GBAS integrity improvement method and system based on ionosphere characteristics

Abstract

The invention discloses a GBAS integrity improvement method and system based on ionosphere characteristics, and the method achieves the high-precision data monitoring of space-time alignment through the construction of a multi-source joint monitoring system of a GBAS ground station and an ionosphere monitoring station. Capturing an ionosphere anomaly propagation speed and a gradient amplitude from a space dimension by using a dual-channel gradient sensing model, and detecting carrier signal time-frequency anomaly from a time dimension by using an adaptive filter; a composite threat quantization factor is established, ionosphere error parameters are dynamically corrected through a four-dimensional decision space, a vertical gradient error standard deviation is subjected to square weighting adjustment by adopting a threat factor, and optimization fusion of a nominal value and a coupling effect value is realized by an undetected pseudo-range error limit through a weight coefficient; executing a protection-level compensation algorithm for the corrected parameters, and enhancing the enveloping capability of airborne positioning errors; a double-risk closed-loop control mechanism is introduced, the optimality of the weight coefficient is verified in a closed-loop mode, and closed-loop verification of double-risk joint optimization is achieved.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation enhancement technology, and in particular to a method and system for improving GBAS integrity based on ionospheric characteristics. Background Technology

[0002] Ground-Based Augmentation System (GBAS) is a key technology in the field of satellite navigation. By deploying reference stations near airports and broadcasting differential corrections and integrity parameters to approaching and landing aircraft, it can significantly improve the positioning accuracy, integrity, continuity, and availability of Global Navigation Satellite System (GNSS), providing crucial support for precision approaches. However, the normal operation of GBAS is highly dependent on the effective suppression of space environment interference, among which ionospheric anomalies are one of the most significant factors threatening system integrity.

[0003] The ionosphere, as the ionized region of Earth's upper atmosphere, experiences spatiotemporal variations in its total electron content (TEC), which can introduce additional delays to navigation signals penetrating it. When the ionosphere experiences anomalous disturbances, such as the equatorial plasma bubble or the ionospheric front, the electron density exhibits a steep spatial gradient, causing significant deviations in the differential pseudorange observed by the base station and the airborne receiver. If such deviations are not effectively detected and compensated for, they will directly lead to positioning errors exceeding the safety envelope, posing a serious integrity risk.

[0004] Existing GBAS ground stations are typically equipped with basic integrity monitors, mainly including modules for signal quality monitoring, measurement quality monitoring, and data quality monitoring, which can identify anomalous observations to a certain extent. However, these monitors are designed for routine fault scenarios, and their detection capabilities are inherently limited when it comes to space environment disturbances such as ionospheric anomalies. Specifically, existing monitors mainly rely on single-station observation data for gradient detection, and can only determine whether there are gradient anomalies exceeding preset thresholds. They cannot obtain multi-dimensional characteristic parameters such as the propagation speed, direction of motion, and spatial scale of anomalous structures, nor can they identify the combined effects of different anomaly types such as plasma bubbles and frontal motion. This "single-dimensional, single-threat" detection mode often leads to insufficient detection rates or excessively high false alarm rates when facing complex ionospheric disturbances.

[0005] In other words, existing GBAS base station integrity monitors only support ionospheric gradient anomaly detection and cannot directly estimate multi-dimensional characteristic parameters such as propagation velocity and scintillation intensity. Furthermore, they lack the ability to identify complex anomalies such as plasma bubbles and frontal motion. Although ionospheric monitoring stations have multi-type anomaly detection capabilities, their data is mainly used for environmental assessment before station deployment and is not coupled with real-time GBAS operation, forming "data silos."

[0006] On the other hand, ionospheric monitoring stations, as specialized space environment observation facilities, possess more comprehensive anomaly monitoring capabilities, typically including ionospheric gradient monitoring, scintillation index monitoring, and frontal velocity monitoring. These stations can acquire multidimensional characteristics of ionospheric disturbances, providing crucial data for space weather research and environmental assessment. However, in practical applications, ionospheric monitoring station data is primarily used for site selection assessments and long-term environmental analysis before the deployment of the GBAS system, and is not effectively coupled with the real-time operation of GBAS. This "data silo" phenomenon prevents valuable multidimensional monitoring information from supporting real-time integrity decisions for the system, thus limiting GBAS's ability to respond to sudden ionospheric anomalies during operation.

