A ground GPS jammer positioning method based on airborne data

Abstract

The present application relates to the technical field of wireless communication, especially to a ground GPS interference source positioning method based on airborne data, which comprises: signal state acquisition; interference identification and recording; loss of lock rate acquisition; reliability determination; flight state acquisition; geometric verification and key point extraction; positioning parameter acquisition; antenna directivity map transformation and attenuation grid modeling; single point heat map generation; multi-source data fusion; parameter correction. The present application solves the problem of interference positioning failure caused by multi-path and shielding effect pollution of observation data by using the joint criterion of signal strength threshold and signal loss of lock rate, introducing high-fidelity signal propagation modeling of effective data screened by signal and flight geometry verification, weighting fusion of generated multi-source heterogeneous map, and interference positioning area screened by combining adaptive closed loop adjustment, improving the precise positioning ability and efficiency of suppression type GPS interference source, and ensuring the safety of aviation operation.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method for locating ground-based GPS interference sources based on airborne data. Background Technology

[0002] With the increasing frequency of GPS interference events in the airspace, the need for rapid and accurate positioning is extremely urgent. However, dense buildings cause severe multipath reflection and blockage of signals, resulting in received signals being a mixture of direct and reflected paths, seriously contaminating observation data. Traditional positioning methods based on single-point direction finding or time difference of arrival suffer a significant drop in positioning accuracy under the combined effects of complex electromagnetic environments and multipath effects, often failing to meet the timeliness and accuracy requirements for interference source localization in practical applications. Existing interference source localization technologies face severe challenges in complex environments.

[0003] Chinese Patent Application Publication No. CN115480271A discloses a GPS interference source localization method, apparatus, electronic device, and readable storage medium. The method includes: acquiring a first signal strength of a GPS interference signal received by a flight device at a first moment, and acquiring a second signal strength of the GPS interference signal received by the flight device at a second moment, wherein the first moment refers to the starting moment when the position of the flight device cannot be acquired, and the second moment refers to the starting moment when the position of the flight device is reacquired, and the GPS interference signal is emitted by a GPS interference source located at a ground point; acquiring a confidence level of each ground point in a set of ground points as a location point of the GPS interference source based on the first signal strength and the second signal strength; and acquiring a predicted location point of the GPS interference source in the set of ground points based on the confidence level.

[0004] Therefore, the GPS interference source localization method, device, electronic device, and readable storage medium have the following problems: the method only relies on the signal strength at two discrete moments for calculation, which is prone to being unable to resist multipath fluctuations and measurement noise due to a severe lack of sample size; the method does not take into account real-time changes in aircraft attitude when querying antenna gain, and static queries are prone to introducing huge pointing errors; the method does not provide a mechanism to distinguish between direct signals and stable reflected signals from fixed reflectors such as buildings, which is prone to generating mirror positioning errors in complex environments. Summary of the Invention

[0005] To address this, the present invention provides a ground-based GPS interference source localization method based on airborne data. This method overcomes the problem of interference-induced localization failure caused by multipath and obstruction effects contaminating observation data in complex electromagnetic environments through a fusion localization mechanism that uses multi-level parameterized screening and dynamic propagation relationship identification.

[0006] To achieve the above objectives, the present invention provides a method for locating ground-based GPS interference sources based on airborne data, comprising: Estimate the signal strength of the GPS interference source at the antenna based on the GPS signal status obtained from the airborne data; Potential interference periods are identified based on the signal strength and a preset strength threshold, and the peak strength within the potential interference period is recorded. Obtain the GPS signal loss rate during the potential interference period; Based on the signal strength and the signal loss rate, it is determined whether the change in the interference signal during the potential interference period is reliable, and the determination result is used to determine whether the potential interference period is a period of stable interference. Obtain the latitude and longitude coordinates and flight speed of the aircraft during the period when the interference is stable; The target's critical moment is determined based on the track consistency, the peak intensity, and the flight speed, wherein the track consistency is determined based on the latitude and longitude coordinates and the signal strength; Obtain the aircraft's three-dimensional flight coordinates, flight attitude angles, and onboard GPS antenna pattern at key moments; Based on the preset grid side length, an attenuation grid is established with the flight three-dimensional coordinates as the center, and the theoretical attenuation value of the grid intersection point is determined by combining the correspondence between the flight attitude angle and the antenna pattern. A single-point decay heatmap is generated based on the theoretical decay value. The interference location area is determined by superimposing several single-point attenuation heatmaps within a preset spatiotemporal window using a superposition weight determined based on the peak intensity and the track consistency. The preset intensity threshold is adjusted based on the threshold screening results of the divergence of the interference positioning area within the preset statistical period.

[0007] Furthermore, the process of identifying potential interference periods based on the signal strength and a preset strength threshold, and recording the peak intensity within the potential interference periods, includes: The interference start time and interference termination time are determined based on the comparison between the signal strength and the preset strength threshold. The time window between the start time of the interference and the end time of the interference is recorded as the potential interference period. The maximum value of the signal strength during the potential interference period is determined as the peak strength.

[0008] Furthermore, the process of determining whether the interference signal change during the potential interference period is reliable based on the signal strength and the signal loss rate, and determining the potential interference period as an interference-stable period based on the determination result, includes: The trend stability of the interference signal is determined based on the fluctuation trend and variation concentration characteristics of the signal strength during the potential interference period, so as to obtain the interference variation stability. Calculate the average signal loss rate during the potential interference period to obtain the average loss rate; The correlation coefficient is determined based on the variation characteristics of the signal strength and the signal loss rate during the potential interference period; Based on the threshold screening results of the interference change stability, the mean of the lockout, and the correlation coefficient of the change, the signal change during the potential interference period is determined to be reliable, and the potential interference period is determined to be an interference stable period.

[0009] Furthermore, the process of determining the trend stability of the interference signal based on the fluctuation trend and variation concentration characteristics of the signal strength during the potential interference period, in order to obtain the interference variation stability, includes: The potential interference period is divided into several interference windows based on a preset window length, and the slope of several intensity fitting lines determined based on the temporal relationship of the signal strength within each interference window is recorded as the local trend quantization value of the signal strength within that interference window, so as to obtain several local trend quantization values. The monotonicity index is obtained by calculating the proportion of the sum of the durations during which the local trend quantification value is continuously positive or continuously negative to the total duration of the potential interference period. The determination coefficient of linear fitting is calculated based on the univariate linear regression fitting line corresponding to the signal strength at each time point within the potential interference period, so as to obtain the fitting determination coefficient. The geometric mean of the fitting determination coefficient and the monotonicity index is calculated to obtain the stability of the disturbance change.

[0010] Furthermore, the process of determining the correlation coefficient based on the variation characteristics of the signal strength and the signal loss rate during the potential interference period includes: Calculate the relative deviation of the signal strength between any two adjacent moments within the potential interference period to obtain several stable strength deviations; Calculate the relative deviation of the signal loss rate between any two adjacent moments within the potential interference period to obtain several loss deviations; Calculate the Pearson correlation coefficient between the stability strength deviation and the unlocking deviation to obtain the change correlation coefficient.

[0011] Furthermore, the critical moment of the target is determined based on the track consistency, the peak intensity, and the flight speed, wherein the process of determining the track consistency based on the latitude and longitude coordinates and the signal strength includes: Calculate the relative distance between the latitude and longitude coordinates of two adjacent moments within the period of disturbance stability to obtain the distance between several adjacent locations; Calculate the relative deviation of the distance between the adjacent positions to obtain several adjacent distance deviations, and calculate the relative deviation of the signal strength between two adjacent moments within the interference stabilization period to obtain several target interference deviations; Calculate the Pearson correlation coefficient between the adjacent distance deviation and the target interference deviation to obtain the track consistency. The time corresponding to the peak intensity is recorded as the peak time, and when the track consistency is greater than or equal to a preset consistency threshold, and the flight speed corresponding to the peak time is less than a preset speed threshold, the peak time is determined as the target critical time.

