Satellite navigation jamming troubleshooting method based on situation fusion

Abstract

The application discloses a satellite navigation interference checking method based on situation fusion, relates to the technical field of satellite navigation interference monitoring, and comprises the following steps: constructing a global situation and performing coarse positioning to obtain a target region; constructing a regional situation and narrowing the interference range to obtain a core region; and constructing a single-point situation and positioning an interference source. The application adopts a three-level situation fusion architecture of "global-region-single point", realizes step-by-step approximation of the interference source from coarse positioning in a large range to accurate positioning in a single point, and completely solves the problems of great blindness, low efficiency, inaccurate positioning and high cost of traditional interference checking.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation interference monitoring technology, specifically, a satellite navigation interference investigation method based on situational fusion. Background Technology

[0002] Satellite navigation systems are widely used in many key fields such as aviation, navigation, transportation, surveying and mapping, and public safety. Their positioning, velocity measurement, and timing accuracy directly determine the safety and reliability of operations in these fields. However, with the rapid development of radio technology, various forms of interference, whether intentional or unintentional, are increasing in satellite navigation frequency bands. These interference sources include suppression interference and deceptive interference, which seriously affect the normal operation of satellite navigation systems and may even cause safety accidents.

[0003] Currently, satellite navigation interference investigation mainly employs single monitoring methods, such as monitoring from fixed monitoring stations, random checks with mobile monitoring equipment, and single-point monitoring of direction-finding receivers. However, these methods all have significant drawbacks:

[0004] (1) Fixed monitoring stations have high deployment costs and limited coverage, making it difficult to achieve wide-area interference monitoring;

[0005] (2) The investigation of mobile devices lacks precise scope guidance in the early stage, resulting in a lot of blindness and low investigation efficiency. Especially in a large area of ​​more than 100 kilometers, it often requires a lot of manpower, material resources and time.

[0006] (3) Single-point receiver direction finding can only analyze local points and cannot form a regional interference situation. It is difficult to quickly lock the approximate range of the interference source, resulting in the accuracy and efficiency of interference investigation being unable to meet actual needs. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a satellite navigation interference investigation method based on situational awareness fusion. By providing large-scale situational awareness guidance in the early stages, and then combining regional-level measured data with single-point direction-finding data, the method achieves multi-situational awareness fusion and gradually approaches the interference source, thereby improving the efficiency and accuracy of interference investigation.

[0008] The present invention solves the above problems through the following technical solution:

[0009] A satellite navigation interference detection method based on situational awareness fusion includes:

[0010] Step S1: Construct a global situation and perform coarse localization to obtain the target area;

[0011] Step S2: Construct the regional situation and narrow down the interference range to obtain the core area;

[0012] Step S3: Construct a single-point situation and locate the interference source.

[0013] As a further improvement of the present invention, step S1 includes:

[0014] Collect core data;

[0015] Preprocessing and interference feature extraction of core data;

[0016] After fusing and analyzing the extracted interference features, a global situational awareness is constructed, and the range of satellite navigation interference is locked to the target area.

[0017] As a further improvement of the present invention, the core data includes Automatic Dependent Surveillance-Broadcast (ADS-B) data, Automatic Identification System (AIS) data, and open-source base station data. ADS-B data includes aircraft position, altitude, speed, navigation accuracy category (Navigation Integrity Category NIC or Navigation Position Accuracy Category NACp), trajectory continuity, and other data, used to reflect satellite navigation interference in the aviation field. AIS data includes ship position, speed, heading, positioning status, and other data, used to reflect satellite navigation interference in the maritime field. Open-source base station data includes satellite navigation frequency band signal strength, signal-to-noise ratio (SNR), interference alarm information, and other data within the base station coverage area, used to reflect satellite navigation interference in land areas.

[0018] The preprocessing refers to cleaning, denoising, time synchronization and coordinate alignment of the ADS-B data, AIS data and base station open source data, as well as removing abnormal data, invalid data and noise interference, and retaining valid data.

[0019] As a further improvement of the present invention, the interference feature extraction includes:

[0020] Interference features of ADS-B data, AIS data, and base station open-source data are extracted from the preprocessed valid data, including:

[0021] The interference features of the ADS-B data include positioning accuracy degradation features, trajectory breakage features, and navigation accuracy drop features. The positioning accuracy degradation feature is identified by judging whether the position deviation exceeds a preset threshold. The trajectory breakage feature is identified by analyzing the abnormal signal reception of continuous trajectory interruption points. The navigation accuracy drop feature is identified by judging potential anomalies by a sudden drop in navigation accuracy category parameters.

