Method and system for confirming satellite navigation deception jamming perception alarm

Abstract

The invention provides a satellite navigation deception interference perception alarm confirmation method and system, and relates to the technical field of satellite navigation anti-interference, and the method comprises the steps: obtaining the related data of a to-be-confirmed satellite signal received by a satellite navigation device in an urban electromagnetic environment; based on known historical time domain data, historical frequency domain data, historical occlusion data and historical orbit data of a real satellite signal, determining an association reference and an occlusion rule, and based on the association reference and the occlusion rule, processing related data to obtain a verification result and a target comparison result; performing propagation trajectory reduction on the to-be-confirmed satellite signal by using a digital twin model to obtain trajectory data; and when the Doppler frequency offset value or the power spectral density does not conform to the preset range, the target comparison result is mismatching, the trajectory data is inconsistent with the historical orbit data, and the verification result is inconsistent, determining that the to-be-determined satellite signal is the deception jamming signal, thereby improving the recognition accuracy and robustness of the deception jamming signal in the complex urban environment.

Description

Technical Field

[0001] This application relates to the field of satellite navigation anti-interference technology, and in particular to a method and system for confirming satellite navigation deception interference perception alarm. Background Technology

[0002] In the complex electromagnetic environment of cities, satellite navigation systems face an increasingly severe threat of deception and interference. Especially in high-security scenarios such as autonomous driving, intelligent transportation, and critical infrastructure, there is an urgent need for a technical means to quickly and accurately identify and confirm deception signals.

[0003] Current solutions employ signal consistency verification, comparing the differences in characteristics between the test signal and the reference signal in the time and frequency domains, and performing logical verification using receiver location information. Significant deviations are identified as abnormal signals. However, this approach has several significant drawbacks. For example, it fails to adequately consider the impact of dynamic occlusion on signal propagation paths in urban environments, leading to potential misjudgments in densely built-up areas or complex terrain. Furthermore, it relies solely on static signal feature comparison, lacking the ability to dynamically reconstruct the signal propagation process, making it difficult to effectively distinguish between normal distortion caused by multipath effects and artificially constructed deceptive signals. This limits its applicability and accuracy in real-world, complex scenarios. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for confirming satellite navigation deception interference perception alarms, so as to solve the problem of misjudgment of deception interference signals due to ignoring dynamic occlusion and propagation process restoration in the prior art.

[0005] To address the aforementioned technical problems, in a first aspect, this application provides a method for confirming satellite navigation spoofing interference sensing alarms, comprising: Acquire relevant data of satellite signals to be confirmed received by satellite navigation equipment in urban electromagnetic environment. The relevant data includes Doppler frequency offset, power spectral density, time domain data, frequency domain data of the satellite signals to be confirmed, and blockage data corresponding to the propagation path of the satellite signals to be confirmed. Based on known historical time-domain data, historical frequency-domain data, historical obstruction data, and historical orbit data of real satellite signals, the correlation benchmark and obstruction pattern are determined. Based on the occlusion pattern, the occlusion data is verified to obtain the verification result; The propagation trajectory of the satellite signal to be confirmed is reconstructed using a pre-built digital twin model to obtain trajectory data; Based on the aforementioned correlation benchmark, the time-domain data and frequency-domain data are processed using anomaly pattern recognition technology to obtain target comparison results; When the Doppler frequency offset or power spectral density does not meet the preset range, the target comparison result is mismatched, the trajectory data is inconsistent with historical orbit data, and the verification result is inconsistent, the satellite signal to be confirmed is confirmed as a deception interference signal.

[0006] Optionally, based on known historical time-domain data, historical frequency-domain data, historical occlusion data, and historical orbit data of real satellite signals, the correlation benchmark and occlusion patterns are determined, including: The amplitude variation characteristics and temporal distribution characteristics of different satellite orbit types are extracted from the historical time domain data of known real satellite signals, and the frequency component characteristics and power distribution characteristics of different satellite orbit types are extracted from the historical frequency domain data. Establish the correlation between amplitude variation characteristics, temporal distribution characteristics, frequency component characteristics, and power distribution characteristics of the same satellite orbit type, and form a correlation benchmark based on the correlation between different satellite orbit types; Based on historical orbit data and historical obstruction data, the probability of obstruction and historical obstruction types of the propagation path of the real satellite signal under different satellite orbit types and different satellite operating positions are statistically analyzed to form obstruction patterns adapted to each satellite orbit type.

[0007] Optionally, the occlusion data is verified according to the occlusion pattern to obtain a verification result, including: Determine the target satellite orbit type of the satellite signal to be confirmed, and extract the occlusion judgment criteria corresponding to the target satellite orbit type from the occlusion rules; The occlusion location, actual occlusion type, occlusion duration, and occlusion distribution information are extracted from the occlusion data. Based on the correspondence between satellite operating position and obstruction probability, historical obstruction types, obstruction time range, and obstruction timing characteristics in the obstruction determination criteria, the obstruction data is verified to obtain verification results. The verification content includes verifying whether the obstruction probability corresponding to the obstruction position is within a preset high probability range, verifying whether the degree of fit between the actual obstruction type and the historical obstruction type is higher than a preset degree of fit, verifying whether the obstruction duration is within the obstruction time range, and verifying whether the obstruction distribution information conforms to the obstruction occurrence pattern of real satellite signals.

[0008] Optionally, the propagation trajectory of the satellite signal to be confirmed is reconstructed using a pre-built digital twin model to obtain trajectory data, including: Based on the reception time sequence of the satellite signal to be confirmed, the target coordinate points of the satellite signal to be confirmed at different time nodes are determined in the virtual propagation scenario of the pre-constructed digital twin model. Based on the building distribution information and terrain undulation data in the digital twin model, the target coordinate points at different time points are connected to form the propagation trajectory of the satellite signal to be confirmed in the virtual propagation scenario; Trajectory data is extracted from the propagation trajectory.

[0009] Optionally, based on the building distribution information and terrain undulation data in the digital twin model, target coordinate points at different time points are connected to form the propagation trajectory of the satellite signal to be confirmed in the virtual propagation scenario, including: The building distribution information and terrain undulation data of the propagation area of ​​the satellite signal to be confirmed are extracted from the digital twin model; Based on the building distribution information and terrain undulation data, the propagation channels between adjacent target coordinate points at all time nodes are analyzed. If there are adjacent time nodes and the corresponding propagation path has a first target coordinate point that is not blocked by buildings or terrain, then all first target coordinate points are connected according to the reception time order of the satellite signal to be confirmed to obtain the first coordinate segment. If there are adjacent time nodes and the corresponding propagation path has a second target coordinate point that is blocked by a building or by terrain, then based on the obstacle distribution information in the digital twin model, the target propagation direction that bypasses the obstacle is determined, a transition coordinate point that maintains a safe distance from the obstacle is selected in the target propagation direction, and the second target coordinate point and the transition coordinate point are connected to form an obstacle avoidance path. The first coordinate segment and the obstacle avoidance path are integrated to form the propagation trajectory of the satellite signal to be confirmed in the virtual propagation scenario.

[0010] Optionally, based on the correlation benchmark, the time-domain data and frequency-domain data are processed using anomaly pattern recognition technology to obtain target comparison results, including: Match the target feature association relationship with the target satellite orbit type from the association benchmark, and extract the association constraint conditions between each feature from the target feature association relationship; Amplitude variation parameters and time distribution parameters are extracted from the time domain data, and frequency component parameters and power distribution parameters are extracted from the frequency domain data. Based on the aforementioned correlation constraints, anomaly pattern recognition technology is used to compare the amplitude variation parameters, time distribution parameters, frequency component parameters, and power distribution parameters with the corresponding features in the target feature correlation relationship to obtain the target comparison result.