[0007] Among the multiple threats posed by ionospheric anomalies, plasma bubbles and frontal motion are two of the most representative and complex scenarios. Plasma bubbles manifest as large-scale, low-density void structures with dramatic fluctuations in electron density, which can cause rapid flickering in signal amplitude and phase, and in severe cases, even signal loss of lock. Frontal motion, on the other hand, is the propagation process of abruptly changing electron density boundaries, with gradient amplitudes reaching hundreds of millimeters per kilometer, and is the main source of differential pseudorange errors. Adding to the complexity, these two types of anomalies often do not occur in isolation. The front may be precisely the boundary of the plasma bubble, or the two may overlap spatially and couple temporally, forming a "composite anomaly" scenario. Under such a composite threat, a single anomaly detection model cannot accurately estimate its combined effect, easily leading to an underestimation of errors and consequently a hidden increase in integrity risks.

[0008] Furthermore, existing GBAS integrity parameter settings generally employ static models. For example, the standard deviation of vertical gradient error and the error limit for undetected ionospheric pseudorange (EIG) are typically set to fixed values ​​based on historical statistics or worst-case assumptions. This static parameter configuration method has a fundamental flaw when facing the time-varying characteristics of ionospheric anomalies: when the actual threat level is low, overly conservative parameter settings can lead to an artificially high Protection Level, increasing unnecessary alarms and compromising system continuity; while when the actual threat level is high, especially when there are combined anomalies of strong gradients (>250 mm / km) and high-speed propagation (>150 m / s), static parameters may underestimate the true error, resulting in a decrease in the Protection Level's ability to envelop the positioning error and creating potential integrity risks.

[0009] The core issue is that existing technologies have failed to establish a complete closed-loop control mechanism from "multi-dimensional anomaly perception" to "dynamic parameter adjustment" and then to "dual-risk balance." On the one hand, the system's perception capability is limited to a single dimension, failing to fully capture the complex characteristics of ionospheric anomalies; on the other hand, the system's decision-making mechanism uses a static model, unable to adaptively adjust based on real-time perceived dynamic threats; ultimately, the system's risk control lacks joint optimization of false alarm rate (continuous risk) and missed alarm rate (integrity risk), often falling into a dilemma of addressing one aspect at the expense of the other.

[0010] Therefore, there is an urgent need for a GBAS integrity improvement method and system that can integrate multi-source monitoring data, comprehensively perceive ionospheric anomaly characteristics, dynamically adjust integrity parameters, and achieve joint optimization of false alarm and missed alarm risks, in order to meet the dual requirements of high integrity and high continuity for air navigation in complex space environments. Summary of the Invention

[0011] Therefore, the purpose of this invention is to provide a GBAS integrity improvement method and system based on ionospheric characteristics. By constructing an integrated architecture of multi-source data fusion sensing, dynamic parameter adaptation, and dual-risk closed-loop control, it achieves accurate identification and quantification of ionospheric composite anomalies. On this basis, it dynamically corrects integrity parameters and protection levels, ultimately achieving joint optimization and dynamic balance between the risk of missed alarms (integrity) and the risk of false alarms (continuity). This significantly improves the service performance and security assurance capabilities of the GBAS system in complex spatial environments such as strong gradients and high-speed propagation.