[0022] The interference features of the AIS data include position drift features, positioning inaccuracy features, and speed and heading mismatch features; the position drift feature is identified by the ship's position deviation threshold, the positioning inaccuracy feature is identified by the consistency analysis between position, speed and heading, and the speed and heading mismatch feature is captured by motion state anomaly analysis;

[0023] The interference characteristics of the base station open-source data include a sudden drop in signal-to-noise ratio (SNR), a rise in noise floor, and an abnormal increase in positioning error. These characteristics are identified through spectrum monitoring and abnormal jump threshold judgment. Preferably, the sudden drop in SNR originates from the detection of instantaneous or short-term abrupt changes in the SNR time series of the base station received signal. The rise in noise floor, based on spectrum monitoring, mainly analyzes the continuous or gradual upward trend of the noise floor at the base station receiver relative to the interference-free period or the reference value of a neighboring station. The abnormal increase in positioning error originates from the detection of abnormal jumps in the deviation sequence between the positioning result calculated based on the base station signal or reported by the terminal and its actual reference position.

[0024] As a further improvement of the present invention, the step of completing the global situation construction after fusing and analyzing the extracted interference features and locking the satellite navigation interference range to the target area specifically includes:

[0025] Weights are assigned based on the category and severity of interference features, and a weighted sum is obtained to obtain interference feature points with comprehensive interference weights. For example, if there is a sudden change in position, no position output for a period of time, or abnormal speed, all interference feature points are mapped to a unified spatiotemporal coordinate system and clustered according to spatial and temporal density. The spatial center and coverage of each cluster are obtained by comprehensively considering the comprehensive interference weights and density of interference feature points within the cluster, thereby identifying the core interference area. Based on the location and intensity information of the interference area, the interference range is locked to the target area, and a global situation report is output to clarify the approximate location and interference intensity level of the target area. Furthermore, the main interference type within the target area is determined based on the performance pattern of different interference features. If the main feature is a sudden disappearance of the signal, a general decrease in positioning accuracy, or a trajectory interruption, it is determined to be suppression interference. If the main feature is an abnormal position shift, a sudden change in speed or heading, it is determined to be deceptive interference.

[0026] As a further improvement of the present invention, step S2 includes:

[0027] The navigation interference detection device is installed on a mobile vehicle. The navigation interference detection device uses a multi-directional antenna array to receive interference signals from multiple directions.

[0028] Based on the geographical environment and road network distribution of the target area, plan the driving route of the mobile vehicle;

[0029] The mobile vehicle travels along the driving path and collects interference data of the navigation frequency band in the target area in real time, including information such as interference signal strength, interference frequency, interference waveform, and direction of arrival.

[0030] The interference data is analyzed and fused to obtain the comprehensive interference intensity of each grid in the target area.

[0031] After normalizing the comprehensive interference intensity of each grid, it is mapped to a visualized interference heatmap. Based on the interference heatmap, the area with the highest interference intensity is identified, and the interference range is further narrowed down to the core area. A regional situation report is output to clarify the interference distribution, peak interference location, and interference intensity change trend in the core area.

[0032] As a further improvement of the present invention, the step of analyzing and fusing the interference data to obtain the comprehensive interference intensity of each grid in the target region includes:

[0033] Determine the grid within the influence range of the acquisition location. For each affected grid, calculate the distance and azimuth angle of the grid relative to the acquisition location, and determine the angle weight and distance weight of each antenna of the mobile vehicle.

[0034] For each antenna, if the interference signal strength received by the antenna at the acquisition location is higher than the preset effective threshold, the measured interference signal strength is weighted by the angle weight and distance weight corresponding to the antenna to obtain the single antenna contribution value of the antenna to the grid; if the interference signal strength received by the antenna at the acquisition location is not higher than the preset effective threshold, the antenna does not generate a single antenna contribution value.

[0035] The total interference contribution of the sampling location to the grid is obtained by summing the individual antenna contribution values ​​of each antenna.

[0036] Repeat the above calculation for all acquisition locations, and fuse all the total interference contribution values ​​received from the same grid to obtain the comprehensive interference intensity of each grid.

[0037] As a further improvement of the present invention, step S3 specifically includes:

[0038] Within the core area, the mobile vehicle travels at a preset speed, and the satellite navigation receiver installed on the mobile vehicle synchronously collects monitoring data for each preset trajectory point, including information such as the number of visible satellites, carrier-to-noise ratio, pseudorange residual, positioning error, and number of lock-offs. At the same time, the navigation interference detection equipment installed on the mobile vehicle synchronously collects interference signal data for that trajectory point through a multi-element directional antenna array. The interference signal data includes the direction of arrival of the interference signal and the strength of the interference signal.