[0012] Furthermore, the process of establishing an attenuation grid centered on the flight three-dimensional coordinates based on a preset grid side length, and determining the theoretical attenuation value at the grid intersection point by combining the correspondence between the flight attitude angle and the antenna pattern, includes: Construct a square region with the flight three-dimensional coordinates as the center point of the ground projection and a preset length threshold as the side length, and divide the square region into several grid units based on the preset grid side length; Obtain the three-dimensional coordinates of the grid intersection points of each grid cell to obtain several intersection point coordinates; Calculate the straight-line distance between the coordinates of each intersection point and the three-dimensional coordinates of the flight to obtain several attenuation distances, and calculate the spatial attenuation value based on the attenuation distances; Based on the correspondence between the flight attitude angle change, the aircraft's three-dimensional coordinate change, and the antenna pattern, the directional pattern gain of the airborne GPS antenna corresponding to the grid point is calculated through coordinate transformation. The spatial attenuation value and the directional pattern gain are summed to obtain the theoretical attenuation value at each grid intersection.

[0013] Furthermore, the process of generating a single-point decay heatmap based on the theoretical decay value includes: Using the minimum value among the theoretical attenuation values ​​as a benchmark, the theoretical attenuation values ​​are classified into several levels according to a preset attenuation gradient to obtain several interference levels. Different interference levels are distinguished by different colors to obtain the single-point attenuation heatmap.

[0014] Furthermore, the process of determining the interference location area based on the fused heatmap obtained by superimposing several single-point attenuation heatmaps within a preset spatiotemporal window according to the superposition weight determined based on the peak intensity and the track consistency includes: The fusion weight of each single-point attenuation heatmap is calculated based on the weighted contribution of the peak intensity and the track consistency. The weighted geometric center is calculated based on the fusion weight and the ground projection center point of the single-point attenuation heatmap; Construct a comprehensive square region with the weighted geometric center as the ground projection center point and the preset length threshold as the side length, and divide the comprehensive square region into several comprehensive grid units based on the preset grid side length to establish a comprehensive attenuation grid, and take the average of the theoretical attenuation values ​​of the grid intersections of the several single-point attenuation heatmaps as the comprehensive attenuation value of each of the comprehensive grid intersections in the comprehensive attenuation grid; Using the minimum value among the comprehensive attenuation values ​​as a benchmark, the comprehensive attenuation values ​​are classified into several levels according to the preset attenuation gradient to obtain several comprehensive interference levels. Different comprehensive interference levels are distinguished by different colors to obtain the fusion heat map. When the overall interference level is greater than a preset level threshold, the area corresponding to the overall interference level in the fused heatmap is determined to be the interference location area.

[0015] Furthermore, the process of adjusting the preset intensity threshold based on the threshold screening results of the divergence of the interference positioning area within the preset statistical period includes: Obtain and calculate the average longitude and average latitude of each grid intersection point in the interference location area to obtain several representative interference coordinates; Obtain and calculate the average values ​​of the longitude and latitude of the interference-representing coordinates respectively to obtain the geometric center; Calculate the distance between each of the interference representative coordinates and the geometric center to obtain several interference center distances, and calculate the standard deviation of all interference center distances to obtain the divergence. When the divergence is greater than a preset divergence threshold, the preset intensity threshold is adjusted according to the relative deviation between the divergence and the preset divergence threshold.

[0016] Compared with existing technologies, the advantages of this invention lie in its construction of a multi-level parameterized screening and fusion positioning mechanism for complex electromagnetic environments, fundamentally improving the reliability and practicality of GPS interference source positioning. By using a joint criterion of signal strength threshold and signal loss rate, background noise and invalid data caused by severe obstruction are effectively eliminated, laying a foundation for highly reliable data. By introducing track consistency to dynamically verify the signal and flight geometry, multipath spoofing signals generated by reflections from fixed buildings are fundamentally identified and eliminated, ensuring that the positioning core relies solely on observations of the actual direct path. Furthermore, by fusing flight attitude angles and antenna patterns to perform high-fidelity signal propagation modeling, the gain changes in the directional pattern caused by dynamic changes in aircraft attitude are compensated, significantly improving the spatial accuracy of single-point positioning. In addition, by weighted fusion of multi-source heterogeneous maps and adaptive closed-loop adjustment of the density threshold based on the divergence of the output positioning area, the entire method possesses the ability to output stable, convergent, and reliable interference source positioning capabilities in complex electromagnetic environments, effectively solving the problem of interference positioning failure caused by multipath and obstruction effects contaminating observation data in complex electromagnetic environments.

[0017] Furthermore, by automatically comparing continuous signal strength time series with preset fixed thresholds, accurate detection and segmentation of abnormal signal segments exceeding the thresholds are achieved in the time domain. This not only defines the start and end boundaries of each interference event, thereby extracting independent potential interference periods, but also simultaneously extracts the most representative signal strength features, i.e., peak intensity, within each period, providing the most crucial initial observations for all subsequent positioning calculations based on signal attenuation models. By condensing a signal that changes over a continuous period into a single-point feature representing the maximum probable proximity of the event, the complexity of subsequent spatial geometric calculations is simplified. This outputs structured event data packets with core strength labels for the entire positioning process, which is a prerequisite for subsequent multi-level reliability screening and high-precision fusion, ensuring the input quality and consistency of the processing flow.

[0018] Furthermore, by constructing a three-dimensional joint decision threshold consisting of interference change stability, mean loss of lock, and correlation coefficient, a refined assessment and screening of signal quality during potential interference periods was achieved. Specifically, interference change stability excluded segments of drastic signal fluctuations and trend disturbances caused by multipath interference from both temporal and trend perspectives; the mean loss of lock threshold directly quantified and filtered observation data under severe physical obstruction, ensuring data acquisition in spaces with the possibility of direct sunlight paths; and by introducing the statistical correlation coefficient, the highly synchronized phenomenon between signal intensity fluctuations and satellite loss of lock events within short time windows could be identified, thus eliminating unreliable periods of strong signal-environment coupling caused by dynamic obstruction or complex reflection paths. The synergy of these three factors ensures that the data ultimately determined to be from interference-stable periods simultaneously meets the high-quality standards of stable signal, open environment, and signal changes independent of obstruction dynamics. This provides a reliable input data foundation for subsequent high-precision positioning based on geometric consistency, fundamentally reducing the risk of mispositioning caused by introducing contaminated data into positioning calculations.

[0019] Furthermore, by constructing a multi-dimensional fusion quantification index for interference change stability, a comprehensive and detailed assessment of signal quality, from micro-fluctuations to macro-trends, was achieved. The local trend quantification value and monotonicity index, from a time-domain dynamic perspective, collaboratively characterize the directional consistency and purity of signal changes, identifying frequent trend reversals caused by multipath interference. The fitting decision coefficient further evaluates the predictability and linearity of signal changes from the perspective of overall trend explanatory power. Finally, the geometric mean is used to fuse these three independent features representing fluctuation, trend, and linearity into a comprehensive stability score. This not only overcomes the limitations of a single criterion but also establishes a multi-criteria joint decision-making model strongly correlated with the physical characteristics of direct signals. This lays a crucial foundation of data reliability for subsequent processes to confidently select high-quality observation periods with stable, monotonic, and clearly defined trends.

[0020] Furthermore, by quantifying the Pearson correlation coefficient between the relative changes in signal strength and signal loss rate over consecutive time intervals, a key correlation index capable of sensitively diagnosing the physical root causes of signal distortion was constructed. This correlation coefficient can dynamically capture the microscopic coordinated change pattern between signal strength fluctuations and reception environment deterioration, i.e., satellite loss of lock-up. Specifically, when the coefficient shows a significant positive correlation, it clearly reveals that signal instability and physical obstruction events are highly synchronized in the time domain, thus statistically determining the physical nature of the signal being dominated by multipath or non-line-of-sight propagation during that period. By calculating the correlation coefficient, a deeper diagnostic basis based on causal relationships is provided for reliability assessment, which is not available in traditional single-parameter analysis. This eliminates deceptive data segments that, while seemingly acceptable in individual indicators, reveal underlying mechanisms indicating severe environmental contamination, thereby enhancing the discriminative power and robustness of front-end data screening.