[0012] To achieve the above objectives, this invention provides a GBAS integrity enhancement method based on ionospheric characteristics, comprising the following steps: S1. Acquire multi-source monitoring data, which includes GBAS ground station integrity monitoring data and ionospheric monitoring station monitoring data. S2. Clean the multi-source monitoring data to form an effective monitoring dataset; S3. Input the effective surveillance dataset into the dual-channel gradient sensing model to extract the core feature parameters representing ionospheric anomalies from the spatial and temporal domains; output a high-dimensional anomaly feature vector containing the dual threat interaction factor DT. S4. When the dual-threat interaction factor DT exceeds a preset threshold, a 4D decision space is constructed, and the standard deviation of the vertical gradient error of the GBAS key integrity parameter is dynamically adjusted. Weighting coefficients for the undetected ionospheric pseudorange error limit (EIG) This enables adaptive mapping of integrity parameters under multiple operating conditions; S5. Dynamically correct integrity parameters, execute real-time compensation algorithm for protection level, accurately calculate integrity protection level under the influence of ionosphere; ensure its effective envelope of the actual positioning error; S6. Using the compensated protection level, monitor the false alarm rate and false alarm rate, and adjust the weighting coefficient of the adjusted undetected ionospheric pseudorange error limit EIG. Under this framework, a closed-loop verification of dual-risk joint optimization is achieved, verifying the balance between false alarm rate and missed alarm rate in complex ionospheric scenarios, and ultimately improving the integrity of GBAS under the risk control framework.

[0013] Furthermore, in S2, when cleaning multi-source monitoring data to form an effective monitoring dataset, a two-level cascaded filtering architecture is used for cleaning and screening. The primary filtering architecture is as follows: Multi-source monitoring data is filtered through physically effective thresholds. Real-time filtering, exceeding the physical effective threshold Monitored elements are removed and alerts are issued in real time; The two-level filtering architecture is as follows: The physical effective thresholds in the first-level filtering architecture are... A dynamically adjustable physical boundary determination mechanism is adopted. , A threshold is set based on the baseline; where, To dynamically determine the confidence level, the effective threshold is relaxed or tightened based on the increase or decrease of the confidence level, thereby accurately determining the credibility of each element in the multi-source monitoring data.

[0014] Furthermore, in S3, the core feature parameters for characterizing ionospheric anomalies extracted from the spatial and temporal domains include: Capturing the propagation speed of ionospheric anomalies in spatial dimensions With abnormal gradient magnitude ; Calculate the dual-threat interaction factor DT: in, The steep gradient of the ionospheric front. For the speed of transmission, This represents the gradient amplitude of the plasma bubble. The velocity of the plasma bubble; An adaptive filter is used to detect time-frequency anomalies in carrier signals from a time dimension; these time-frequency anomalies are used to detect rapid signal disturbances caused by ionospheric scintillation. The dual-threat interaction factor DT output from the spatial domain channel and the time-frequency anomaly features of the carrier signal output from the time domain channel together constitute a high-dimensional anomaly feature vector.

[0015] Furthermore, in S4, the 4D decision space is determined by the ionospheric anomaly propagation velocity. Ionospheric gradient magnitude Direction of dissemination Spatial related radius The four-dimensional decision space constitutes the structure. Dynamically adjust the standard deviation of vertical gradient error, a key integrity parameter of GBAS. This includes establishing a dynamic correction model according to the following formula. : in, for Weighting coefficients The standard deviation of the vertical gradient error is the key integrity parameter of GBAS. This is the corrected standard deviation of the error.

[0016] Furthermore, it also includes adopting A dynamic weighted algorithm is used to dynamically adjust the error limit EIG for undetected ionospheric pseudoranges. Will Embedded into the error bound of the undetected ionospheric gradient Calculation in progress: in, This is the nominal value. This represents the coupling effect value. To enhance the protection margin against complex threats, the coupling sensitivity coefficient is used. This is the orientation-spatial weighting function. These are the weighting coefficients; This is the dynamically adjusted EIG.

[0017] Furthermore, the weighting coefficients are established according to the following formula. Adaptive model: in, , Dynamic adjustment by fuzzy rules significantly enhances strong gradients. Error suppression capability in various scenarios Due to the abnormal propagation speed, This represents the ionospheric gradient magnitude. To determine the critical value of the gradient , velocity critical value for .