[0039] The monitoring data of each trajectory point is analyzed to determine the type and intensity of interference. If the carrier-to-noise ratio drops sharply or the number of satellite lock-offs increases, it is determined to be suppression interference. If the pseudorange residual changes abruptly, the positioning error increases abnormally, or the position jumps, it is determined to be deception interference. The interference level of the trajectory point is then quantitatively evaluated in conjunction with the interference intensity.

[0040] At least three trajectory points of interference signal data should be selected, and the direction of the interference source should be determined by spatial spectrum or interferometer direction finding methods respectively.

[0041] Cross-location is performed on the direction-finding data of multiple trajectory points. By combining the position information of each trajectory point with the interference intensity, the precise location of the interference source is calculated, a single-point situation map is generated, and a single-point situation report is output.

[0042] As a further improvement of the present invention, the step of calculating the precise location of the interference source and generating a single-point situation map includes:

[0043] Starting from the position of each direction finding point, and drawing the direction line of the incoming wave measured by that direction finding point, a direction finding observation equation is constructed. Using the interference intensity or direction finding confidence of each direction finding point as weights, a weighted error minimization model is established. The optimal intersection point of each direction line is solved by a nonlinear least squares optimization algorithm. The coordinates of the optimal intersection point are the precise location of the interference source.

[0044] The location of each direction finding point, direction lines, and interference level are overlaid on the map to mark the precise location of the interference source and generate a single-point situation map.

[0045] As a further improvement of the present invention, it also includes verifying, investigating and confirming the interference source of the location.

[0046] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0047] (1) The present invention adopts a three-level situational fusion architecture of “global-regional-single point” to realize the gradual approximation of interference sources from large-scale coarse positioning to single-point precise positioning, which completely solves the problems of blindness and low efficiency in traditional interference investigation; among them, the global situational fusion locks the interference range from more than 100 kilometers to within 20 kilometers, the regional situational fusion further narrows it to within 2 kilometers, and the single-point situational fusion achieves precise positioning within 50 meters, which greatly improves the accuracy of interference investigation.

[0048] (2) In this invention, the global situation relies on open source data such as ADS-B, AIS, and base stations, which eliminates the need to deploy a large number of fixed monitoring stations, greatly reducing the initial deployment cost of interference investigation. At the same time, it realizes rapid coarse positioning of wide-area and long-area interference, and is suitable for various complex scenarios such as ocean, airspace, and land.

[0049] (3) In this invention, the regional situation is collected by a mobile vehicle equipped with a multi-directional antenna array. By performing gridded azimuth-range weighted fusion calculation on each collection location, the discrete multi-directional interference signal intensity is transformed into a comprehensive interference intensity distribution in continuous space. The interference heat map is generated by inversion, which can intuitively and accurately present the interference distribution and interference energy accumulation area in the region, providing precise guidance for subsequent single-point investigation. The multi-directional antenna array has omnidirectional direction finding and high-precision acquisition capabilities. Combined with the few-channel quasi-correction scheme, the accuracy of interference data acquisition and direction finding is further improved.

[0050] (4) In this invention, the single-point situational awareness is combined with detailed data from the satellite navigation receiver and direction finding data from the multi-dimensional directional antenna array. The MUSIC algorithm and nonlinear least squares optimization algorithm are used to achieve accurate positioning of the interference source and accurate judgment of the interference type, providing a reliable basis for investigating and dealing with the interference source and avoiding misjudgment and omission.

[0051] (5) The present invention can quickly complete the investigation of satellite navigation interference. Compared with traditional investigation methods, the investigation efficiency is improved by more than 80% and the positioning accuracy is improved by more than 90%. It can be widely used in various satellite navigation interference investigation scenarios such as aviation, navigation, and land, and has extremely high engineering application value. Attached Figure Description

[0052] Figure 1 This is a flowchart of an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram illustrating the regional situational awareness interference investigation in an embodiment of the present invention. Detailed Implementation

[0054] The present invention will be further described in detail below with reference to embodiments, but the implementation of the present invention is not limited thereto.

[0055] Combined with appendix Figure 1 As shown, this embodiment of the invention provides a satellite navigation interference investigation method based on situational fusion, including the following steps:

[0056] I. Overall Situation Construction and Coarse Positioning

[0057] The system collects three core data types: ADS-B data, AIS data, and open-source base station data. It preprocesses and extracts interference features from each type of data, and then performs fusion analysis to complete the global situational awareness construction, thereby narrowing down the satellite navigation interference range from a wide area of ​​over 100 kilometers to a target area within 20 kilometers.