[0011] Optionally, based on the associated constraints, anomaly pattern recognition technology is used to compare the amplitude variation parameters, time distribution parameters, frequency component parameters, and power distribution parameters with the corresponding features in the target feature association relationship to obtain target comparison results, including: The associated constraints are analyzed to obtain the reference quantization value range and parameter fluctuation threshold of multiple features of the same satellite orbit type, as well as the linkage logic between amplitude change features and time distribution features, and the matching logic between frequency component features and power distribution features. Based on the linkage logic and the matching logic, the amplitude change parameter, time distribution parameter, frequency component parameter and power distribution parameter are correlated and compared to obtain the first comparison result; The actual quantized value ranges of the amplitude variation parameter, time distribution parameter, frequency component parameter, and power distribution parameter are compared with the reference quantized value ranges of the corresponding features in the target feature association relationship to obtain a second comparison result. The parameter variation trends of the amplitude variation parameter, time distribution parameter, frequency component parameter and power distribution parameter are compared with the preset trends of the corresponding features to obtain the third comparison result; From the second comparison result, target parameters whose actual quantization value range exceeds the reference quantization value range are extracted. Combined with the parameter fluctuation threshold, the complementary relationship between each target parameter and the associated parameter that meets the association constraint condition is analyzed. Based on the first comparison result, the second comparison result, the third comparison result, and the complementarity relationship, the target comparison result is generated.

[0012] Secondly, this application provides a method and system for confirming satellite navigation spoofing interference sensing alarms, including: The acquisition module is used to acquire relevant data of the satellite signal to be confirmed received by the satellite navigation device in an urban electromagnetic environment. The relevant data includes the Doppler frequency offset value, power spectral density, time domain data, frequency domain data of the satellite signal to be confirmed, and the blockage data corresponding to the propagation path of the satellite signal to be confirmed. The determination module is used to determine the correlation benchmark and occlusion pattern based on known historical time-domain data, historical frequency-domain data, historical occlusion data, and historical orbit data of real satellite signals. The verification module is used to verify the occlusion data according to the occlusion pattern and obtain the verification result; The restoration module is used to restore the propagation trajectory of the satellite signal to be confirmed using a pre-built digital twin model to obtain trajectory data; The processing module is used to process the time-domain data and frequency-domain data based on the associated benchmark using anomaly pattern recognition technology to obtain target comparison results; The judgment module is used to confirm that the satellite signal to be confirmed is a deception interference signal when the Doppler frequency offset value or power spectral density does not meet the preset range, the target comparison result is mismatched, the trajectory data is inconsistent with historical orbit data, and the verification result is inconsistent.

[0013] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor, configured to execute the computer program, implement the steps of a method for confirming a satellite navigation spoofing interference sensing alarm as described in the first aspect above.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the confirmation method for satellite navigation spoofing interference perception alarm as described in the first aspect above.

[0015] This application provides a method for confirming satellite navigation deception interference sensing alarms. It acquires the Doppler frequency offset, power spectral density, time-domain and frequency-domain data, and propagation path obstruction data of the satellite signal to be confirmed, providing a foundation for comprehensively characterizing the signal features. Based on historical multi-dimensional data of real satellite signals, it establishes correlation benchmarks and obstruction patterns, making the judgment criteria more environmentally adaptable. Using these obstruction patterns to verify the current obstruction data eliminates misjudgments caused by changes in environmental structure. A pre-constructed digital twin model reconstructs the signal propagation trajectory, enabling dynamic modeling of the signal's spatial behavior. Anomaly pattern recognition technology is combined with comparative analysis of time-frequency domain data to enhance sensitivity to atypical deception signals. Finally, through multi-dimensional criteria joint decision-making, it improves the accuracy of identifying deception interference signals and the robustness against interference in complex urban electromagnetic environments, overcoming the limitations of relying solely on static feature comparison and ignoring the dynamic characteristics of the propagation path.

[0016] Furthermore, by determining the target coordinates of the signal to be confirmed in the virtual scene based on the receiving time sequence in the digital twin model, and combining the building distribution and terrain undulation information to form a propagation trajectory, the actual propagation process of the signal in the three-dimensional space of the city can be accurately restored. This method can effectively reflect the real path characteristics of the signal affected by the environmental structure, providing a reliable basis for distinguishing between normal signal distortion caused by physical obstruction or multipath effects and deceptive signals forged by humans, thereby improving the reliability and adaptability of deception identification in complex scenarios such as high-density urban areas. Attached Figure Description

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

[0018] Figure 1 A flowchart illustrating a method for confirming satellite navigation spoofing interference sensing alarms provided in an embodiment of this application; Figure 2 A schematic diagram illustrating a specific implementation of a method for confirming satellite navigation spoofing interference perception alarms provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of a satellite navigation spoofing interference perception alarm confirmation system provided in an embodiment of this application. Detailed Implementation

[0019] To address the high false positive rate of existing deception interference identification methods in urban environments due to neglecting the impact of dynamic occlusion and lacking signal propagation process modeling, this application provides a confirmation method for satellite navigation deception interference perception alarms. The core idea of ​​this method is to integrate multi-dimensional signal features with environmental context information, construct a correlation benchmark and occlusion pattern based on historical real data, perform consistency verification on the occlusion state of the currently received signal, introduce a digital twin model to reconstruct the signal propagation trajectory, and combine it with time-frequency domain abnormal pattern recognition to form a multi-condition joint judgment mechanism, thereby achieving reliable confirmation of deception interference signals in complex electromagnetic environments.

[0020] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The core of this application is to provide a method for confirming satellite navigation spoofing interference sensing alarms, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes: Step 101: Obtain relevant data of the satellite signal to be confirmed received by the satellite navigation device in the urban electromagnetic environment. The relevant data includes the Doppler frequency offset value, power spectral density, time domain data, frequency domain data of the satellite signal to be confirmed, and the obstruction data corresponding to the propagation path of the satellite signal to be confirmed.

[0022] In this step, the urban electromagnetic environment refers to the complex electromagnetic radiation environment within the urban area formed by the combined effects of various electronic devices, communication systems, power facilities, and the natural environment.

[0023] Satellite signals to be confirmed refer to satellite navigation signals received by satellite navigation equipment in an urban electromagnetic environment that need to be determined to be deceptive or interference signals. These satellite signals to be confirmed can be the original navigation signals transmitted by the satellite and any possible forged or tampered signals.

[0024] Doppler frequency offset refers to the offset of the frequency of the received signal relative to the frequency of the satellite's transmitted signal caused by the relative motion between the satellite and the receiving equipment.

[0025] Power spectral density refers to the amount of power contained in a signal per unit frequency range.

[0026] Time-domain data refers to the time-domain variation information of the satellite signal to be confirmed, recorded with time as the horizontal axis and signal amplitude as the vertical axis. This time-domain data includes parameters such as the amplitude, phase, and pulse width of the signal at different time points.

[0027] Frequency domain data refers to the information obtained by converting time domain data to the frequency domain through Fourier transform. With frequency as the horizontal axis and signal amplitude or power as the vertical axis, frequency domain data includes the frequency composition of the signal, the amplitude ratio of each frequency component, etc.

[0028] Obstruction data refers to information about the obstruction of satellite signals from the satellite to the receiving equipment by obstacles such as buildings, terrain, and vegetation. This obstruction data includes the location of the obstruction, the type of obstruction, the duration of the obstruction, and the temporal distribution of the obstruction.

[0029] In this embodiment, the satellite navigation device first acquires the satellite signal to be confirmed received in the urban electromagnetic environment through its signal receiving module. Then, the signal processing module extracts the Doppler frequency offset and power spectral density from the signal, and simultaneously records the time domain data and frequency domain data of the signal. At the same time, the environmental perception module acquires the geographical information corresponding to the signal propagation path, and determines the obstruction data such as the obstruction location and the type of obstruction object by combining the distribution of obstacles on the path. Finally, the above data are integrated to form the relevant data of the satellite signal to be confirmed.