[0018] Furthermore, in S5, the integrity parameter is dynamically corrected, and a real-time compensation algorithm for the protection level is executed. This real-time compensation algorithm includes calculating the vertical protection level according to the following formula: in, It is a vertical protection level. The coefficient for no-fault missed detections; For the first The projection of the satellite ranging source error in the vertical direction. To be based on the corrected standard deviation of error The calculated standard deviation of the residual pseudorange measurement error. This is the protection level calculated after dynamic correction and compensation.

[0019] Furthermore, in S6, the weighting coefficients of the undetected ionospheric pseudorange error bound EIG are... The following closed-loop verification for joint optimization of dual risks is achieved, including: Weighting coefficients of the undetected ionospheric pseudorange error bound (EIG) Simultaneously correlate ionospheric characteristic parameters with risk indicators, where when When it increases, Reduction leads to increased risk of damage to integrity; when When decreasing, Increased risk leads to a higher level of continuity. Calculate the minimum value of the objective function using the following formula. in, The false alarm rate is used to characterize the risk of integrity failure. The false alarm rate is used to characterize continuous risk; pass By selecting reasonable values, the objective function is verified to reach its minimum value, ensuring that the deviation of missed alarm risk and the deviation of false alarm risk are minimized simultaneously, thus achieving closed-loop verification of dual-risk joint optimization.

[0020] The GBAS integrity enhancement method and system based on ionospheric characteristics disclosed in this application have at least the following advantages compared with the prior art: By innovatively integrating multi-source data from GBAS base stations and ionospheric monitoring stations, the problem of traditional GBAS systems relying solely on gradient detection from the base stations and having limited detection capabilities has been overcome. This new system can detect complex anomalies such as ionospheric frontal motion and plasma bubbles, thus reducing the false negative rate.

[0021] This application deploys ionospheric monitoring measures such as gradient, frontal velocity, and scintillation index, which are deeply coupled with GBAS base station signal quality, measurement quality, and data quality monitors. Real-time fusion analysis of multi-dimensional feature parameters is achieved in the data processing center, realizing dual protection of GBAS service continuity and integrity in complex spatial environments.

[0022] This application employs a dynamic confidence quantification model, combining historical accuracy and real-time reliability to construct an adaptive detection threshold, thereby improving the composite anomaly identification rate and reducing the false alarm rate.

[0023] This application constructs an adaptive weight adjustment model based on features such as anomalous ionospheric propagation velocity and gradient magnitude, dynamically corrects the vertical error standard deviation and pseudorange error boundary, achieves a balance between the false alarm rate and the false alarm rate, and reduces the integrity risk of the system. Attached Figure Description

[0024] Figure 1 This is an overall architecture diagram of the GBAS integrity enhancement system based on ionospheric characteristics of the present invention; Figure 2 A flowchart illustrating a GBAS integrity enhancement method based on ionospheric characteristics provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the multi-source monitoring data synchronization and cleaning process described in an embodiment of the present invention; Figure 4 This is a flowchart illustrating the entire process of gradient perception, decision correction, and risk closed-loop as described in an embodiment of the present invention. Detailed Implementation

[0025] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0026] like Figure 2 As shown, one embodiment of the present invention provides a GBAS integrity enhancement method based on ionospheric characteristics, which includes the following steps: S1. Acquire multi-source monitoring data, which includes GBAS ground station integrity monitoring data and ionospheric monitoring station monitoring data. Specifically, a multi-source monitoring system is constructed to achieve synchronous data acquisition and preprocessing. This includes: GBAS ground station integrity monitors for signal quality, measurement quality, and data quality; and ionospheric monitoring stations for ionospheric gradient, scintillation index, and frontal velocity. These monitors are synchronized to create a multi-source monitoring system with high spatiotemporal alignment. Precision clock synchronization technology is employed during the integration process to ensure data timestamp errors are <1ms.