[0058] Specifically, it includes the following steps:

[0059] 1. Data Acquisition: Simultaneously collect ADS-B data, AIS data, and open-source base station data through public or authorized data sources. ADS-B data includes information such as aircraft position, altitude, speed, navigation accuracy category (NIC / NACp), and trajectory continuity, reflecting satellite navigation interference in the aviation field. AIS data includes information such as ship position, speed, heading, and positioning status, reflecting satellite navigation interference in the maritime field. Open-source base station data includes satellite navigation frequency band signal strength, signal-to-noise ratio (SNR), and interference alarm information within the base station coverage area, reflecting satellite navigation interference in land areas.

[0060] 2. Data preprocessing: The three types of collected data are cleaned, denoised, time-synchronized, and coordinate-aligned to remove abnormal, invalid, and noise interference, and retain valid data. Abnormal data includes data with location jumps exceeding reasonable ranges, abnormal signal strength changes, and data missing rates exceeding preset thresholds.

[0061] 3. Interference Feature Extraction: For the preprocessed valid data, interference features are extracted using methods such as threshold judgment, trajectory continuity analysis, and motion state assessment. Interference features for ADS-B data include positioning accuracy degradation (identified by whether the position deviation exceeds a preset threshold), trajectory breakage (analyzed by continuous trajectory interruption points to identify signal reception anomalies), and navigation accuracy sudden drop (identified by sudden drops in NIC / NACp parameters to identify potential anomalies). Interference features for AIS data include position drift (identified by ship position deviation thresholds), positioning inaccuracy (identified by consistency analysis between position, speed, and heading), and speed and heading mismatch (captured by motion state anomaly analysis). Interference features for base station open-source data include signal-to-noise ratio sudden drop, noise floor increase, and abnormally increased positioning error (identified by spectrum monitoring and abnormal jump threshold judgment, such as increased noise floor in the spectrum or decreased carrier-to-noise ratio in navigation data).

[0062] All interference features were quantified in severity and spatiotemporally labeled after extraction to form a set of interference features that can be used for subsequent fusion analysis.

[0063] 4. Fusion Analysis and Coarse Localization: For the extracted interference feature points, weights are first assigned according to feature category and severity, with higher weights given to high-severity or critical feature points to reflect the intensity of potential interference. Then, all weighted interference feature points are mapped to a unified spatiotemporal coordinate system and clustered according to spatial and temporal density, dividing the feature points into several clusters. The spatial center and coverage area of ​​each cluster are obtained by comprehensively considering the weights and densities of feature points within the cluster, thereby identifying the core interference area. Based on the location and intensity information of the core area, the interference range is narrowed down from a large area to a target area within approximately 20 kilometers. A global situation report is output, clearly indicating the approximate location and interference intensity level of the target area. Furthermore, the main interference type within the target area is determined based on the performance patterns of different interference features. If the main features are characterized by sudden signal loss, a general decrease in positioning accuracy, or trajectory interruption, it is identified as suppression interference. If the main features are characterized by abnormal position shifts, sudden changes in speed or heading, it is identified as deceptive interference.

[0064] 1. Data Acquisition: Simultaneously collect ADS-B data, AIS data, and open-source base station data through public or authorized data sources. ADS-B data includes information such as aircraft position, altitude, speed, navigation accuracy category (NIC / NACp), and trajectory continuity, reflecting satellite navigation interference in the aviation field. AIS data includes information such as ship position, speed, heading, and positioning status, reflecting satellite navigation interference in the maritime field. Open-source base station data includes satellite navigation frequency band signal strength, signal-to-noise ratio (SNR), and interference alarm information within the base station coverage area, reflecting satellite navigation interference in land areas.

[0065] 2. Data preprocessing: The three types of collected data are cleaned, denoised, time-synchronized, and coordinate-aligned to remove abnormal, invalid, and noise interference, and retain valid data. Abnormal data includes data with location jumps exceeding reasonable ranges, abnormal signal strength changes, and data missing rates exceeding preset thresholds.

[0066] 3. Interference Feature Extraction: For the preprocessed valid data, interference features are extracted using methods such as threshold judgment, trajectory continuity analysis, and motion state assessment. Interference features for ADS-B data include positioning accuracy degradation (identified by whether the position deviation exceeds a preset threshold), trajectory breakage (analyzed by continuous trajectory interruption points to identify signal reception anomalies), and navigation accuracy sudden drop (identified by sudden drops in NIC / NACp parameters to identify potential anomalies). Interference features for AIS data include position drift (identified by ship position deviation thresholds), positioning inaccuracy (identified by consistency analysis between position, speed, and heading), and speed and heading mismatch (captured by motion state anomaly analysis). Interference features for base station open-source data include signal-to-noise ratio (SNR) sudden drop (detected by abrupt changes in SNR time series), noise floor increase (detected by comparing the real-time noise floor at the base station receiver with the baseline value during interference-free periods), and abnormal increase in positioning error (detected by calculating the deviation sequence between the real-time positioning result and its actual reference position and detecting abnormal jumps).