[0030] Step 102: Based on the known historical time-domain data, historical frequency-domain data, historical occlusion data, and historical orbit data of real satellite signals, determine the correlation benchmark and occlusion pattern.

[0031] In this step, the real satellite signal refers to the standard satellite navigation signal emitted by a legitimate satellite that has not been tampered with or interfered with. Its signal characteristics and propagation laws are deterministic and stable, and it is used as a reference benchmark to determine the legitimacy of the satellite signal to be verified. The real satellite signal can be a navigation signal emitted by legitimate satellites such as the Global Positioning System and the BeiDou Navigation Satellite System.

[0032] Historical time-domain data refers to a collection of records of the time-domain changes of real satellite signals from different satellites, at different orbital positions, and under different propagation environments over a period of time. This data includes time-dimensional parameters such as the amplitude, phase, and pulse interval of the signal at different points in time.

[0033] Historical frequency domain data refers to a collection of records of the frequency domain of real satellite signals from different satellites, at different orbital positions, and under different propagation environments over a period of time. This data includes frequency dimension parameters such as the frequency composition of the signal, the amplitude ratio of each frequency component, and the power distribution.

[0034] Historical obstruction data refers to the historical record of how real satellite signals were obstructed by obstacles such as buildings and terrain along the path from the satellite to the receiving device over a period of time. This data includes obstruction locations, historical obstruction types, obstruction durations, and obstruction distribution information under different satellite orbit types and satellite operating positions.

[0035] Historical orbit data refers to a collection of records of parameters related to the actual orbit of a satellite over a period of time. This data includes parameters such as the satellite's orbital position coordinates, orbital speed, orbital inclination, and orbital period at different points in time.

[0036] The association benchmark refers to the set of feature associations established based on the real satellite signal characteristics of different satellite orbit types, which is used as a reference standard for comparing the feature parameters of satellite signals to be confirmed.

[0037] The obstruction pattern refers to the statistical pattern of obstruction of the actual satellite signal propagation path that is compatible with various satellite orbit types. It is used as a reference standard for verifying the rationality of the satellite signal propagation path to be confirmed.

[0038] In this embodiment of the application, step 102 specifically includes the following steps: Step 201: Extract the amplitude variation characteristics and temporal distribution characteristics of different satellite orbit types from the historical time domain data of known real satellite signals, and extract the frequency component characteristics and power distribution characteristics of different satellite orbit types from the historical frequency domain data.

[0039] In this step, satellite orbit type refers to the category of orbit in which a satellite revolves around the Earth. It is divided based on orbital altitude and inclination. Satellites with different orbital types have different signal characteristics, which is used to distinguish the signal patterns of different satellites.

[0040] Amplitude variation characteristics refer to the amplitude fluctuation patterns of real satellite signals over time.

[0041] Temporal distribution characteristics refer to the distribution pattern of real satellite signals in the time dimension.

[0042] Frequency component characteristics refer to the compositional patterns of real satellite signals in the frequency dimension.

[0043] Power distribution characteristics refer to the power distribution pattern of real satellite signals in the frequency dimension.

[0044] In this embodiment, the collected historical time-domain data and historical frequency-domain data are first classified according to satellite orbit type, that is, the historical data subsets corresponding to different orbit types such as low orbit and medium orbit are divided. Then, for the historical time-domain data of each orbit type, the amplitude data at different time points are extracted, and parameters such as the amplitude fluctuation range and fluctuation frequency are calculated to obtain the amplitude change characteristics of the orbit type. At the same time, the signal occurrence time, signal duration, time interval, and other data are extracted to obtain the time distribution characteristics. Next, for the historical frequency-domain data of each orbit type, the fundamental wave, harmonics, and other frequency components contained in the signal are identified, and the amplitude ratio of each frequency component is statistically analyzed to obtain the frequency component characteristics. At the same time, the power magnitude and distribution of each frequency component are statistically analyzed to obtain the power distribution characteristics.

[0045] Step 202: Establish the correlation between amplitude variation characteristics, temporal distribution characteristics, frequency component characteristics, and power distribution characteristics of the same satellite orbit type, and form a correlation benchmark based on the correlation between each satellite orbit type.

[0046] In this step, the correlation can be understood as the inherent correspondence rules between amplitude variation characteristics, time distribution characteristics, frequency component characteristics, and power distribution characteristics under the same satellite orbit type, which are used to reflect the coordinated change law between various characteristics of real satellite signals.

[0047] The associated benchmark refers to the benchmark set formed by integrating the feature association relationships of all satellite orbit types. This associated benchmark includes the feature association rules corresponding to each orbit type.

[0048] In this embodiment, for the same satellite orbit type, correlation analysis is performed on amplitude variation characteristics, time distribution characteristics, frequency component characteristics, and power distribution characteristics to determine the coordinated change rules between various characteristics. For example, when the amplitude fluctuation range is within a certain interval, the corresponding frequency component proportion and power distribution should meet the conditions, thereby establishing the correlation relationship of the four types of characteristics under this orbit type. After repeating this process to obtain the correlation relationship of all satellite orbit types, these correlation relationships are classified and integrated according to orbit type to form a correlation benchmark.

[0049] Step 203: Based on historical orbit data and historical obstruction data, statistically analyze the probability of obstruction and historical obstruction types of the propagation path of the real satellite signal under different satellite orbit types and different satellite operating positions, so as to form an obstruction pattern that is compatible with each satellite orbit type.

[0050] In this step, the satellite's operating position refers to the specific spatial coordinates of the satellite in its orbit, which is determined based on the orbital position coordinate parameters in historical orbital data. Different operating positions correspond to different propagation paths between the satellite and the receiving equipment, thus affecting the occurrence of blockage.

[0051] In this embodiment, firstly, satellite positions corresponding to different satellite orbit types are extracted from historical orbit data, and occlusion information at the corresponding positions is extracted from historical occlusion data; then, the occlusion information is grouped and statistically analyzed according to satellite orbit type and satellite position, and the probability of occlusion in each group is calculated, the historical occlusion types corresponding to each group are summarized, and the average duration range and temporal distribution characteristics of occlusion in each group are statistically analyzed; finally, the statistical results corresponding to different satellite orbit types are integrated to form an occlusion pattern adapted to each orbit type.

[0052] The embodiments of this application provide accurate reference standards for subsequent verification and comparison steps, solving the problems of single reference standards and lack of classification adaptability in traditional methods. This ensures that the associated benchmarks and occlusion patterns are accurately matched with satellite orbit types, improving the pertinence and reliability of subsequent determination of the legality of signals to be confirmed.

[0053] Step 103: Verify the occlusion data according to the occlusion pattern to obtain the verification result.

[0054] In this step, the verification result refers to the result of determining the rationality of the satellite signal propagation path to be confirmed by comparing the obstruction data with the obstruction pattern. The verification result includes matching and non-matching. Matching means that the propagation path conforms to the obstruction pattern of the real satellite signal, while non-matching means that the propagation path deviates from the obstruction pattern of the real satellite signal.

[0055] In this embodiment of the application, step 103, which verifies the occlusion data according to the occlusion pattern to obtain the verification result, specifically includes the following steps: Step 301: Determine the target satellite orbit type of the satellite signal to be confirmed, and extract the occlusion judgment criteria corresponding to the target satellite orbit type from the occlusion rules.

[0056] In this step, the occlusion determination criteria refer to the specific reference standards extracted from the occlusion patterns that correspond to the target satellite's orbital type.