[0021] Furthermore, by calculating the Pearson correlation coefficient of the relative deviation sequence between displacement change and signal change, a key geometric verification index, track consistency, was constructed. This physically identifies whether signal changes strictly follow a linear relationship with flight distance, thus eliminating deceptive data where the signal and trajectory are disconnected in a multipath environment. Based on this, the peak moment with the strongest signal in the verified period was set as a candidate point, and observation points with insufficient geometric information in low-speed or hovering states were excluded by using a flight speed threshold. Ultimately, it was ensured that the determined target key moment simultaneously met three core conditions: the signal source was a direct path, the signal strength reached a local optimum, and the carrier was in an effective motion state. The peak moment that met the conditions was determined as the target key moment, which theoretically corresponds precisely to the moment with the closest distance and the clearest geometric relationship. In contrast, the initial moment of the interference stabilization period is only the point where the signal first exceeds the threshold. This point may be located at any position on the rising edge of the signal, and the actual geometric distance from the interference source is uncertain and usually far, lacking optimal observation geometric conditions. This logic condenses continuous flight paths and signal sequences into an optimal spatiotemporal reference point with both high geometric reliability and high observational information, providing a unique and reliable input for subsequent precise positioning based on a single-point attenuation model. This constitutes a key transformation link in extracting static positioning data from dynamic data.

[0022] Furthermore, through systematic spatial discretization and physical modeling, high-fidelity simulation and efficient computation of complex propagation environments were achieved. By constructing a regularized grid centered on the ground projection of the warning point, the continuous spatial search problem was transformed into parallel computation of discrete grid points, significantly improving computational efficiency and ensuring the systematic nature of the search. In the computation, the free-space propagation model and dynamic antenna pattern correction were integrated to accurately quantify path loss and attitude-related gain, ensuring that the final theoretical attenuation value strictly follows the physical laws of radio wave propagation. This effectively compensates for the differences in receiver sensitivity introduced by changes in aircraft attitude, laying a precise data foundation for generating single-point attenuation heatmaps with clear physical meaning. This is a key step in transforming observational data into a spatial theoretical value distribution.

[0023] Furthermore, through normalized level mapping and visualization encoding, abstract attenuation field data is transformed into intuitive single-point attenuation heatmaps. Normalization based on the minimum attenuation value effectively eliminates the interference of absolute numerical differences on visualization contrast, ensuring consistency of heatmaps under different observation conditions. Discretized level division is achieved through preset attenuation gradients, reducing data complexity and visualization redundancy while preserving the core characteristics of the attenuation field's spatial distribution. Color coding distinguishes interference levels, conforming to human-computer interaction habits, enabling operators to quickly identify the core area with the minimum attenuation—the interference source area—improving situational awareness efficiency and decision-making intuitiveness. The final output of standardized single-point attenuation heatmaps provides a structurally unified and visually clear input foundation for subsequent multi-source data fusion.

[0024] Furthermore, by constructing an adaptive weighted fusion mechanism based on data quality, the integration accuracy and reliability of multi-source heterogeneous positioning results were significantly improved. By dynamically calculating the fusion weights of each point's heatmap using peak intensity and track consistency, observations with high signal quality and high geometric reliability were ensured to dominate the fusion process, thereby suppressing the influence of low-quality or contaminated data. The reference origin of the unified fusion grid was dynamically established by weighted geometric centers, enabling the fusion process to adaptively align the most reliable regions of each observation, optimizing the targeting of the spatial search. Finally, by uniformly normalizing and discretizing the comprehensive attenuation values ​​of grid points, an intuitive fusion heatmap was generated, and interference positioning areas were automatically extracted using preset level thresholds. Overall, by simultaneously adopting a combination of multi-flight monitoring and multi-point directional approximation, the uncertainty of multiple batches of observations was transformed into a stable and convergent fusion heatmap, effectively overcoming the random errors of single-point positioning and achieving maximum likelihood estimation of the interference source location.

[0025] Furthermore, by quantifying the divergence of historical positioning results, the rationality of the current preset intensity threshold can be objectively assessed. Excessive divergence indicates a low threshold, incorporating too much noise or unreliable signals. Based on this assessment, a nonlinear adjustment of the relative deviation is introduced, making the adjustment magnitude proportional to the severity of the problem. This achieves sensitive and robust negative feedback control, dynamically adapting to the constantly changing urban electromagnetic environment. Without human intervention, it automatically seeks the optimal balance between interference detection sensitivity and positioning result convergence, fundamentally improving the long-term reliability and environmental robustness of the entire positioning method. Attached Figure Description

[0026] Figure 1 This is a flowchart of the ground GPS interference source localization method based on airborne data in this embodiment; Figure 2 This embodiment presents a logic diagram for determining whether signal changes during potential interference periods are reliable. Figure 3 This embodiment defines the decision logic for determining the critical moment of the target. Figure 4 This embodiment defines the logic diagram for determining the adjustment of the preset intensity threshold. Detailed Implementation

[0027] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0028] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0029] Please see Figure 1 The diagram shows a flowchart of the ground GPS interference source localization method based on airborne data in this embodiment. This embodiment provides a ground GPS interference source localization method based on airborne data, including: Estimate the signal strength of the GPS interference source at the antenna based on the GPS signal status obtained from the airborne data; Potential interference periods are identified based on the signal strength and a preset strength threshold, and the peak strength within the potential interference period is recorded. Obtain the GPS signal loss rate during the potential interference period; Based on the signal strength and the signal loss rate, it is determined whether the change in the interference signal during the potential interference period is reliable, and the determination result is used to determine whether the potential interference period is a period of stable interference. Obtain the latitude and longitude coordinates and flight speed of the aircraft during the period when the interference is stable; The target's critical moment is determined based on the track consistency, the peak intensity, and the flight speed, wherein the track consistency is determined based on the latitude and longitude coordinates and the signal strength; Obtain the aircraft's three-dimensional flight coordinates, flight attitude angles, and onboard GPS antenna pattern at key moments; Based on the preset grid side length, an attenuation grid is established with the flight three-dimensional coordinates as the center, and the theoretical attenuation value of the grid intersection point is determined by combining the correspondence between the flight attitude angle and the antenna pattern. A single-point decay heatmap is generated based on the theoretical decay value. The interference location area is determined by superimposing several single-point attenuation heatmaps within a preset spatiotemporal window using a superposition weight determined based on the peak intensity and the track consistency. The preset intensity threshold is adjusted based on the threshold screening results of the divergence of the interference positioning area within the preset statistical period.

[0030] In this embodiment, the ground-based GPS interference source localization method based on airborne data is applied to the rapid investigation and accurate location of intentional or unintentional interference sources in the GPS frequency band of aircraft in the flight path airspace. When multiple aircraft are flying over or approaching the airspace, their airborne equipment detects abnormal GPS signal quality. At this time, multi-level parameterized screening and dynamic propagation relationship identification are used to remove the pollution data caused by building obstruction and multipath reflection in the complex electromagnetic environment. By comparing the signal attenuation of the interference source propagation path, the area with the highest power energy of the interference signal source is inferred in reverse, so as to obtain a high-confidence interference source location distribution. Signal strength refers to the quantitative representation of the power or amplitude of the radio signal received by the airborne GPS receiver, radiated from ground interference sources and propagated to the aircraft in the air. GPS signal loss rate is the ratio of the number of satellites lost from lock and tracking per unit time to the total number of tracked satellites. It is a direct indicator of the severity of physical obstruction of satellite signals caused by the current environment. It can be directly statistically analyzed and output by the internal status monitoring unit of the airborne GPS receiver. The receiver continuously monitors the satellite signal lock status of each channel. Once a satellite signal falls below the lock threshold due to obstruction or other reasons, it is recorded as a loss of lock, and the loss rate within a specific time period is calculated. Flight three-dimensional coordinates are the longitude, latitude, and altitude of the aircraft in a specific reference frame, such as the WGS-84 geodetic coordinate system. They accurately describe the aircraft's spatial position and are continuously provided during flight by the multi-mode satellite navigation / inertial navigation system standard on modern aircraft. They can also be obtained through ARINC. The data outputs are from standard aviation data interfaces such as ARINC 429 or the NMEA-0183 protocol, recorded by the airborne data recorder or acquired in real time via data link; flight speed refers to the horizontal speed of the aircraft relative to the ground, used to quantify the rate of change of the aircraft's trajectory, and is provided by direct measurement from the airborne multi-mode satellite navigation receiver, or by the satellite navigation / inertial navigation combination system through Kalman filtering fusion calculation, and is output in real time along with the flight status data stream through standard aviation data buses such as ARINC 429 or general protocols such as NMEA-0183; flight attitude angles refer to the three Euler angles describing the aircraft's body coordinate system relative to the local geographic coordinate system, namely pitch angle, roll angle, and yaw angle, which can be provided in real time by the airborne inertial measurement unit or attitude and heading reference system through fusion gyroscope and accelerometer data, and output through aviation data buses such as ARINC 429.