[0067] All interference features were quantified for severity and labeled spatiotemporally and typologically after extraction to form a set of interference feature points that can be used for subsequent fusion analysis.

[0068] 4. Fusion Analysis and Coarse Localization: For the extracted set of interference feature points containing severity and spatiotemporal information, weights are first assigned according to feature category and severity, with higher weights given to high-severity or critical feature points to reflect the intensity of potential interference. Then, all weighted interference feature points are mapped to a unified spatiotemporal coordinate system and clustered according to spatial and temporal density, dividing the interference feature points into several clusters. The spatial center and coverage of each cluster are obtained by comprehensively considering the weights and densities of interference feature points within the cluster, thereby identifying the core interference region. Based on the location and intensity information of the core interference region, the interference range is narrowed down from a large area to a target area within approximately 20 kilometers. Simultaneously, a global situation report is output, clearly defining the approximate location and interference intensity level of the target area. Furthermore, the main interference type within the target area is determined based on the performance patterns of different interference features. If the main features are characterized by sudden signal disappearance, widespread decrease in positioning accuracy, or trajectory interruption, it is identified as suppression interference; if the main features are characterized by abnormal position shifts, sudden changes in speed or heading, it is identified as deceptive interference.

[0069] II. Regional Situation Construction and Refinement

[0070] A mobile vehicle (or mobile radio monitoring vehicle) equipped with navigation interference detection equipment is deployed. The navigation interference detection equipment is equipped with a multi-channel directional antenna array. The multi-channel directional antenna array adopts a few-channel quasi-calibration scheme, adding a calibration source in each channel to obtain the initial phase difference between any two channels, thereby reducing the impact of the initial phase difference between channels on the direction finding calculation. Based on the locked target area, a preset driving path is automatically or manually planned. The preset path is designed to cover the main road network of the target area, ensuring that the driving trajectory forms sufficient spatial sampling of the target area. The mobile vehicle travels along the preset path, synchronously collecting navigation frequency band signal levels in each direction through a multi-directional antenna array, while recording real-time position and time information. For each collection position, based on the spatial grid within its influence radius, the azimuth and distance of each grid relative to the collection position are calculated. The interference contribution of each grid is obtained by weighted fusion combined with antenna pointing differences and distance attenuation. The contributions of all collection positions to the same grid are fused, and the comprehensive interference intensity of each grid is accumulated or selected to form the comprehensive interference intensity of each grid. The comprehensive interference intensity of each grid is mapped into a visualized interference situation heat map to complete the regional situation construction. Based on the high-value clusters in the heat map, the interference range is further narrowed to a core area within 2 kilometers.

[0071] Specifically, it includes the following steps:

[0072] 1. Equipment Deployment: The navigation interference detection equipment is fixedly installed on a mobile vehicle such as a car. The navigation interference detection equipment includes a multi-element directional antenna array, a signal acquisition module, a data processing module, and a storage module. The multi-element directional antenna array adopts a directional array with at least 4 elements or other methods, supports signal reception in the core frequency band of satellite navigation, has 360° omnidirectional direction finding capability, a direction finding error ≤3.5°, and can simultaneously receive interference signals from multiple directions.

[0073] 2. Route Planning: Based on the geographical environment and road network distribution of the target area within a locked 20-kilometer radius, the travel route of the mobile vehicle is planned. The specific planning method is as follows: Extract the boundary and internal road network data of the target area (road grade, traffic conditions). Using the center of the target area as a reference, prioritize the main roads and secondary roads in the area as the basic road segments for planning. Set the spacing between adjacent parallel travel segments, such as 500-2000 meters. Using the influence radius R of the mobile vehicle monitoring equipment (typical value 500 meters) as a reference, ensure that the spacing between adjacent paths D≤2R, so that the monitoring coverage of each collection point on the path overlaps, eliminates blind spots, and ensures the integrity of interference data collection. For sparse road network sections in the area, use round-trip detours or supplementary branch paths to ensure spatial sampling density. Finally, connect the planned road segments into a closed loop or zigzag continuous travel path, and output the path node sequence to the navigation equipment to guide the travel of the mobile vehicle.

[0074] 3. Data Acquisition: The mobile vehicle travels along the planned path and collects interference data of the navigation frequency band in the area in real time through a multi-directional antenna array, including information such as interference signal strength, interference frequency, interference waveform, and direction of arrival.