[0027] S2. Clean the multi-source monitoring data to form an effective monitoring dataset; Preferably, a two-stage cascaded filtration architecture is used for cleaning and screening; The primary filtering architecture is as follows: Multi-source monitoring data is filtered through physically effective thresholds. Real-time filtering, exceeding the physical effective threshold Monitored elements are removed and alerts are issued in real time; The two-level filtering architecture is as follows: The physical effective thresholds in the first-level filtering architecture are... A dynamically adjustable physical boundary determination mechanism is adopted. , A threshold is set based on the baseline; where, To dynamically determine the confidence level; Based on the increase / decrease in confidence level, the effective threshold is relaxed / tightened to accurately determine the confidence level of each element in multi-source monitoring data.

[0028] S3. Input the effective surveillance dataset into the dual-channel gradient sensing model to extract core feature parameters representing ionospheric anomalies from the spatial and temporal domains; output a high-dimensional anomaly feature vector containing the dual threat interaction factor (DT); including: capturing the propagation speed of ionospheric anomalies from the spatial dimension. With abnormal gradient magnitude Capture carrier smoothing code time-frequency anomalies in the time domain using a variable time constant CSC filter; Specifically, constructing a dual-threat interaction factor ( This model quantifies the coupling and synergistic effect between the front and the plasma bubble. It can effectively identify complex anomaly scenarios and avoid the underestimation of errors caused by traditional single-threat models. The model is as follows: in, The steep gradient of the ionospheric front. For the speed of transmission, This represents the gradient amplitude of the plasma bubble. The velocity of the plasma bubble is represented by the model, which realizes the weighted sum of ionospheric anomalous momentum, reflecting the consistency of the propagation directions of the two threats: enhancement in the same direction and cancellation in the opposite direction.

[0029] S4. When the dual-threat interaction factor DT exceeds a preset threshold, a 4D decision space is constructed, and the standard deviation of the vertical gradient error of the GBAS key integrity parameter is dynamically adjusted. Weighting coefficients for the undetected ionospheric pseudorange error limit (EIG) This enables adaptive mapping of integrity parameters under multiple operating conditions; Specifically, when threat interaction factors When the value exceeds a certain threshold range, construction is required. Decision space (Ionospheric anomaly propagation speed) Ionospheric gradient magnitude , indicating the direction of abnormal propagation , (for spatially relevant radius), through integrated adjustment of integrity parameters Error boundary with undetected ionospheric pseudorange Weighting coefficients This enables dynamic mapping of integrity parameters under multiple operating conditions.

[0030] Dynamic correction: Standard deviation of vertical gradient error ( )according to Dynamic adjustments are made to achieve dynamic protection level correction, avoiding integrity risks caused by neglecting threat interactions in traditional methods, improving anomaly detection rate, and dynamically correcting the model. as follows, for The weighting coefficients can be determined based on covariance analysis.

[0031] Dynamic weighted algorithm: based on Decision space characteristics, dynamically adjusting the error limit of undetected ionospheric gradient ( To cover undetected differential pseudorange errors in complex threat scenarios, Embedded into the undetected ionospheric gradient error bound ( The calculation is as follows: in, This is the nominal value. This represents the coupling effect value. To enhance the protection margin against complex threats, the coupling sensitivity coefficient is used. This is the orientation-spatial weighting function. These are the weighting coefficients. This is the dynamically adjusted EIG.

[0032] Development The weighted adaptive model is as follows: in, , Dynamic adjustment by fuzzy rules significantly enhances strong gradients. Error suppression capability in various scenarios Abnormal propagation speed, The ionospheric gradient magnitude, the critical gradient value. for , velocity critical value for .

[0033] S5. Dynamically correct integrity parameters, execute real-time compensation algorithm for protection level, accurately calculate integrity protection level under the influence of ionosphere; ensure its effective envelope of the actual positioning error; Specifically, after dynamic correction and This method improves the accuracy of ionospheric error estimation for undetected anomalies. The adjusted protection level better encompasses the actual airborne positioning error, reducing integrity risks and increasing the detection rate of ionospheric anomalies. Protection level compensation method: In the formula: For the compensated integrity protection level, The coefficient for no-fault missed detections; For the first The projection of the satellite ranging source error in the vertical direction; dynamically corrected. Used to calculate the standard deviation of residual pseudorange measurement error .