[0031] In this embodiment, the mapping relationship between signal state estimation and the signal strength at the antenna location of the GPS interference source is shown in the following table:

[0032] In this embodiment, the antenna pattern is a key parameter used to describe the spatial distribution characteristics of the antenna's radiated electromagnetic field. It includes two types: amplitude and phase patterns. The pattern can be used to illustrate the antenna's ability to transmit or receive electromagnetic waves in various spatial directions. For an airborne GPS antenna, directivity represents its ability to receive GPS signal waves from GPS satellites in different directions. Since the aircraft's flight attitude varies at different stages of flight (e.g., takeoff, landing, level flight, turning), this change in flight attitude causes the orientation of the airborne GPS antenna to change in three-dimensional space, varying with the flight's elevation angle, ... The radiation pattern of a GPS antenna changes with variations in heading and roll angles. Therefore, different flight attitudes affect the reception of the GPS antenna. The received radiation pattern of a GPS antenna is a function related to flight altitude, aircraft heading angle, aircraft pitch angle, and aircraft roll angle. The antenna radiation pattern is a three-dimensional radiation pattern describing the variation of radiation pattern gain with spatial direction. As an inherent electromagnetic characteristic of the antenna, it is measured by the manufacturer in a microwave anechoic chamber using standard far-field measurement techniques and solidified into a gain data table. In actual positioning calculations, the actual radiation pattern gain of waves arriving from a specific direction on the ground is dynamically determined by consulting this static data table and combining it with real-time flight attitude angles, i.e., pitch, roll, and yaw.

[0033] The preset strength threshold is a signal strength limit used to initially determine whether the GPS signal is subject to ground-based co-channel interference. It depends on the background noise level of the airborne GPS receiver, the signal fluctuation range under typical conditions, and the required sensitivity for interference detection. It is typically set between 30dB-Hz and 40dB-Hz; in this embodiment, it is set to 35dB-Hz, which effectively filters out background noise and normal signal fluctuations, and stably identifies significant potential interference events. The preset grid side length is the geographical span of each square grid cell when establishing the ground attenuation grid. It depends on the balance between the positioning resolution requirements for searching interference sources in the flight airspace and the system's computing resources. It is typically set between 1 km and 5 km; in this embodiment, it is set to 2 km. When establishing an airspace covering approximately 200 km square, a fine search surface consisting of approximately 10,000 grid intersections is formed, thereby ensuring effective detection of interference sources and controlling the number of grid intersections within a reasonable range, ensuring algorithm efficiency and... Balancing positioning precision; the preset spatiotemporal window is the time and geographic spatial range defined when performing multi-source data fusion. It depends on the flight density of aircraft in the airspace, the potential mobility of interference sources, and the validity period of data synchronization. The time window is usually set between 10 minutes and 60 minutes, and the spatial window radius is usually set between 20 kilometers and 50 kilometers. In this embodiment, it is set to 30 minutes and 30 kilometers to ensure that enough flight observation data are fused to improve positioning reliability, while avoiding the introduction of irrelevant or outdated observation information due to excessive time or wide area. The preset statistical period is the statistical analysis time period on which the system evaluates positioning results and adaptively adjusts the density ratio threshold. It depends on the stability and adaptive adjustment response speed required by the system. It is usually set between 12 hours and 7 days. In this embodiment, it is set to 2 days to accumulate a sufficient number of positioning result samples to accurately assess the divergence trend of the interference positioning area, thereby making robust threshold adjustment decisions.

[0034] By constructing a multi-level parameterized screening and fusion positioning mechanism for complex electromagnetic environments, the reliability and practicality of GPS interference source positioning are fundamentally improved. Through a joint criterion of signal strength threshold and signal loss rate, background noise and invalid data caused by severe obstruction are effectively eliminated, laying a foundation for highly reliable data. By introducing track consistency to dynamically verify the signal and flight geometry, multipath spoofing signals generated by reflections from fixed buildings are fundamentally identified and eliminated, ensuring that the positioning core relies solely on observations of the actual direct path. Based on this, high-fidelity signal propagation modeling is performed by fusing flight attitude angles and antenna patterns, compensating for changes in directional pattern gain caused by dynamic changes in aircraft attitude, thereby significantly improving the spatial accuracy of single-point positioning. Furthermore, by weighted fusion of multi-source heterogeneous maps and adaptive closed-loop adjustment of the density threshold based on the divergence of the output positioning area, the entire method possesses the ability to output stable, convergent, and reliable interference source positioning capabilities in complex electromagnetic environments, effectively solving the problem of interference positioning failure caused by multipath and obstruction effects contaminating observation data in complex electromagnetic environments.

[0035] Specifically, the process of identifying potential interference periods based on the signal strength and a preset strength threshold, and recording the peak intensity within the potential interference periods, includes: When the signal strength is greater than the preset strength threshold, the corresponding time is recorded as the interference start time, and the times when the signal strength is less than or equal to the preset strength threshold are recorded as the interference end time, starting from the start time. The time window between the start time of the interference and the end time of the interference is recorded as the potential interference period. The maximum value of the signal strength during the potential interference period is determined as the peak strength.

[0036] By automatically comparing continuous signal strength time series with preset fixed thresholds, accurate detection and segmentation of abnormal signal segments exceeding the thresholds are achieved in the time domain. This not only defines the start and end boundaries of each interference event, thus extracting independent potential interference periods, but also simultaneously extracts the most representative signal strength features, i.e., peak intensity, within each period, providing crucial initial observations for all subsequent positioning calculations based on signal attenuation models. By condensing a signal varying over a continuous time period into a single-point feature representing the maximum probable proximity of the event, the complexity of subsequent spatial geometric calculations is simplified. This outputs structured event data packets with core strength labels for the entire positioning process, serving as a prerequisite for subsequent multi-level reliability screening and high-precision fusion, ensuring the input quality and consistency of the processing flow.

[0037] Please see Figure 2As shown, this is a logic diagram for determining whether the signal change during the potential interference period is reliable in this embodiment. In this embodiment, the process of determining whether the interference signal change during the potential interference period is reliable based on the signal strength and the signal loss rate, and determining whether the potential interference period is a stable interference period based on the determination result, includes: The trend stability of the interference signal is determined based on the fluctuation trend and variation concentration characteristics of the signal strength during the potential interference period, so as to obtain the interference variation stability. Calculate the average signal loss rate during the potential interference period to obtain the average loss rate; The correlation coefficient is determined based on the variation characteristics of the signal strength and the signal loss rate during the potential interference period; When the stability of the interference change is greater than a preset stability threshold, the average value of the lockout is less than a preset lockout threshold, and the correlation coefficient of the change is less than a preset correlation threshold, the signal change during the potential interference period is determined to be reliable, and the potential interference period is determined to be an interference stable period.

[0038] Otherwise, if the interference variation characteristics corresponding to the potential interference period are determined to be affected by multipath effects or complex propagation environments, and therefore lack the reliability for interference source localization analysis, then this period is not considered a valid observation period for interference source localization.