[0057] In this embodiment, orbital parameters are first extracted from the relevant data of the satellite signal to be confirmed. Combined with the preset satellite orbit classification rules, the satellite orbit type to which the satellite signal to be confirmed belongs is determined as the target satellite orbit type. Then, based on the occlusion pattern, information such as the correspondence between the satellite's operating position and the occlusion probability, historical occlusion types, occlusion time range, and occlusion timing characteristics corresponding to the target satellite orbit type are extracted and integrated to form the occlusion judgment criteria.

[0058] Step 302: Extract the occlusion location, actual occlusion type, occlusion duration, and occlusion distribution information from the occlusion data.

[0059] In this step, the obstruction distribution information refers to the temporal distribution information of obstruction phenomena along the propagation path of the satellite signal to be confirmed.

[0060] In this embodiment of the application, the specific spatial coordinates of the occlusion occurrence are extracted from the occlusion data as the occlusion location, the type of occlusion object is identified to obtain the actual occlusion type, the time span from the occurrence to the end of the occlusion is counted to obtain the occlusion duration, and the time point, time period frequency, and continuity or discontinuity of the occlusion are analyzed to obtain occlusion distribution information.

[0061] Step 303: Based on the correspondence between satellite operating position and obstruction probability, historical obstruction types, obstruction time range, and obstruction timing characteristics in the obstruction determination criteria, the obstruction data is verified to obtain verification results. The verification content includes verifying whether the obstruction probability corresponding to the obstruction position is within a preset high probability range, verifying whether the degree of fit between the actual obstruction type and the historical obstruction type is higher than a preset degree of fit, verifying whether the obstruction duration is within the obstruction time range, and verifying whether the obstruction distribution information conforms to the obstruction occurrence pattern of real satellite signals.

[0062] In this step, the correspondence between satellite operating position and obstruction probability refers to the correspondence rules between different satellite operating positions and the probability of obstruction at the same satellite orbit type, as recorded in the obstruction judgment criteria. This is obtained based on historical data of real satellite signals and is used to determine whether the obstruction probability corresponding to the obstruction position of the satellite signal to be confirmed is reasonable.

[0063] The occlusion time sequence characteristics refer to the distribution pattern of occlusion phenomena in the time dimension under the same satellite orbit type, as recorded in the occlusion judgment criteria. This includes the time periods when occlusion often occurs and the continuity characteristics of occlusion, and is used to determine whether the occlusion distribution information is reasonable.

[0064] The preset high probability interval refers to a reasonable range of the probability of occlusion occurring at a certain satellite's operating position, determined based on statistical analysis of occlusion patterns. This preset high probability interval can be the range of occlusion probabilities covering more than 75% of cases in the statistical historical data for a certain satellite's operating position. In this application embodiment, the value is not limited, but can be set according to the target satellite's orbital type, the geographical environmental characteristics of the corresponding satellite's operating position, and the statistical results of historical occlusion data.

[0065] The fit degree refers to the quantified value of the degree of matching between the actual occlusion type in the occlusion data and the historical occlusion type in the occlusion determination criteria. Similarly, the embodiments of this application do not limit the value of the preset fit degree.

[0066] The pattern of obstruction refers to the inherent pattern of obstruction phenomena on the actual satellite signal propagation path, including the obstruction probability range, typical obstruction types, obstruction duration range, and obstruction timing characteristics corresponding to the obstruction judgment criteria.

[0067] In this embodiment, the occlusion data is verified based on the occlusion determination criteria. The specific verification includes: finding the occlusion probability corresponding to the occlusion location based on the correspondence between satellite operating positions and occlusion probabilities in the occlusion determination criteria, and verifying whether this probability falls within a preset high-probability range; calculating the degree of fit between the actual occlusion type in the occlusion data and the historical occlusion type in the occlusion determination criteria, and verifying whether the degree of fit is higher than a preset degree of fit; verifying whether the occlusion duration falls within the occlusion time range in the occlusion determination criteria; verifying whether the occlusion distribution information conforms to the actual occlusion occurrence pattern of satellite signals based on the occlusion timing characteristics in the occlusion determination criteria; and finally, integrating the above four verification results. If all verification items meet the requirements, the verification result is consistent; if any verification item does not meet the requirements, the verification result is inconsistent.

[0068] The embodiments of this application achieve accurate determination of the rationality of satellite signal propagation paths; solve the problem of accurate matching, and improve the pertinence and reliability of propagation path determination.

[0069] Step 104: Use the pre-built digital twin model to reconstruct the propagation trajectory of the satellite signal to be confirmed, and obtain trajectory data.

[0070] In this step, trajectory data refers to the core data set extracted from the reconstructed propagation trajectory that characterizes the propagation path of the satellite signal to be confirmed. This trajectory data includes the coordinate sequence of the trajectory, the timestamps corresponding to each coordinate point, propagation direction change parameters, the length of each segment of the trajectory, and obstacle avoidance node information, etc.

[0071] In the embodiments of this application, such as Figure 2 As shown, step 104 specifically includes the following steps: Step 401: Based on the reception time sequence of the satellite signal to be confirmed, determine the target coordinates of the satellite signal to be confirmed at different time nodes in the virtual propagation scenario of the pre-constructed digital twin model.

[0072] In this step, the virtual propagation scenario refers to the satellite signal propagation space simulated in the digital twin model that perfectly matches the real urban environment. This virtual propagation scenario includes elements such as virtual buildings, virtual terrain, and virtual satellite orbit projection.

[0073] The target coordinates at different time points refer to the theoretical spatial coordinates of the satellite signal to be confirmed at different receiving time points during its propagation.

[0074] It should be noted that the embodiments of this application do not impose specific limitations on the construction process and specific structure of the digital twin model, and can be set accordingly according to the actual situation.

[0075] In this embodiment, the reception time sequence of the satellite signal to be confirmed is first extracted and sorted chronologically to form the reception time order; then, a pre-constructed digital twin model is invoked, and the signal propagation distance corresponding to each reception time point is calculated based on the satellite's operational rules and signal propagation speed; finally, the theoretical spatial position of the signal at each time point is determined as the target coordinate point by combining the initial orbital projection position of the satellite in the digital twin model and the fixed coordinates of the receiving device.

[0076] Step 402: Based on the building distribution information and terrain undulation data in the digital twin model, connect the target coordinate points at different time points to form the propagation trajectory of the satellite signal to be confirmed in the virtual propagation scenario.

[0077] In this step, building distribution information refers to the core parameters of buildings within the propagation area recorded in the digital twin model. This information includes the building's spatial coordinates, height, floor area, and outline shape.

[0078] Terrain relief data refers to the information on changes in terrain elevation within the propagation area recorded in the digital twin model. This data includes the distribution range and elevation parameters of terrain types such as mountains, plains, and depressions.

[0079] The propagation trajectory refers to the complete propagation path curve of the satellite signal to be confirmed in a virtual propagation scenario, which can reproduce the entire process of the signal propagation from the satellite to the receiving device.

[0080] In this embodiment of the application, step 402, which connects the target coordinate points at different time points based on the building distribution information and terrain undulation data in the digital twin model to form the propagation trajectory of the satellite signal to be confirmed in the virtual propagation scenario, specifically includes the following steps: Step 411: Extract the building distribution information and terrain undulation data of the propagation area of ​​the satellite signal to be confirmed from the digital twin model.

[0081] In this embodiment, firstly, based on the location of the receiving device for the satellite signal to be confirmed and the initially determined propagation direction, the corresponding propagation area is delineated in the digital twin model. The initially determined propagation direction is derived from the orbital parameters of the satellite signal to be confirmed and the location of the receiving device in the relevant data. Then, through the data extraction interface, the initial building distribution information and terrain undulation data within the area are retrieved from the digital twin model. The retrieved initial data is then processed to standardize the format to obtain the building distribution information and terrain undulation data, ensuring that the coordinate system of the data is consistent with the coordinate system of the target coordinate point, thus providing standardized data support for subsequent propagation channel analysis.