[0075] 4. Interference Assessment and Heatmap Inversion: The collected interference data is analyzed and processed. The specific assessment and calculation process is as follows:

[0076] (1) Gridding and single-point contribution calculation:

[0077] The target area is divided into a uniform geographic grid (grid spacing is set according to accuracy requirements, typically 30 meters). For each acquisition point along the planned path of the mobile vehicle, the grid cells within its influence range are determined with that point as the center and a preset influence radius (typically 500 meters). For each affected grid, the distance d and azimuth β of the grid relative to the acquisition point are calculated; the azimuth β is calculated to be the angular deviation between the azimuth β and the pointing of each antenna of the mobile vehicle, and the angular weight is determined using a function that decreases as the deviation increases (such as a Gaussian function); at the same time, the distance weight is determined using a function that decreases as the distance increases (such as a linear attenuation function).

[0078] (2) Multi-antenna contribution fusion:

[0079] For each antenna, if the interference signal strength received by that antenna at the acquisition location is higher than a preset effective threshold, the measured interference signal strength is weighted by the angle and distance weights corresponding to that antenna to obtain the single-antenna contribution value for that grid; otherwise, that antenna does not contribute. The total interference contribution value of that acquisition location to that grid is the sum of the contribution values ​​of all effective antennas. If the interference signal strength of all antennas at that acquisition location is lower than the effective threshold, a small base score is assigned to retain the information recorded at that location.

[0080] (3) Multi-location fusion forms the comprehensive interference intensity:

[0081] Repeat the above calculation for all acquisition locations, and fuse all interference contribution values ​​received from the same grid cell. Fusion can be performed in two modes:

[0082] Cumulative mode: All contribution values ​​are directly summed to reflect the total interference energy received by the grid;

[0083] Maximum Optimal Mode: Records the maximum normalized contribution value experienced by the grid, reflecting the strongest instantaneous disturbance the grid has ever suffered. After fusion, the comprehensive disturbance intensity of each grid is obtained.

[0084] (4) Normalization and heatmap mapping:

[0085] The overall interference intensity of each grid is normalized and mapped to the 0-1 interval to obtain a normalized overall interference intensity matrix. A color gradient is used to map the normalized overall interference intensity matrix into a visualized interference heatmap; for example, red represents strong interference (normalized intensity ≥ 0.7), orange represents medium interference (0.4-0.7), yellow represents weak interference (0.1-0.4), and green represents no interference (< 0.1). When generating the heatmap, a display mask determined by whether the original grid is affected by at least one effective antenna contribution is used to restrict the heatmap to only the areas actually affected by monitoring, avoiding false diffusion caused by mathematical interpolation. Simultaneously, spatial interpolation algorithms such as Kriging interpolation can be used to smooth the discrete grid data, making the heatmap more continuously reflect the interference distribution.

[0086] The above process directly utilizes the raw interference signal strength data received by each antenna at each acquisition location. Through azimuth weighting, range attenuation, and multi-location fusion, the discrete acquisition data is transformed into an evaluation result of the interference intensity distribution in continuous space. The normalized comprehensive interference intensity matrix serves as the core input for subsequent refinement and single-point situational awareness construction.

[0087] 5. Refinement: Based on the interference heatmap, identify the area with the highest interference intensity and further narrow the interference range to a core area within 2 kilometers. Output a regional situation report, clarifying the interference distribution, peak location, and intensity trend within the core area. The peak location is obtained by extracting the grid coordinates corresponding to the maximum intensity value from the normalized integrated interference intensity matrix; the geographic center coordinates of this grid are the peak location. If multiple discrete high-value grid clusters exist, extract the peak value for each cluster and output multiple candidate peak locations.

[0088] III. Single-point situational awareness construction and precise positioning

[0089] Within the locked core area, detailed monitoring data for each trajectory point is collected using a satellite navigation receiver (which can also be integrated into the mobile detection vehicle equipment). The interference situation at each trajectory point is accurately assessed, and the direction of the interference source is determined by a multi-directional antenna array. The precise location of the interference source is then achieved by combining the multi-point direction finding results.

[0090] Specifically, it includes the following steps:

[0091] 1. Single-point data acquisition: Within the locked core area of ​​2 kilometers, the mobile vehicle travels slowly (speed ≤20km / h), and the satellite navigation receiver simultaneously acquires detailed monitoring data for each trajectory point, including the number of visible satellites, carrier-to-noise ratio (C / N0), pseudorange residual, positioning error, number of times the lock is lost, etc. At the same time, the multi-element directional antenna array simultaneously acquires data such as the direction of arrival of the interference signal and the strength of the interference signal for that trajectory point.