[0039] The preset stability threshold is a comprehensive evaluation threshold used to determine whether the changing trend of the interference signal meets the stability characteristics of the direct signal. It depends on the minimum requirements for stationarity and linearity in signal processing, the receiver's noise floor, and the impact of typical multipath environments. It is usually set between 0.65 and 0.85. In this embodiment, it is set to 0.75, which can effectively filter out unreliable data segments that cause drastic signal fluctuations or disordered trends due to multipath interference. The preset loss-of-lock threshold is a threshold value used to determine whether there is severe physical obstruction in the GPS receiving environment. It depends on the receiver's typical loss-of-lock rate in open environments and the tolerable degree of obstruction. Typically set between 15% and 40%, this embodiment sets it to 25%, which can reliably exclude harsh observation environments where the aircraft is in densely built-up areas or deep, narrow canyons where there is almost no direct path. The preset correlation threshold is a criterion threshold used to identify whether signal strength fluctuations and signal loss changes show a highly synchronous positive correlation. It depends on the upper limit of tolerance for the synergistic phenomenon of signal degradation caused by obstruction. It is typically set between 0.5 and 0.7, and this embodiment sets it to 0.6, which can keenly diagnose and eliminate those periods where signal strength changes are strongly coupled with physical obstruction events and are clearly dominated by non-line-of-sight or multipath events.

[0040] By constructing a three-dimensional joint decision threshold consisting of interference change stability, mean loss of lock, and correlation coefficient, a refined assessment and screening of signal quality during potential interference periods was achieved. Specifically, interference change stability excluded segments of drastic signal fluctuations and trend disturbances caused by multipath interference from both temporal and trend perspectives. The mean loss of lock threshold directly quantified and filtered observation data in environments with severe physical obstruction, ensuring data acquisition in spaces with the possibility of direct sunlight paths. By introducing the statistical correlation coefficient, a statistical correlation feature, the high synchronization between signal intensity fluctuations and satellite loss of lock events within short time windows could be identified, thus eliminating unreliable periods with strong signal-environment coupling caused by dynamic obstruction or complex reflection paths. The synergy of these three factors ensures that data ultimately determined to be from interference-stable periods simultaneously meets the high-quality standards of stable signal, open environment, and signal changes independent of obstruction dynamics. This provides a reliable input data foundation for subsequent high-precision positioning based on geometric consistency, fundamentally reducing the risk of mispositioning due to the introduction of contaminated data into positioning calculations.

[0041] Specifically, the process of determining the trend stability of the interfering signal based on the fluctuation trend and variation concentration characteristics of the signal strength during the potential interference period, in order to obtain the interference variation stability, includes: The potential interference period is divided into several interference windows based on a preset window length, and the signal intensity corresponding to the interference window is fitted with a first-order linear relationship using the least squares method to obtain several intensity fitting lines. The slope of each intensity fitting line is recorded as the local trend quantization value of the signal intensity within the interference window to obtain several local trend quantization values. The monotonicity index is obtained by calculating the proportion of the sum of the durations during which the local trend quantification value is continuously positive or continuously negative to the total duration of the potential interference period. The determination coefficient of linear fitting is calculated based on the univariate linear regression fitting line corresponding to the signal strength at each time point within the potential interference period, so as to obtain the fitting determination coefficient. The geometric mean of the fitting determination coefficient and the monotonicity index is calculated to obtain the stability of the disturbance change.

[0042] The preset window length is the time interval used to divide a continuous signal strength sequence into several sub-intervals for local trend fitting. It depends on the sampling rate of the signal strength, the typical maneuverability of the aircraft in the airspace, and the minimum time scale of the local trend to be captured. It is usually set between 5 and 20 seconds. In this embodiment, it is set to 10 seconds, which can effectively balance the suppression of local noise and the preservation of the true trend change when performing piecewise linear fitting on the signal strength sequence, thereby obtaining a stable and representative local trend quantization value.

[0043] By constructing a multi-dimensional fusion quantification index for interference variation stability, a comprehensive and detailed assessment of signal quality, from micro-fluctuations to macro-trends, was achieved. The local trend quantification value and monotonicity index, from a time-domain dynamic perspective, collaboratively characterize the directional consistency and purity of signal changes, identifying frequent trend reversals caused by multipath interference. The fitting decision coefficient further evaluates the predictability and linearity of signal changes from the perspective of overall trend explanatory power. Finally, geometric averaging integrates these three independent features—fluctuation, trend, and linearity—into a comprehensive stability score. This not only overcomes the limitations of a single criterion but also establishes a multi-criteria joint decision-making model strongly correlated with the physical characteristics of direct signals. This lays a crucial foundation of data reliability for subsequent processes to confidently select high-quality observation periods with stable, monotonic, and clearly defined trends.

[0044] Specifically, the process of determining the correlation coefficient based on the variation characteristics of the signal strength and the signal loss rate during the potential interference period includes: Calculate the relative deviation of the signal strength between any two adjacent moments within the potential interference period to obtain several stable strength deviations; Calculate the relative deviation of the signal loss rate between any two adjacent moments within the potential interference period to obtain several loss deviations; Calculate the Pearson correlation coefficient between the stability strength deviation and the unlocking deviation to obtain the change correlation coefficient.

[0045] By quantifying the Pearson correlation coefficient between the relative changes in signal strength and signal loss rate over consecutive time intervals, a key correlation index capable of sensitively diagnosing the physical root causes of signal distortion was constructed. This correlation coefficient dynamically captures the microscopic synergistic change patterns between signal strength fluctuations and reception environment deterioration, i.e., satellite loss of lock-up. Specifically, when the coefficient exhibits a significant positive correlation, it clearly reveals a high degree of temporal synchronization between signal instability and physical obstruction events, thus statistically determining the physical nature of the signal being dominated by multipath or non-line-of-sight propagation during that period. Calculating the correlation coefficient provides a deeper diagnostic basis based on causal relationships, which is lacking in traditional single-parameter analysis for reliability assessment. This eliminates deceptive data segments that, while seemingly acceptable in individual indicators, reveal underlying mechanisms indicating severe environmental contamination, thereby enhancing the discriminative power and robustness of front-end data screening.

[0046] Please see Figure 3 As shown, this is the logic diagram for determining the critical moment of the target in this embodiment. In this embodiment, the critical moment of the target is determined based on the track consistency, the peak intensity, and the flight speed. The process of determining the track consistency based on the latitude and longitude coordinates and the signal strength includes: Calculate the geodetic distance between the latitude and longitude coordinates at two adjacent moments within the period of disturbance stability to obtain the distance between several adjacent locations; Calculate the relative deviation of the distance between the adjacent positions to obtain several adjacent distance deviations, and calculate the relative deviation of the signal strength between two adjacent moments within the interference stabilization period to obtain several target interference deviations; Calculate the Pearson correlation coefficient between the adjacent distance deviation and the target interference deviation to obtain the track consistency. The time corresponding to the peak intensity is recorded as the peak time, and when the track consistency is greater than or equal to a preset consistency threshold, and the flight speed corresponding to the peak time is less than a preset speed threshold, the peak time is determined as the target critical time.

[0047] The preset consistency threshold is the lowest threshold value used to determine whether there is a significant linear correlation between changes in aircraft displacement and changes in signal strength. It depends on the minimum confidence requirement for the consistency of the geometric relationship of direct signal propagation and the system's tolerance to multipath interference. It is usually set between 0.7 and 0.9. In this embodiment, it is set to 0.8, which ensures that only those periods in which the signal strength changes strictly conform to the linear relationship with the flight distance are considered to have high geometric confidence in direct path observation data. The preset speed threshold is the lowest speed threshold value used to exclude observation data obtained by the aircraft at low speed or near hovering. It depends on the typical minimum ground speed of the aircraft when cruising in the terminal area or en route and the minimum movement speed required to maintain an effective geometric observation baseline. It is usually set between 300 km / h and 900 km / h. In this embodiment, it is set to 500 km / h, which can effectively avoid misjudging signal peaks with insufficient geometric information captured by the aircraft during low-speed maneuvers such as takeoff, landing, circling, or holding in the air as high-quality key observation moments.