[0082] Step 412: Based on the building distribution information and terrain undulation data, analyze the propagation channels between adjacent target coordinate points at all time nodes. If there are adjacent time nodes and the corresponding propagation path has a first target coordinate point that is not blocked by buildings or terrain, then connect all the first target coordinate points according to the reception time sequence of the satellite signal to be confirmed to obtain the first coordinate segment.

[0083] In this step, the propagation channel refers to the spatial region where the signal may propagate between two adjacent target coordinate points at two adjacent time nodes. It covers the effective range of signal propagation with the line connecting the two points as the central axis and is used to analyze whether there is any obstruction or blockage in signal propagation.

[0084] The first target coordinate point refers to the target coordinate points that are adjacent in time and whose propagation channel between the two points is not obstructed by any buildings or terrain. The corresponding propagation path conforms to the basic law of signal straight-line propagation.

[0085] The first coordinate segment refers to the set of straight line segments formed by connecting all the first target coordinate points in the order of the reception time of the satellite signal to be confirmed. Each straight line segment corresponds to a signal propagation path without obstruction, which can reflect the propagation state of the signal in an interference-free environment.

[0086] In this embodiment, the target coordinates of all adjacent time nodes are first analyzed, and the propagation channel is defined with the line connecting the two points as the central axis and the effective range of signal propagation. Then, based on the building distribution information, it is determined whether there are building obstructions in the propagation channel, and based on the terrain undulation data, it is determined whether there are terrain obstacles in the propagation channel. If the propagation channel of a pair of adjacent target coordinates has neither building obstructions nor terrain obstacles, then the pair of coordinates is marked as the first target coordinates. Finally, all the marked first target coordinates are connected in sequence according to the receiving time to form the first coordinate segment.

[0087] Step 413: If there are adjacent time nodes and the corresponding propagation path has a second target coordinate point that is blocked by a building or by terrain, then based on the obstacle distribution information in the digital twin model, determine the target propagation direction that bypasses the obstacle, select a transition coordinate point that maintains a safe distance from the obstacle in the target propagation direction, and connect the second target coordinate point and the transition coordinate point to form an obstacle avoidance path.

[0088] In this step, the second target coordinate point refers to the target coordinate point that is adjacent to the time node and the propagation channel between the two points is blocked by buildings or terrain. The corresponding straight propagation path cannot achieve normal signal propagation, and a bypass path needs to be planned.

[0089] Obstacle distribution information refers to the detailed information of obstacles in the propagation channel between the second target coordinate points recorded in the digital twin model. This obstacle distribution information includes the type of obstacle, boundary coordinates, maximum height / altitude, and extension range.

[0090] The target propagation direction refers to the optimal propagation direction that avoids obstacles and ensures that the signal propagates from the previous second target coordinate point to the next second target coordinate point. It is determined based on obstacle distribution information and signal propagation efficiency to avoid encountering obstruction or blockage again.

[0091] Transition coordinate points refer to intermediate coordinate points selected in the direction of target propagation, used to connect two adjacent second target coordinate points. Their positions must maintain a safe distance from obstacles to ensure that the signal can successfully bypass obstacles and propagate.

[0092] The safe distance refers to the minimum distance between the transition coordinate point and the obstacle. It is determined based on the diffraction characteristics of signal propagation and the size of the obstacle to ensure that the signal is not blocked by the obstacle during propagation.

[0093] An obstacle avoidance path refers to a curved or broken line path formed by connecting two adjacent second target coordinate points through transition coordinate points.

[0094] In this embodiment, firstly, based on building distribution information and terrain undulation data, the propagation channels between adjacent target coordinate points at all time points are analyzed to identify adjacent target coordinate points that are obstructed by buildings or blocked by terrain as second target coordinate points. The obstacle distribution within the propagation channels between these second target coordinate points is extracted from the digital twin model. Then, based on the obstacle distribution, a target propagation direction that bypasses the obstacles with the shortest propagation distance is planned. Next, multiple transition coordinate points are evenly selected at preset intervals along the target propagation direction, ensuring that the distance between each transition coordinate point and the obstacle is not less than a safe distance. The previous second target coordinate point is sequentially connected to each transition coordinate point, and then sequentially connected to the next second target coordinate point to form an obstacle avoidance path. In this embodiment, the length of the preset interval is not specifically limited and can be set according to actual conditions.

[0095] Step 414: Integrate the first coordinate segment and the obstacle avoidance path to form the propagation trajectory of the satellite signal to be confirmed in the virtual propagation scenario.

[0096] In this embodiment, the time node range corresponding to the first coordinate segment and the time node range corresponding to the obstacle avoidance path are first extracted; according to the reception time sequence of the satellite signal to be confirmed, the first coordinate segment and the obstacle avoidance path are time-aligned to ensure that there is no time sequence disorder; for adjacent first coordinate segments and obstacle avoidance paths, it is checked whether the endpoint coordinates of the two are smoothly connected. If there is a connection deviation, the endpoint coordinates are finely adjusted and corrected; all time-aligned and smoothly connected first coordinate segments and obstacle avoidance paths are integrated into a continuous path curve. This curve is the propagation trajectory of the satellite signal to be confirmed in the virtual propagation scenario, so as to completely reproduce the entire process of signal propagation from satellite to receiving device.

[0097] Step 403: Extract trajectory data from the propagation trajectory.

[0098] In this step, trajectory data refers to a set of quantified data extracted from the propagation trajectory that can characterize the core features of the signal propagation path. It is extracted based on the coordinate, time, and direction information of the propagation trajectory. The trajectory data includes the coordinate sequence of the trajectory, the receiving timestamps corresponding to each coordinate point, the angle of change of propagation direction between adjacent coordinate points, the length of each path segment, and the location information of obstacle avoidance nodes.

[0099] In this embodiment, the propagation trajectory is first parsed to extract the spatial coordinates of all points on the trajectory, which are then arranged in order of reception time to form a coordinate sequence. Next, a corresponding reception timestamp is matched to each coordinate point, and the angle between the line connecting adjacent coordinate points and the horizontal direction is calculated to obtain the propagation direction change angle. Then, the straight-line distance between each first coordinate segment and the obstacle avoidance path is measured to obtain the path length of each segment. The positions of all transition coordinate points are integrated into obstacle avoidance node information. Finally, the obtained coordinate sequence, reception timestamps, propagation direction change angles, path lengths, and obstacle avoidance node information are integrated according to a preset data format to form complete trajectory data. This embodiment does not specifically limit the form of the preset data format.

[0100] The embodiments of this application solve the problems of traditional trajectory reconstruction ignoring building and terrain obstruction and incomplete path reconstruction, ensuring that the trajectory reconstruction fits the real propagation environment, improving the authenticity and accuracy of trajectory data, and strengthening the rigor of the overall deception and interference identification scheme.

[0101] Step 105: Based on the aforementioned correlation benchmark, the time-domain data and frequency-domain data are processed using anomaly pattern recognition technology to obtain the target comparison results.

[0102] In this step, the target comparison result refers to the result obtained after comparing the feature parameters of the satellite signal to be confirmed with the target feature in the association benchmark through anomaly pattern recognition technology, which characterizes whether the feature of the signal to be confirmed conforms to the law of real satellite signals. The result includes matching and non-matching. Matching means that the feature parameters meet the association constraints, and non-matching means that the feature parameters deviate from the association constraints.

[0103] In this embodiment of the application, step 105 specifically includes the following steps: Step 501: Match the target feature association relationship corresponding to the target satellite orbit type from the association benchmark, and extract the association constraint conditions between each feature from the target feature association relationship.

[0104] In this step, the target feature association relationship refers to the feature association relationship in the association benchmark that precisely matches the target satellite orbit type of the satellite signal to be confirmed.