[0034] S6. Using the compensated protection level, monitor the false alarm rate and false alarm rate, and the weighting coefficient of the undetected ionospheric pseudorange error limit EIG. Under this framework, a closed-loop verification of dual-risk joint optimization is achieved, verifying the balance between false alarm rate and missed alarm rate in complex ionospheric scenarios, and ultimately improving the integrity of GBAS under the risk control framework.

[0035] Specifically, Simultaneously correlate ionospheric characteristic parameters with risk indicators, where when When it increases, Reduce the risk of loss of integrity (false alarm rate) ) rise; when When decreasing, Increased risk leads to continuous risk (false alarm rate) (Increases.) By selecting a reasonable value, the objective function is verified to reach its minimum, ensuring that the deviations of missed alarm risk and false alarm risk are minimized simultaneously, thus achieving closed-loop verification of joint optimization for dual risks. Minimum value of the objective function: Closed-loop verification To achieve the optimal value, the risk of missed alarms should be minimized. and false alarm risk bias Reach the global minimum.

[0036] This invention constructs an integrated architecture for multi-source collaborative monitoring and dynamic risk control. It deeply integrates the multi-dimensional data streams from GBAS base station monitors (signal quality / measurement quality / data quality) and ionospheric monitoring stations (gradient / frontal velocity / scintillation index). By allocating monitoring weights through a real-time confidence quantification model, it overcomes the technical bottleneck of traditional single gradient detection being unable to capture complex anomalies, and improves the anomaly identification accuracy in complex scenarios such as concurrent fronts and plasma bubbles. At the same time, it implements a dual-risk adjustment mechanism driven by ionospheric features, adaptively correcting integrity parameters based on parameters such as real-time gradient amplitude and propagation velocity, establishing a dynamic balance between false alarm rate and false alarm rate, and significantly enhancing the dual effectiveness of continuity and integrity control of aviation services in complex space environments.

[0037] like Figure 1 As shown, another embodiment of the present invention provides a GBAS integrity enhancement system based on ionospheric characteristics, including a multi-source monitoring data synchronization module, a data cleaning and filtering module, a dual-channel gradient sensing module, a 4D decision space, a protection-level compensation module, and a risk closed-loop verification module, wherein... like Figure 3 The multi-source monitoring data synchronization module is used to jointly synchronize monitoring data from the GBAS ground station and the ionospheric monitoring station. The GBAS ground station integrity monitor includes signal quality monitoring, measurement quality monitoring, and data quality monitoring, while the ionospheric monitoring station includes ionospheric gradient monitoring, scintillation index monitoring, and frontal velocity monitoring. By synchronizing and combining these monitors, a multi-source monitoring system with high spatiotemporal alignment is constructed. Precision clock synchronization technology is used during the synchronization to ensure that the data timestamp error is <1ms.

[0038] The data cleaning and filtering module adopts a two-level cascaded filtering architecture. First, multi-source monitoring data is filtered through physical effective thresholds. Real-time filtering, exceeding The monitored elements are removed in real time and alerts are issued. A dynamically adjustable physical boundary determination mechanism is adopted. , First, set a threshold based on the baseline. Second, dynamically determine the confidence level. , Based on the increase / decrease in confidence level, the effective threshold is relaxed / tightened to accurately determine the confidence level of each element in multi-source monitoring data.

[0039] like Figure 4 The dual-channel gradient sensing module, based on an effective surveillance dataset, extracts core feature parameters characterizing ionospheric anomalies from both the spatial and temporal domains, and constructs a dual-threat interaction factor (…). This model quantifies the coupling and synergistic effect between the front and the plasma bubble. It can effectively identify complex anomaly scenarios and avoid the underestimation of errors caused by traditional single-threat models. The model is as follows: This model achieves a weighted sum of ionospheric anomalous momentum, reflecting the consistency of the propagation directions of the two threats: enhancement in the same direction and cancellation in the opposite direction. Among these, , The steep gradient and propagation velocity of the ionospheric front, , The gradient amplitude and velocity of the plasma bubble.