[0048] By calculating the Pearson correlation coefficient of the relative deviation sequence between displacement change and signal change, a key geometric verification index, track consistency, was constructed. This physically identifies whether signal changes strictly follow a linear relationship with flight distance, thus eliminating deceptive data caused by the disconnect between signal and trajectory in multipath environments. Based on this, the peak moment with the strongest signal in the verified period was set as a candidate point. Observation points with insufficient geometric information in low-speed or hovering states were excluded using flight speed thresholds. Ultimately, this ensured that the determined target critical moment simultaneously met three core conditions: the signal source was a direct path, the signal strength reached a local optimum, and the carrier was in effective motion. The peak moment meeting these conditions was determined as the target critical moment, theoretically corresponding precisely to the moment with the closest distance and the clearest geometric relationship. In contrast, the initial moment of the interference stabilization period is merely the point where the signal first exceeds the threshold. This point may be located at any position on the rising edge of the signal, and its true geometric distance from the interference source is uncertain and usually quite far, lacking optimal observation geometric conditions. This logic condenses continuous flight paths and signal sequences into an optimal spatiotemporal reference point with both high geometric reliability and high observational information, providing a unique and reliable input for subsequent precise positioning based on a single-point attenuation model. This constitutes a key transformation link in extracting static positioning data from dynamic data.

[0049] Specifically, the process of establishing an attenuation grid centered on the flight three-dimensional coordinates based on a preset grid side length, and determining the theoretical attenuation value at the grid intersection point by combining the correspondence between the flight attitude angle and the antenna pattern includes: Construct a square region with the flight three-dimensional coordinates as the center point of the ground projection and a preset length threshold as the side length, and divide the square region into several grid units based on the preset grid side length; Obtain the three-dimensional coordinates of the grid intersection points of each grid cell to obtain several intersection point coordinates; Calculate the straight-line distance between the coordinates of each intersection point and the three-dimensional flight coordinates to obtain several attenuation distances, and calculate the spatial attenuation value based on the attenuation distances, where L D =a+20lgD+20lgF, where, L D is the spatial attenuation value, 'a' is the preset loss constant, 'D' is the attenuation distance, and 'F' is the preset frequency band threshold. Based on the correspondence between the flight attitude angle change, the aircraft's three-dimensional coordinate change, and the antenna pattern, the directional pattern gain of the airborne GPS antenna corresponding to the grid point is calculated through coordinate transformation. The spatial attenuation value and the directional pattern gain are summed to obtain the theoretical attenuation value at each grid intersection.

[0050] The preset grid side length is the length of each basic unit in the ground search grid. It depends on the balance between system positioning accuracy requirements and computing resources, and is typically set between 1 and 5 kilometers. In this embodiment, it is set to 2 kilometers, which allows for sufficiently fine sampling points in geographic space to achieve preliminary identification of the possible areas of the interference source. The preset length threshold is the side length of the established square search area, which depends on the effective propagation distance of the interference signal and the aircraft's flight altitude. It is typically set between 100 and 300 kilometers, and in this embodiment, it is set to 200 kilometers, which allows for the construction of a search area centered on the warning point projection that is sufficient to cover the most likely range of the interference source. The preset loss constant is a fundamental constant term in the free-space propagation loss formula. Its value depends on the distance and frequency unit system (kilometers and megahertz) used in the formula, and is typically a fixed value of 32.44. In this embodiment, it is set to 32.44, which provides a correct reference value for the formula when calculating the energy attenuation of radio waves propagating in space. The preset frequency band threshold is the radio frequency at which the ground GPS interference source operates, which depends on the downlink signal frequency of the target global navigation satellite system, typically GPS. The L1 band is set to 1575 MHz in this embodiment, which enables accurate signal propagation analysis of the GPS navigation frequency band most frequently interfered with in the civil aviation field.

[0051] In this embodiment, the process of calculating the directional gain of the airborne GPS antenna corresponding to the grid point through coordinate transformation is as follows: Each azimuth angle corresponds to an antenna gain on the three-dimensional radiation pattern of the GPS antenna. During flight, due to changes in latitude, longitude, altitude, and aircraft attitude, the azimuth angle of the ground interference source relative to the GPS antenna on the aircraft will also change. Given the location, latitude, longitude, altitude, and aircraft attitude of the ground interference source, the directional gain of the aircraft's GPS antenna in the direction of receiving the interference signal can be obtained: Assume the latitude and longitude of the ground point dry source is (E j N j The aircraft's location is (E, 0), p N p h p Based on the Haverisine formula, (E) j N j ,0) to the aircraft ground projection point (E p N p The distance d0 = R·hsin(0) is given by the equation d0 = R·hsin(0). -1 [hsin(N p -N j )]+cosN j ·cosN p ·hsin(E p -E j), where R is the Earth's radius, which is taken as 6371 km in this embodiment, and E j N j E p N p Both are expressed in radians, hsin(x) = sin 2 (X / 2), which is the semi-sine function, then the ground point (E) p N p The distance from 0 to the point in the air is:

[0052] In a three-dimensional coordinate system, the azimuth angle (θ, φ) can be used to describe the orientation of a coordinate point. θ represents the angle between the origin (the vector pointing from the aircraft to the coordinate point) and the positive Z-axis, also known as the polar angle. φ represents the angle between the projection of the vector from the origin onto the XOY plane (the ground) and the positive X-axis, also known as the azimuth angle. The polar angle θ = arctan(d0 / h) p ).

[0053] Assuming the aircraft's heading angle is head, the azimuth angle φ is the angle between the aircraft's flight vector in the XOY plane (i.e., its projection onto the ground) and true north. The formula is:

[0054] The gain of the airborne GPS antenna relative to the ground interference point can be obtained by querying the (θ, φ) direction. That is, by querying the inherent directivity pattern of the airborne GPS antenna based on (θ, φ), the actual receiving gain value of the antenna relative to the direction of the ground interference source under this attitude can be obtained.

[0055] Through systematic spatial discretization and physical modeling, high-fidelity simulation and efficient computation of complex propagation environments were achieved. By constructing a regularized grid centered on the ground projection of the warning point, the continuous spatial search problem was transformed into parallel computation of discrete grid points, significantly improving computational efficiency and ensuring the systematic nature of the search. In the computation, the free-space propagation model and dynamic antenna pattern correction were integrated to accurately quantify path loss and attitude-related gain. This ensured that the final theoretical attenuation value strictly followed the physical laws of radio wave propagation, effectively compensating for the differences in receiver sensitivity introduced by changes in aircraft attitude. This laid a precise data foundation for generating single-point attenuation heatmaps with clear physical meaning and was a key step in transforming observational data into a spatial theoretical value distribution.

[0056] Specifically, the process of generating a single-point decay heatmap based on the theoretical decay value includes: Using the minimum value among the theoretical attenuation values ​​as a benchmark, the theoretical attenuation values ​​are classified into several levels according to a preset attenuation gradient to obtain several interference levels. Different interference levels are distinguished by different colors to obtain the single-point attenuation heatmap.

[0057] The preset attenuation gradient is the signal strength variation interval used to convert continuous theoretical attenuation values ​​into discrete interference levels. It depends on the requirements for the visualization precision of the positioning results and the typical variation range of interference signal attenuation. It is usually set between 3dB and 10dB. In this embodiment, it is set to 5dB, which can clearly and intuitively distinguish the interference probability of different areas with color levels. This ensures that the heat map can effectively characterize the details of attenuation distribution while avoiding color redundancy and visual confusion caused by overly fine level division.

[0058] In this embodiment, different interference levels are distinguished by different colors to obtain the single-point attenuation heatmap. The minimum theoretical attenuation value is used as the base, and the change is 5dB as a level. Different levels are distinguished by different colors. The theoretical attenuation value of 0-5dB is red, the theoretical attenuation value of 6-10dB is orange, the theoretical attenuation value of 11-15dB is yellow, the theoretical attenuation value of 16-20dB is green, the theoretical attenuation value of 21-25dB is blue, the theoretical attenuation value of 26-30dB is gray, and the theoretical attenuation value of ≥26dB is white.