[0105] Each feature refers to the core characteristics of a real satellite signal under the same satellite orbit type. These features include amplitude variation characteristics, temporal distribution characteristics, frequency component characteristics, and power distribution characteristics.

[0106] Association constraints refer to specific limiting rules extracted from the association relationship of target features, which characterize the coordinated change law between various features. They are obtained based on the statistical analysis of historical feature data of real satellite signals. These constraints include the value range of each feature parameter and the correspondence requirements between different feature parameters.

[0107] In this embodiment, the target feature association relationship corresponding to the target satellite orbit type is matched in the association benchmark; then the target feature association relationship is parsed to extract the cooperative change rules between the features contained therein, clarify the legal value range of each feature parameter, the matching requirements of different feature parameters, etc., and thus form the association constraint conditions.

[0108] Step 502: Extract amplitude variation parameters and time distribution parameters from the time domain data, and extract frequency component parameters and power distribution parameters from the frequency domain data.

[0109] In this step, the amplitude variation parameter refers to the quantitative data extracted from the time-domain data that characterizes the amplitude variation features of the satellite signal to be confirmed.

[0110] Temporal distribution parameters refer to quantitative data extracted from time-domain data that characterize the temporal distribution features of the satellite signal to be confirmed.

[0111] Frequency component parameters refer to quantitative data extracted from frequency domain data that characterize the frequency component features of the satellite signal to be confirmed.

[0112] Power distribution parameters refer to quantitative data extracted from frequency domain data that characterize the power distribution features of the satellite signal to be confirmed.

[0113] In this embodiment, amplitude data at different time points are extracted from time-domain data, and amplitude fluctuation range, amplitude fluctuation frequency, and the difference between peak and trough amplitude values ​​are calculated to obtain amplitude change parameters. Simultaneously, signal duration, signal pulse time interval, and the proportion of time periods in which the signal appears are extracted to obtain time distribution parameters. Then, frequency-domain data is analyzed to identify frequency components such as fundamental and harmonics, and the fundamental frequency value, the ratio of each harmonic frequency to the fundamental frequency, and the amplitude proportion of the main frequency components are calculated to obtain frequency component parameters. At the same time, the power proportion of different frequency intervals, the frequency value corresponding to the power peak, and the ratio of the power peak to the mean are statistically analyzed to obtain power distribution parameters.

[0114] Step 503: Based on the aforementioned correlation constraints, use anomaly pattern recognition technology to compare the amplitude variation parameters, time distribution parameters, frequency component parameters, and power distribution parameters with the corresponding features in the target feature correlation relationship to obtain the target comparison result.

[0115] In this step, the corresponding feature refers to the real satellite signal feature in the target feature association relationship that corresponds to the feature parameter to be compared. The corresponding feature can be the amplitude variation feature corresponding to the amplitude variation parameter, the time distribution feature corresponding to the time distribution parameter, the frequency component feature corresponding to the frequency component parameter, or the power distribution feature corresponding to the power distribution parameter.

[0116] In this embodiment of the application, step 503, based on the associated constraints, uses anomaly pattern recognition technology to compare the amplitude variation parameters, time distribution parameters, frequency component parameters, and power distribution parameters with the corresponding features in the target feature association relationship to obtain the target comparison result, specifically including the following steps: Step 511: Analyze the associated constraints to obtain the reference quantization value range and parameter fluctuation threshold of multiple features of the same satellite orbit type, as well as the linkage logic between amplitude change features and time distribution features, and the matching logic between frequency component features and power distribution features.

[0117] In this step, the reference quantization value range of the feature refers to the legal value range of each feature parameter obtained by statistical analysis of historical feature data of real satellite signals of the same satellite orbit type. This value range includes the normal value range of each of the amplitude variation parameter, time distribution parameter, frequency component parameter, and power distribution parameter.

[0118] The parameter fluctuation threshold refers to the preset maximum deviation value that the characteristic parameter is allowed to fluctuate within the reference quantization range.

[0119] Linkage logic refers to the rules governing the coordinated changes in amplitude variation characteristics and temporal distribution characteristics under the same satellite orbit type.

[0120] Matching logic refers to the adaptation rules between frequency component characteristics and power distribution characteristics under the same satellite orbit type.

[0121] In this embodiment, firstly, the legal numerical ranges corresponding to amplitude variation features, time distribution features, frequency component features, and power distribution features are extracted from the associated constraints to obtain the reference quantization numerical ranges of multiple features; then, based on the stability statistics of the real satellite signal features of the target satellite orbit type, the parameter fluctuation threshold is set.

[0122] Next, the collaborative change rules between amplitude fluctuation range, fluctuation frequency and signal duration, and pulse interval in the correlation constraints regarding amplitude variation characteristics are analyzed to obtain linkage logic, which clarifies the corresponding adjustment requirements for signal duration when amplitude fluctuation range expands and the adaptation shortening standard for pulse interval when amplitude fluctuation frequency increases. At the same time, the adaptation rules between fundamental frequency proportion, harmonic quantity and main frequency power proportion, and harmonic power allocation ratio in the correlation constraints regarding frequency component characteristics are analyzed to obtain matching logic, which clarifies the lower limit requirement of main frequency power proportion corresponding to a specific fundamental frequency proportion and the matching relationship between harmonic quantity and harmonic power allocation ratio.

[0123] Step 512: Based on the linkage logic and the matching logic, perform correlation comparison on the amplitude change parameter, time distribution parameter, frequency component parameter and power distribution parameter to obtain the first comparison result.

[0124] In this step, the first comparison result refers to the result obtained after comparing the amplitude change parameter with the time distribution parameter and the frequency component parameter with the power distribution parameter based on the linkage logic and the matching logic. The result includes coordination conformity and coordination inconsistency. Coordination conformity means that the correlation between the parameters conforms to the linkage logic and the matching logic, while coordination inconsistency means that the correlation between at least one set of parameters deviates from the corresponding logic.

[0125] In this embodiment, based on linkage logic, it verifies whether the change in amplitude variation parameter meets the coordination requirement with the change in time distribution parameter. For example, when the amplitude fluctuation frequency increases, does the signal pulse time interval conform to the preset change rule? Then, based on matching logic, it verifies whether the frequency component parameter meets the adaptation requirement with the power distribution parameter. For example, does the power ratio corresponding to a specific fundamental frequency meet the preset standard? The results of the two verifications are integrated. If both meet the requirements, the first comparison result is coordinated compliance. If any verification item does not meet the requirements, the first comparison result is coordinated non-compliance.

[0126] Step 513: Compare the actual quantization value ranges of the amplitude variation parameter, time distribution parameter, frequency component parameter, and power distribution parameter with the reference quantization value ranges of the corresponding features in the target feature association relationship to obtain a second comparison result.

[0127] In this step, the second comparison result refers to the result obtained by comparing the actual quantized value range of the parameter to be compared with the reference quantized value range of the corresponding feature in the target feature association relationship. The result includes range conformity and range non-conformity.

[0128] In this embodiment, the actual quantization ranges of amplitude variation parameters, time distribution parameters, frequency component parameters, and power distribution parameters are first defined, and the reference quantization ranges of the corresponding features of the target feature correlation are extracted. The actual quantization range of each type of parameter to be compared is compared with the reference quantization range of the corresponding feature one by one. If the actual quantization ranges of all parameters are within the reference quantization range of the corresponding feature, the second comparison result is range compliance; if the actual quantization range of any parameter exceeds the reference quantization range of the corresponding feature, the second comparison result is range mismatch.

[0129] Step 514: Compare the parameter change trends of the amplitude change parameter, time distribution parameter, frequency component parameter and power distribution parameter with the preset trends of the corresponding features to obtain the third comparison result.