[0092] 2. Single-point interference assessment: Detailed analysis of monitoring data for each trajectory point is performed to determine the type and intensity of interference at that point: If the carrier-to-noise ratio (C / N0) drops sharply or the number of satellite lock-offs increases, it is determined to be suppression interference; if the pseudorange residual changes abruptly, the positioning error increases abnormally, or the position jumps, it is determined to be deception interference; combined with the interference signal strength, the interference level at that point is quantitatively assessed.

[0093] 3. Interference source direction finding: Using spatial spectrum or interferometer direction finding methods, the direction of arrival of the interference source is calculated by collecting interference signal data through a multi-element directional antenna array; at least three different trajectory points are selected in the core area for direction finding to obtain multiple incoming wave direction data;

[0094] 4. Precise positioning: Cross-location is performed on the direction-finding data of multiple trajectory points, and the precise location of the interference source is calculated by combining the position information of each trajectory point with the interference intensity data.

[0095] The specific process is as follows: Starting from the position of each direction finding point, a direction finding observation equation is constructed by drawing a direction finding line based on the direction of incoming waves measured at that point. A weighted error minimization model is established using the interference intensity or direction finding confidence level of each direction finding point as weights. The optimal intersection point of each direction finding line is then solved using a nonlinear least squares optimization algorithm. The coordinates of this intersection point represent the precise location of the interference source, with a positioning error ≤ 50 meters. Simultaneously, a single-point situation map is generated, overlaying the positions of each direction finding point, the direction finding line, and the interference level on the map, marking the precise location of the interference source. A single-point situation report is output, clearly specifying the interference situation, the direction of incoming waves from the interference source, and the precise location of the interference source for each trajectory point.

[0096] Regional situation map as follows Figure 2 As shown, with the eastern industrial zone of a land-based city (target area within 18 kilometers) as the geographical background, the road network distribution is overlaid, and the interference intensity is represented by a color gradient, where green areas represent no interference, yellow areas represent weak interference, orange areas represent moderate interference, and red areas represent strong interference. The core interference area (central part of the industrial zone, within 1.5 kilometers) is clearly marked in the figure, along with the peak interference location (coordinates: 39°54′N, 116°23′E) and interference intensity (-85dBm). The travel paths of mobile vehicles and data collection points are also marked, intuitively presenting the interference distribution pattern and intensity change trend within the area, providing accurate guidance for subsequent single-point investigation.

[0097] IV. Interference Source Investigation and Handling: Based on the precise location of the identified interference source, personnel will be dispatched with portable monitoring equipment to the site to verify and investigate the interference source, thus completing the satellite navigation interference investigation.

[0098] Although the present invention has been described herein with reference to illustrative embodiments, the above embodiments are merely preferred embodiments of the present invention, and the implementation of the present invention is not limited to the above embodiments. It should be understood that those skilled in the art can devise many other modifications and implementations, which will fall within the scope and spirit of the principles disclosed in this application.

Claims

1. A satellite navigation interference investigation method based on situational awareness fusion, characterized in that, include: Step S1: Construct a global situation and perform coarse localization to obtain the target area; Step S1 includes: Collect core data; The core data undergoes preprocessing and interference feature extraction; the interference feature extraction includes: Interference features of ADS-B data, AIS data, and base station open-source data are extracted from the preprocessed valid data, including: The interference features of the ADS-B data include positioning accuracy degradation features, trajectory breakage features, and navigation accuracy drop features. The positioning accuracy degradation feature is identified by judging whether the position deviation exceeds a preset threshold. The trajectory breakage feature is identified by analyzing the abnormal signal reception of continuous trajectory interruption points. The navigation accuracy drop feature is identified by judging potential anomalies by a sudden drop in navigation accuracy category parameters. The interference features of the AIS data include position drift features, positioning inaccuracy features, and speed and heading mismatch features; the position drift feature is identified by the ship's position deviation threshold, the positioning inaccuracy feature is identified by the consistency analysis between position, speed and heading, and the speed and heading mismatch feature is captured by motion state anomaly analysis; The interference characteristics of the base station open-source data include a sudden drop in signal-to-noise ratio, an increase in noise floor, and an abnormal increase in positioning error; these characteristics are identified through spectrum monitoring and threshold judgment. After fusing and analyzing the extracted interference features, a global situational awareness is constructed, and the range of satellite navigation interference is locked to the target area, specifically including: Weights are assigned based on the category and severity of the interference features, and the interference feature points are obtained by weighted summation. All interference feature points are mapped to a unified spatiotemporal coordinate system and clustered according to spatial and temporal density. The spatial center and coverage of each cluster are obtained by comprehensively considering the weight and density of interference feature points within the cluster, thereby identifying the core interference area. Based on the location and intensity information of the interference area, the interference range is locked to the target area, and a global situation report is output. Step S2: Plan a driving path according to the target area and collect interference data along the driving path. After analyzing, fusing and normalizing the interference data, map it into an interference heat map, construct the regional situation and narrow the interference range to obtain the core area. Step S3: Collect monitoring data and interference signal data for each preset trajectory point in the core area, calculate the precise location of the interference source based on the monitoring data and interference signal data, construct a single-point situation and locate the interference source.