[0059] By employing normalized level mapping and visual encoding, abstract attenuation field data is transformed into intuitive single-point attenuation heatmaps. Normalization based on the minimum attenuation value effectively eliminates the interference of absolute numerical differences on visualization contrast, ensuring consistency of heatmaps under different observation conditions. Discretized level division is achieved through preset attenuation gradients, preserving the core characteristics of the attenuation field's spatial distribution while reducing data complexity and visualization redundancy. Color coding distinguishes interference levels, aligning with human-computer interaction habits and enabling operators to quickly identify the core area with the minimum attenuation—the interference source area—improving situational awareness efficiency and decision-making intuitiveness. The final output of standardized single-point attenuation heatmaps provides a structurally unified and visually clear input foundation for subsequent multi-source data fusion.

[0060] Specifically, the process of determining the interference location area by superimposing a fused heatmap obtained by superimposing several single-point attenuation heatmaps within a preset spatiotemporal window based on the superposition weight determined by the peak intensity and the track consistency includes: The fusion weight of each single-point attenuation heatmap is calculated based on the weighted contribution of the peak intensity and the track consistency, where R = α × (Z / Z0) + β × H, where R is the fusion weight, α is the preset interference weight coefficient, Z is the peak intensity, Z0 is the maximum value among all peak intensities, β is the preset consistency weight coefficient, and H is the track consistency. The weighted geometric center is calculated based on the fusion weights and the ground projection center point of the single-point attenuation heatmap, wherein,

[0061]

[0062] Where N is the longitude of the weighted geometric center, N k A is the longitude of the center point of a single-point decay heatmap, and A is the latitude of the weighted geometric center. k It is the latitude of the center point of a single-point decay heatmap; Construct a comprehensive square region with the weighted geometric center as the ground projection center point and the preset length threshold as the side length, and divide the comprehensive square region into several comprehensive grid units based on the preset grid side length to establish a comprehensive attenuation grid, and take the average of the theoretical attenuation values ​​of the grid intersections of the several single-point attenuation heatmaps as the comprehensive attenuation value of each of the comprehensive grid intersections in the comprehensive attenuation grid; Using the minimum value among the comprehensive attenuation values ​​as a benchmark, the comprehensive attenuation values ​​are classified into several levels according to the preset attenuation gradient to obtain several comprehensive interference levels. Different comprehensive interference levels are distinguished by different colors to obtain the fusion heat map. When the overall interference level is greater than a preset level threshold, the area corresponding to the overall interference level in the fused heatmap is determined to be the interference location area.

[0063] The preset interference weight coefficient is a proportional coefficient used to adjust the contribution of the normalized peak intensity to the final fusion weight. It depends on the relative balance between the importance of the absolute strength of the observed signal and the reliability of the geometric relationship in the fusion positioning process, and is typically set between 0.2 and 0.8. In this embodiment, it is set to 0.5, ensuring that signal strength and geometric reliability have equal influence in the data fusion decision. The preset consistency weight coefficient is a proportional coefficient used to adjust the contribution of track consistency to the final fusion weight. Together with the preset interference weight coefficient, it determines the tendency of the fusion strategy. It is typically set between 0.2 and 0.8, and in this embodiment, it is set to 0.5, which, in conjunction with the preset interference weight coefficient, ensures the fusion strategy's bias. Similarly, a balanced fusion decision model that takes into account both signal strength and geometric reliability is constructed. The preset level threshold is used to filter out the attenuation range of the core area that represents the highest probability of the existence of the interference source from all color levels of the fusion heatmap. It depends on the strict requirements for the core confidence area of ​​the positioning result. It is usually set between the first level with the smallest attenuation, such as 0-5dB, and the two consecutive levels with the smallest attenuation, such as 0-10dB. In this embodiment, the threshold is set to the range corresponding to 0dB to 5dB according to the color level division, which can ensure that the interference positioning area of ​​the final output only includes the core geographical area with the smallest overall signal attenuation and the best geometric relationship with the aerial observation point, and therefore the highest probability of the existence of the interference source.

[0064] By constructing an adaptive weighted fusion mechanism based on data quality, the integration accuracy and reliability of multi-source heterogeneous positioning results are significantly improved. By dynamically calculating the fusion weights of each point's heatmap using peak intensity and track consistency, observations with high signal quality and high geometric reliability are ensured to dominate the fusion process, thereby suppressing the influence of low-quality or contaminated data. The reference origin of the unified fusion grid is dynamically established by weighted geometric centers, enabling the fusion process to adaptively align the most reliable regions of each observation, optimizing the targeting of the spatial search. Finally, by uniformly normalizing and discretizing the comprehensive attenuation values ​​of grid points, an intuitive fusion heatmap is generated, and interference positioning areas are automatically extracted using preset level thresholds. Overall, by simultaneously adopting a combination of multi-flight monitoring and multi-point directional approximation, the uncertainty of multiple batches of observations is transformed into a stable and convergent fusion heatmap, effectively overcoming the random errors of single-point positioning and achieving maximum likelihood estimation of the interference source location.

[0065] Please see Figure 4 As shown, this is the logic diagram for determining and adjusting the preset intensity threshold in this embodiment. In this embodiment, the process of adjusting the preset intensity threshold based on the threshold screening result of the divergence of the interference positioning area within a preset statistical period includes: Obtain and calculate the average longitude and average latitude of each grid intersection point in the interference location area to obtain several representative interference coordinates; Obtain and calculate the average values ​​of the longitude and latitude of the interference-representing coordinates respectively to obtain the geometric center; Calculate the distance between each of the interference representative coordinates and the geometric center to obtain several interference center distances, and calculate the standard deviation of all interference center distances to obtain the divergence. When the divergence is greater than the preset divergence threshold, the preset intensity threshold is increased according to the relative deviation between the divergence and the preset divergence threshold, where Y'=Y×[1+t×(S-S0) / S0], where Y' is the adjusted preset intensity threshold, Y is the original preset intensity threshold, t is the preset density ratio adjustment coefficient, S is the divergence, and S0 is the preset divergence threshold.

[0066] The preset divergence threshold is the maximum permissible dispersion threshold used to determine whether the spatial distribution of the interference positioning area generated based on multi-source data fusion is too dispersed. It depends on the minimum requirement for spatial consistency of positioning results, single-point positioning accuracy, and the effective range of typical interference sources. It is usually set between 3 km and 10 km. In this embodiment, it is set to 5 km, which can effectively distinguish the situation where the positioning results are excessively divergent and suspected of being due to excessive noise introduced by insufficient screening of previous data from the normal situation where the positioning results converge well. The preset density adjustment coefficient is a proportional coefficient used to control the adjustment range and sensitivity of the preset intensity threshold when the divergence of the positioning results exceeds the standard. It depends on the system's trade-off between the response speed and stability of adaptive adjustment. It is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.2, which can gradually tighten the data admission criteria and guide the entire process to converge to a working point that can produce stable positioning results.

[0067] By quantifying the divergence of historical positioning results, the rationality of the current preset intensity threshold can be objectively assessed. Excessive divergence indicates a low threshold, incorporating too much noise or unreliable signals. Based on this assessment, a nonlinear adjustment of the relative deviation is introduced, making the adjustment magnitude proportional to the severity of the problem. This achieves sensitive and robust negative feedback control, dynamically adapting to constantly changing electromagnetic environments. Without manual intervention, it automatically seeks the optimal balance between interference detection sensitivity and positioning result convergence, fundamentally improving the long-term reliability and environmental robustness of the entire positioning method.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for locating ground-based GPS interference sources based on airborne data, characterized in that, include: Estimate the signal strength of the GPS interference source at the antenna based on the GPS signal status obtained from the airborne data; Potential interference periods are identified based on the signal strength and a preset strength threshold, and the peak strength within the potential interference period is recorded. Obtain the GPS signal loss rate during the potential interference period; Based on the signal strength and the signal loss rate, it is determined whether the change in the interference signal during the potential interference period is reliable, and the determination result is used to determine whether the potential interference period is a period of stable interference. Obtain the latitude and longitude coordinates and flight speed of the aircraft during the period when the interference is stable; The target's critical moment is determined based on the track consistency, the peak intensity, and the flight speed, wherein the track consistency is determined based on the latitude and longitude coordinates and the signal strength; Obtain the aircraft's three-dimensional flight coordinates, flight attitude angles, and onboard GPS antenna pattern at key moments; Based on the preset grid side length, an attenuation grid is established with the flight three-dimensional coordinates as the center, and the theoretical attenuation value of the grid intersection point is determined by combining the correspondence between the flight attitude angle and the antenna pattern. A single-point decay heatmap is generated based on the theoretical decay value. The interference location area is determined by superimposing several single-point attenuation heatmaps within a preset spatiotemporal window using a superposition weight determined based on the peak intensity and the track consistency. The preset intensity threshold is adjusted based on the threshold screening results of the divergence of the interference positioning area within the preset statistical period.