[0130] In this step, the parameter change trend refers to the trend of the characteristic parameters to be compared over time. This trend includes the fluctuation trend of amplitude change parameters, the interval change trend of time distribution parameters, the proportion change trend of frequency component parameters, and the power distribution change trend of power distribution parameters.

[0131] Preset trend refers to the standard change trend of corresponding features in the target feature association relationship over time.

[0132] The third comparison result refers to the result obtained by comparing the parameter change trend of the parameter to be compared with the preset trend of the corresponding feature. The result includes trend conformity and trend non-conformity.

[0133] In this embodiment, firstly, based on amplitude variation parameters, time distribution parameters, frequency component parameters, and power distribution parameters, the numerical changes of each parameter at different time points are analyzed to obtain the parameter change trend of each type of parameter; then, the parameter change trend of each type of parameter to be compared is compared with the corresponding preset trend one by one to check whether the trend is consistent, for example, whether the fluctuation frequency change trend of the amplitude variation parameter matches the preset trend; if the change trend of all parameters is consistent with the corresponding preset trend, the third comparison result is trend conformity; if the change trend of any parameter deviates from the corresponding preset trend, the third comparison result is trend inconsistency.

[0134] Step 515: Extract the target parameters whose actual quantization value range exceeds the reference quantization value range from the second comparison result, and analyze the complementary relationship between each target parameter and the associated parameter that meets the association constraint conditions in combination with the parameter fluctuation threshold.

[0135] In this step, the associated parameter refers to other characteristic parameters that have a cooperative relationship with the target parameter. For example, the associated parameter of the amplitude change parameter is the time distribution parameter, and the associated parameter of the frequency component parameter is the power distribution parameter.

[0136] Complementary relationship can be understood as a special relationship between target parameter and related parameter, where one parameter exceeds the legal range but the other parameter has a compensating deviation, and the two together still meet the related constraint conditions. This is used to avoid misjudgment caused by a single parameter slightly exceeding the range.

[0137] In this embodiment, parameters whose actual quantized values ​​exceed the reference quantized value range are first selected from the second comparison results and marked as target parameters. Then, based on the association constraint conditions, the associated parameters corresponding to each target parameter are determined. The out-of-range deviation of the target parameter is analyzed to see if it is within the parameter fluctuation threshold. At the same time, it is checked whether the associated parameter has a compensating deviation so that the cooperative relationship between the two still conforms to the linkage logic or matching logic. The out-of-range deviation is the difference between the actual quantized value of the target parameter and the boundary value of the corresponding reference quantized value range, and the difference between the actual quantized value of the associated parameter and the standard value of the corresponding reference quantized value range. If the out-of-range deviation of the target parameter is within the parameter fluctuation threshold and the compensating deviation of the associated parameter makes the two conform to the association constraint conditions, then a complementary relationship is determined to exist. If the deviation exceeds the parameter fluctuation threshold or there is no effective compensating deviation, then a complementary relationship is determined not to exist.

[0138] Step 516: Based on the first alignment result, the second alignment result, the third alignment result, and the complementarity relationship, generate the target alignment result.

[0139] In this embodiment, the first comparison result, the second comparison result, and the third comparison result are first integrated. If all three are consistent, the target comparison result is directly determined to be normal. If any discrepancy exists, the results of the complementary relationship analysis are combined: if the target parameters corresponding to the discrepancy have a complementary relationship and other comparison results are consistent, the target comparison result is determined to be normal. If the target parameters corresponding to the discrepancy do not have a complementary relationship, or if there are multiple discrepancies and no effective complementary relationship, the target comparison result is determined to be abnormal, thus achieving the final determination of the characteristics of the satellite signal to be confirmed.

[0140] The embodiments of this application solve the problems of traditional feature comparison having a single dimension, ignoring parameter correlation, and lacking tolerance for slight fluctuations, thereby improving the comprehensiveness and fault tolerance of feature determination.

[0141] Step 106: When the Doppler frequency offset or power spectral density does not meet the preset range, the target comparison result is mismatched, the trajectory data is inconsistent with the historical orbit data, and the verification result is inconsistent, the satellite signal to be confirmed is confirmed as a deception interference signal.

[0142] In this step, the preset range refers to a reasonable range of values ​​determined based on the Doppler frequency offset and power spectral density statistics of real satellite signals.

[0143] Deceptive interference signals refer to interference signals that are artificially forged, altered, or forwarded to mislead satellite navigation equipment into receiving incorrect signals, generating incorrect positioning or navigation results. Their characteristics and propagation patterns differ from those of real satellite signals.

[0144] In this embodiment of the application, when the following four conditions are met: the Doppler frequency offset or power spectral density does not meet the preset range, the target comparison result is mismatched, the trajectory data is inconsistent with historical orbit data, and the verification result is inconsistent, it indicates that the satellite signal to be confirmed deviates from the true satellite signal pattern in multiple dimensions such as signal characteristics, propagation trajectory, and path rationality. Therefore, the satellite signal to be confirmed is confirmed as a deception interference signal, and the deception interference perception alarm confirmation is completed.

[0145] The embodiments of this application are adapted to the complex electromagnetic environment of cities, making up for the shortcomings of traditional single-dimensional identification methods that are weak in anti-interference and prone to misjudgment, and improving the reliability and environmental adaptability of deception interference identification.

[0146] Figure 3 This is a schematic diagram illustrating a specific implementation of a satellite navigation spoofing interference detection and alarm confirmation system provided in this application. (Refer to...) Figure 3 The system may include: The acquisition module 31 is used to acquire relevant data of the satellite signal to be confirmed received by the satellite navigation device in an urban electromagnetic environment. The relevant data includes the Doppler frequency offset value, power spectral density, time domain data, frequency domain data of the satellite signal to be confirmed, and the blockage data corresponding to the propagation path of the satellite signal to be confirmed. The determination module 32 is used to determine the correlation benchmark and the occlusion pattern based on the known historical time domain data, historical frequency domain data, historical occlusion data and historical orbit data of real satellite signals. The verification module 33 is used to verify the occlusion data according to the occlusion pattern and obtain the verification result; The restoration module 34 is used to restore the propagation trajectory of the satellite signal to be confirmed using a pre-built digital twin model to obtain trajectory data; Processing module 35 is used to process the time domain data and frequency domain data based on the correlation benchmark using abnormal pattern recognition technology to obtain target comparison results; The judgment module 36 is used to confirm that the satellite signal to be confirmed is a deception interference signal when the Doppler frequency offset value or power spectral density does not meet the preset range, the target comparison result is mismatched, the trajectory data is inconsistent with the historical orbit data, and the verification result is inconsistent.

[0147] The satellite navigation spoofing interference sensing alarm confirmation system of this application embodiment is used to implement the aforementioned satellite navigation spoofing interference sensing alarm confirmation method. Therefore, the specific implementation of the satellite navigation spoofing interference sensing alarm confirmation system can be found in the embodiment section of the satellite navigation spoofing interference sensing alarm confirmation method above. The specific implementation can be referred to the description of the corresponding embodiment, and will not be repeated here.

[0148] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described method for confirming a satellite navigation spoofing interference perception alarm.

[0149] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for confirming satellite navigation spoofing interference perception alarm.

[0150] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0151] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above-described methods for confirming satellite navigation deception interference perception alarms.