2. The satellite navigation interference investigation method based on situational fusion according to claim 1, characterized in that, The core data includes Automatic Dependent Surveillance-Broadcast (ADS-B) data, Automatic Identification System (AIS) data, and base station open-source data. The preprocessing refers to cleaning, denoising, time synchronization, and coordinate alignment of the ADS-B data, AIS data, and base station open-source data, as well as removing abnormal data, invalid data, and noise interference, and retaining valid data.

3. The satellite navigation interference investigation method based on situational fusion according to claim 1, characterized in that, Step S2 includes: The navigation interference detection device is installed on a mobile vehicle. The navigation interference detection device uses a multi-directional antenna array to receive interference signals from multiple directions. Based on the geographical environment and road network distribution of the target area, plan the driving route of the mobile vehicle; The mobile vehicle travels along the driving path and collects interference data of the navigation frequency band in the target area in real time; The interference data is analyzed and fused to obtain the comprehensive interference intensity of each grid in the target area. After normalizing the comprehensive interference intensity of each grid, it is mapped to a visualized interference heatmap. Based on the interference heatmap, the area with the highest interference intensity is identified, and the interference range is further narrowed down to the core area. A regional situation report is output to clarify the interference distribution, peak interference location, and interference intensity change trend in the core area.

4. The satellite navigation interference investigation method based on situational fusion according to claim 3, characterized in that, The steps of analyzing and fusing the interference data to obtain the comprehensive interference intensity of each grid in the target area include: Determine the grid within the influence range of the acquisition location. For each affected grid, calculate the distance and azimuth angle of the grid relative to the acquisition location, and determine the angle weight and distance weight of each antenna of the mobile vehicle. For each antenna, if the interference signal strength received by the antenna at the acquisition location is higher than the preset effective threshold, the measured interference signal strength is weighted by the angle weight and distance weight corresponding to the antenna to obtain the single antenna contribution value of the antenna to the grid; if the interference signal strength received by the antenna at the acquisition location is not higher than the preset effective threshold, the antenna does not generate a single antenna contribution value. The total interference contribution of the sampling location to the grid is obtained by summing the individual antenna contribution values ​​of each antenna. Repeat the above calculation for all acquisition locations, and fuse all the total interference contribution values ​​received from the same grid to obtain the comprehensive interference intensity of each grid.

5. The satellite navigation interference investigation method based on situational fusion according to claim 1, characterized in that, Step S3 specifically includes: Within the core area, the mobile vehicle travels at a preset speed, and the satellite navigation receiver installed on the mobile vehicle synchronously collects monitoring data for each preset trajectory point; at the same time, the navigation interference detection equipment installed on the mobile vehicle synchronously collects interference signal data for that trajectory point through a multi-directional antenna array, and the interference signal data includes the direction of arrival of the interference signal and the strength of the interference signal; The monitoring data of each trajectory point is analyzed to determine the type and intensity of interference at that trajectory point; and the interference level of the trajectory point is quantitatively evaluated based on the interference intensity. Interference signal data from multiple trajectory points were selected, and the direction of the interference source was determined using either spatial spectrum or interferometer direction finding methods. Cross-location is performed on the direction-finding data of multiple trajectory points. By combining the position information of each trajectory point with the interference intensity, the precise location of the interference source is calculated, a single-point situation map is generated, and a single-point situation report is output.

6. The satellite navigation interference investigation method based on situational fusion according to claim 5, characterized in that, The steps for calculating the precise location of the interference source and generating a single-point situation map include: Starting from the position of each direction finding point, and drawing the direction line of the incoming wave measured by that direction finding point, a direction finding observation equation is constructed. Using the interference intensity or direction finding confidence of each direction finding point as weights, a weighted error minimization model is established. The optimal intersection point of each direction line is solved by a nonlinear least squares optimization algorithm. The coordinates of the optimal intersection point are the precise location of the interference source. The location of each direction finding point, direction lines, and interference level are overlaid on the map to mark the precise location of the interference source and generate a single-point situation map.

7. The satellite navigation interference investigation method based on situational fusion according to claim 1, characterized in that, It also includes verifying, investigating, and retesting the sources of interference in the positioning.

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