2. The ground-based GPS interference source localization method based on airborne data according to claim 1, characterized in that, The process of identifying potential interference periods based on the signal strength and a preset strength threshold, and recording the peak intensity within the potential interference periods, includes: The interference start time and interference termination time are determined based on the comparison between the signal strength and the preset strength threshold. The time window between the start time of the interference and the end time of the interference is recorded as the potential interference period. The maximum value of the signal strength during the potential interference period is determined as the peak strength.

3. The ground-based GPS interference source localization method based on airborne data according to claim 2, characterized in that, The process of determining whether the interference signal change during the potential interference period is reliable based on the signal strength and the signal loss rate, and determining whether the potential interference period is a stable interference period based on the determination result, includes: The trend stability of the interference signal is determined based on the fluctuation trend and variation concentration characteristics of the signal strength during the potential interference period, so as to obtain the interference variation stability. Calculate the average signal loss rate during the potential interference period to obtain the average loss rate; The correlation coefficient is determined based on the variation characteristics of the signal strength and the signal loss rate during the potential interference period; Based on the threshold screening results of the interference change stability, the mean of the lockout, and the correlation coefficient of the change, the signal change during the potential interference period is determined to be reliable, and the potential interference period is determined to be an interference stable period.

4. The ground-based GPS interference source localization method based on airborne data according to claim 3, characterized in that, The process of determining the trend stability of the interference signal based on the fluctuation trend and variation concentration characteristics of the signal strength during the potential interference period, in order to obtain the interference variation stability, includes: The potential interference period is divided into several interference windows based on a preset window length, and the slope of several intensity fitting lines determined based on the temporal relationship of the signal strength within each interference window is recorded as the local trend quantization value of the signal strength within that interference window, so as to obtain several local trend quantization values. The monotonicity index is obtained by calculating the proportion of the sum of the durations during which the local trend quantification value is continuously positive or continuously negative to the total duration of the potential interference period. The determination coefficient of linear fitting is calculated based on the univariate linear regression fitting line corresponding to the signal strength at each time point within the potential interference period, so as to obtain the fitting determination coefficient. The geometric mean of the fitting determination coefficient and the monotonicity index is calculated to obtain the stability of the disturbance change.

5. The ground-based GPS interference source localization method based on airborne data according to claim 4, characterized in that, The process of determining the correlation coefficient based on the variation characteristics of the signal strength and the signal loss rate during the potential interference period includes: Calculate the relative deviation of the signal strength between any two adjacent moments within the potential interference period to obtain several stable strength deviations; Calculate the relative deviation of the signal loss rate between any two adjacent moments within the potential interference period to obtain several loss deviations; Calculate the Pearson correlation coefficient between the stability strength deviation and the unlocking deviation to obtain the change correlation coefficient.

6. The ground-based GPS interference source localization method based on airborne data according to claim 5, characterized in that, The target's critical moment is determined based on track consistency, peak intensity, and flight speed. The process of determining track consistency based on the latitude and longitude coordinates and the signal strength includes: Calculate the relative distance between the latitude and longitude coordinates of two adjacent moments within the period of disturbance stability to obtain the distance between several adjacent locations; Calculate the relative deviation of the distance between the adjacent positions to obtain several adjacent distance deviations, and calculate the relative deviation of the signal strength between two adjacent moments within the interference stabilization period to obtain several target interference deviations; Calculate the Pearson correlation coefficient between the adjacent distance deviation and the target interference deviation to obtain the track consistency. The time corresponding to the peak intensity is recorded as the peak time, and when the track consistency is greater than or equal to a preset consistency threshold, and the flight speed corresponding to the peak time is less than a preset speed threshold, the peak time is determined as the target critical time.

7. The ground GPS interference source localization method based on airborne data according to claim 6, characterized in that, The process of establishing an attenuation grid centered on the flight three-dimensional coordinates based on a preset grid side length, and determining the theoretical attenuation value at the grid intersection point by combining the correspondence between the flight attitude angle and the antenna pattern, includes: Construct a square region with the flight three-dimensional coordinates as the center point of the ground projection and a preset length threshold as the side length, and divide the square region into several grid units based on the preset grid side length; Obtain the three-dimensional coordinates of the grid intersection points of each grid cell to obtain several intersection point coordinates; Calculate the straight-line distance between the coordinates of each intersection point and the three-dimensional coordinates of the flight to obtain several attenuation distances, and calculate the spatial attenuation value based on the attenuation distances; Based on the correspondence between the flight attitude angle change, the aircraft's three-dimensional coordinate change, and the antenna pattern, the directional pattern gain of the airborne GPS antenna corresponding to the grid point is calculated through coordinate transformation. The spatial attenuation value and the directional pattern gain are summed to obtain the theoretical attenuation value at each grid intersection.

8. The ground GPS interference source localization method based on airborne data according to claim 7, characterized in that, The process of generating a single-point decay heatmap based on the theoretical decay value includes: Using the minimum value among the theoretical attenuation values ​​as a benchmark, the theoretical attenuation values ​​are classified into several levels according to a preset attenuation gradient to obtain several interference levels. Different interference levels are distinguished by different colors to obtain the single-point attenuation heatmap.

9. The ground GPS interference source localization method based on airborne data according to claim 8, characterized in that, The process of determining the interference location area based on the fused heat map obtained by superimposing several single-point attenuation heat maps within a preset spatiotemporal window according to the superposition weight determined based on the peak intensity and the track consistency includes: The fusion weight of each single-point attenuation heatmap is calculated based on the weighted contribution of the peak intensity and the track consistency. The weighted geometric center is calculated based on the fusion weight and the ground projection center point of the single-point attenuation heatmap; Construct a comprehensive square region with the weighted geometric center as the ground projection center point and the preset length threshold as the side length, and divide the comprehensive square region into several comprehensive grid units based on the preset grid side length to establish a comprehensive attenuation grid, and take the average of the theoretical attenuation values ​​of the grid intersections of the several single-point attenuation heatmaps as the comprehensive attenuation value of each of the comprehensive grid intersections in the comprehensive attenuation grid; Using the minimum value among the comprehensive attenuation values ​​as a benchmark, the comprehensive attenuation values ​​are classified into several levels according to the preset attenuation gradient to obtain several comprehensive interference levels. Different comprehensive interference levels are distinguished by different colors to obtain the fusion heat map. When the overall interference level is greater than a preset level threshold, the area corresponding to the overall interference level in the fused heatmap is determined to be the interference location area.

10. The ground GPS interference source localization method based on airborne data according to claim 9, characterized in that, The process of adjusting the preset intensity threshold based on the threshold screening results of the divergence of the interference positioning area within the preset statistical period includes: Obtain and calculate the average longitude and average latitude of each grid intersection point in the interference location area to obtain several representative interference coordinates; Obtain and calculate the average values ​​of the longitude and latitude of the interference-representing coordinates respectively to obtain the geometric center; Calculate the distance between each of the interference representative coordinates and the geometric center to obtain several interference center distances, and calculate the standard deviation of all interference center distances to obtain the divergence. When the divergence is greater than a preset divergence threshold, the preset intensity threshold is adjusted according to the relative deviation between the divergence and the preset divergence threshold.

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