[0152] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0153] The present application provides a detailed description of a method and system for confirming satellite navigation deception interference perception alarms. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for confirming satellite navigation spoofing interference sensing alarms, characterized in that, include: Acquire relevant data of satellite signals to be confirmed received by satellite navigation equipment in urban electromagnetic environment. The relevant data includes Doppler frequency offset, power spectral density, time domain data, frequency domain data of the satellite signals to be confirmed, and blockage data corresponding to the propagation path of the satellite signals to be confirmed. Based on known historical time-domain data, historical frequency-domain data, historical obstruction data, and historical orbit data of real satellite signals, the correlation benchmark and obstruction pattern are determined. Based on the occlusion pattern, the occlusion data is verified to obtain the verification result; The propagation trajectory of the satellite signal to be confirmed is reconstructed using a pre-built digital twin model to obtain trajectory data; Based on the aforementioned correlation benchmark, the time-domain data and frequency-domain data are processed using anomaly pattern recognition technology to obtain target comparison results; When the Doppler frequency offset or power spectral density does not meet the preset range, the target comparison result is mismatched, the trajectory data is inconsistent with historical orbit data, and the verification result is inconsistent, the satellite signal to be confirmed is confirmed as a deception interference signal.

2. The method according to claim 1, characterized in that, Based on known historical time-domain data, historical frequency-domain data, historical obstruction data, and historical orbit data of real satellite signals, the correlation benchmark and obstruction patterns are determined, including: The amplitude variation characteristics and temporal distribution characteristics of different satellite orbit types are extracted from the historical time domain data of known real satellite signals, and the frequency component characteristics and power distribution characteristics of different satellite orbit types are extracted from the historical frequency domain data. Establish the correlation between amplitude variation characteristics, temporal distribution characteristics, frequency component characteristics, and power distribution characteristics of the same satellite orbit type, and form a correlation benchmark based on the correlation between different satellite orbit types; Based on historical orbit data and historical obstruction data, the probability of obstruction and historical obstruction types of the propagation path of the real satellite signal under different satellite orbit types and different satellite operating positions are statistically analyzed to form obstruction patterns adapted to each satellite orbit type.

3. The method according to claim 1, characterized in that, Based on the occlusion pattern, the occlusion data is verified to obtain the verification results, including: Determine the target satellite orbit type of the satellite signal to be confirmed, and extract the occlusion judgment criteria corresponding to the target satellite orbit type from the occlusion rules; The occlusion location, actual occlusion type, occlusion duration, and occlusion distribution information are extracted from the occlusion data. Based on the correspondence between satellite operating position and obstruction probability, historical obstruction types, obstruction time range, and obstruction timing characteristics in the obstruction determination criteria, the obstruction data is verified to obtain verification results. The verification content includes verifying whether the obstruction probability corresponding to the obstruction position is within a preset high probability range, verifying whether the degree of fit between the actual obstruction type and the historical obstruction type is higher than a preset degree of fit, verifying whether the obstruction duration is within the obstruction time range, and verifying whether the obstruction distribution information conforms to the obstruction occurrence pattern of real satellite signals.

4. The method according to claim 1, characterized in that, The propagation trajectory of the satellite signal to be confirmed is reconstructed using a pre-built digital twin model to obtain trajectory data, including: Based on the reception time sequence of the satellite signal to be confirmed, the target coordinate points of the satellite signal to be confirmed at different time nodes are determined in the virtual propagation scenario of the pre-constructed digital twin model. Based on the building distribution information and terrain undulation data in the digital twin model, the target coordinate points at different time points are connected to form the propagation trajectory of the satellite signal to be confirmed in the virtual propagation scenario; Trajectory data is extracted from the propagation trajectory.

5. The method according to claim 4, characterized in that, Based on the building distribution information and terrain undulation data in the digital twin model, target coordinate points at different time points are connected to form the propagation trajectory of the satellite signal to be confirmed in the virtual propagation scenario, including: The building distribution information and terrain undulation data of the propagation area of ​​the satellite signal to be confirmed are extracted from the digital twin model; Based on the building distribution information and terrain undulation data, the propagation channels between adjacent target coordinate points at all time nodes are analyzed. If there are adjacent time nodes and the corresponding propagation path has a first target coordinate point that is not blocked by buildings or terrain, then all first target coordinate points are connected according to the reception time order of the satellite signal to be confirmed to obtain the first coordinate segment. If there are adjacent time nodes and the corresponding propagation path has a second target coordinate point that is blocked by a building or by terrain, then based on the obstacle distribution information in the digital twin model, the target propagation direction that bypasses the obstacle is determined, a transition coordinate point that maintains a safe distance from the obstacle is selected in the target propagation direction, and the second target coordinate point and the transition coordinate point are connected to form an obstacle avoidance path. The first coordinate segment and the obstacle avoidance path are integrated to form the propagation trajectory of the satellite signal to be confirmed in the virtual propagation scenario.

6. The method according to claim 1, characterized in that, Based on the aforementioned correlation benchmark, the time-domain and frequency-domain data are processed using anomaly pattern recognition technology to obtain target comparison results, including: Match the target feature association relationship with the target satellite orbit type from the association benchmark, and extract the association constraint conditions between each feature from the target feature association relationship; Amplitude variation parameters and time distribution parameters are extracted from the time domain data, and frequency component parameters and power distribution parameters are extracted from the frequency domain data. Based on the aforementioned correlation constraints, anomaly pattern recognition technology is used to compare the amplitude variation parameters, time distribution parameters, frequency component parameters, and power distribution parameters with the corresponding features in the target feature correlation relationship to obtain the target comparison result.

7. The method according to claim 6, characterized in that, Based on the aforementioned correlation constraints, anomaly pattern recognition technology is used to compare the amplitude variation parameters, time distribution parameters, frequency component parameters, and power distribution parameters with the corresponding features in the target feature correlation relationship to obtain the target comparison results, including: The associated constraints are analyzed to obtain the reference quantization value range and parameter fluctuation threshold of multiple features of the same satellite orbit type, as well as the linkage logic between amplitude change features and time distribution features, and the matching logic between frequency component features and power distribution features. Based on the linkage logic and the matching logic, the amplitude change parameter, time distribution parameter, frequency component parameter and power distribution parameter are correlated and compared to obtain the first comparison result; The actual quantized value ranges of the amplitude variation parameter, time distribution parameter, frequency component parameter, and power distribution parameter are compared with the reference quantized value ranges of the corresponding features in the target feature association relationship to obtain a second comparison result. The parameter variation trends of the amplitude variation parameter, time distribution parameter, frequency component parameter and power distribution parameter are compared with the preset trends of the corresponding features to obtain the third comparison result; From the second comparison result, target parameters whose actual quantization value range exceeds the reference quantization value range are extracted. Combined with the parameter fluctuation threshold, the complementary relationship between each target parameter and the associated parameter that meets the association constraint condition is analyzed. Based on the first comparison result, the second comparison result, the third comparison result, and the complementarity relationship, the target comparison result is generated.

8. A confirmation system for satellite navigation spoofing interference detection alarm, characterized in that, include: The acquisition module is used to acquire relevant data of the satellite signal to be confirmed received by the satellite navigation device in an urban electromagnetic environment. The relevant data includes the Doppler frequency offset value, power spectral density, time domain data, frequency domain data of the satellite signal to be confirmed, and the blockage data corresponding to the propagation path of the satellite signal to be confirmed. The determination module is used to determine the correlation benchmark and occlusion pattern based on known historical time-domain data, historical frequency-domain data, historical occlusion data, and historical orbit data of real satellite signals. The verification module is used to verify the occlusion data according to the occlusion pattern and obtain the verification result; The restoration module is used to restore the propagation trajectory of the satellite signal to be confirmed using a pre-built digital twin model to obtain trajectory data; The processing module is used to process the time-domain data and frequency-domain data based on the associated benchmark using anomaly pattern recognition technology to obtain target comparison results; The judgment module is used to confirm that the satellite signal to be confirmed is a deception interference signal when the Doppler frequency offset value or power spectral density does not meet the preset range, the target comparison result is mismatched, the trajectory data is inconsistent with historical orbit data, and the verification result is inconsistent.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a method for confirming satellite navigation spoofing interference perception alarm as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a method for confirming satellite navigation spoofing interference perception alarms as described in any one of claims 1 to 7.

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