi-system, multi-band GNSS signals; A GNSS receiver module, connected to the GNSS antenna, is used to acquire pseudorange and carrier phase observations output by the global navigation satellite system. The main control processing unit is connected to the GNSS receiver module and is configured to execute the method described in any one of claims 1-8; The communication display unit is connected to the main control processor and is used to output detection results and alarm information.

10. A GNSS anti-spoofing detection system based on multi-dimensional consistency verification according to claim 9, characterized in that, The main control processing unit includes an STM32F407 series microcontroller; it also includes an antenna short-circuit protection circuit connected between the GNSS antenna and the GNSS receiver module.

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