[0040] 4D decision space, when threat interaction factors When the value exceeds a certain threshold range, construction is required. Decision space (Ionospheric anomaly propagation speed) Ionospheric gradient magnitude , indicating the direction of abnormal propagation , (for spatially relevant radius), through integrated adjustment of integrity parameters Error boundary with undetected ionospheric pseudorange Weighting coefficients This enables dynamic mapping of integrity parameters under multiple operating conditions.

[0041] Dynamic correction: Standard deviation of vertical gradient error ( )according to Dynamic adjustments are made to achieve dynamic protection level correction, avoiding integrity risks caused by neglecting threat interactions in traditional methods, improving anomaly detection rate, and dynamically correcting the model. as follows, for The weighting coefficients can be determined based on covariance analysis.

[0042] Dynamic weighted algorithm: based on Decision space characteristics, dynamically adjusting the error limit of undetected ionospheric gradient ( To cover undetected differential pseudorange errors in complex threat scenarios, Embedded into the undetected ionospheric gradient error bound ( The calculation is as follows: in, This is the nominal value. This represents the coupling effect value. To enhance the protection margin against complex threats, the coupling sensitivity coefficient is used. This is the orientation-spatial weighting function. These are the weighting coefficients. This is the dynamically adjusted EIG.

[0043] Development The weighted adaptive model is as follows: in, , Dynamic adjustment by fuzzy rules significantly enhances strong gradients. Error suppression capability in various scenarios Abnormal propagation speed, The ionospheric gradient magnitude, the critical gradient value. for , velocity critical value for .

[0044] The protection-level compensation module is used to achieve adaptive mapping of integrity parameters under multiple operating conditions, correcting the integrity parameters used to calculate the protection level. Protection-level compensation method: In the formula: For the compensated integrity protection level, The coefficient for no-fault missed detections; For the first The projection of the satellite ranging source error in the vertical direction; dynamically corrected. Used to calculate the standard deviation of residual pseudorange measurement error .

[0045] The risk closed-loop verification module is used to achieve closed-loop verification of joint optimization of dual risks, verifying the balance between false alarm rate and missed alarm rate in complex ionospheric scenarios, and ultimately improving the integrity of GBAS within the risk control framework. When it increases, Reduce the risk of loss of integrity (false alarm rate) ) rise; when When decreasing, Increased risk leads to continuous risk (false alarm rate) (Increases.) By selecting a reasonable value, the objective function is verified to reach its minimum, ensuring that the deviations of missed alarm risk and false alarm risk are minimized simultaneously, thus achieving closed-loop verification of joint optimization for dual risks. Minimum value of the objective function: Closed-loop verification To achieve the optimal value, the risk of missed alarms should be minimized. and false alarm risk bias Reach the global minimum.

[0046] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A GBAS integrity enhancement method based on ionospheric characteristics, characterized in that, Includes the following steps: S1. Acquire multi-source monitoring data, which includes GBAS ground station integrity monitoring data and ionospheric monitoring station monitoring data. S2. Clean the multi-source monitoring data to form an effective monitoring dataset; S3. Input the effective monitoring dataset into the dual-channel gradient sensing model to extract the core feature parameters characterizing ionospheric anomalies from the spatial and temporal domains. Output a high-dimensional anomaly feature vector containing the dual-threat interaction factor (DT); S4, when the double threat interaction factor DT exceeds the preset threshold value, a 4D decision space is constructed, and the vertical gradient error standard deviation of the GBAS key integrity parameter is dynamically adjusted and the weight coefficient of the undetected ionosphere pseudo-range error limit EIG , realizing adaptive mapping of integrity parameters under multiple working conditions; S5. Dynamically correct integrity parameters, execute real-time compensation algorithm for protection level, accurately calculate integrity protection level under the influence of ionosphere, and ensure its effective envelope of the actual positioning error; S6, using the compensated protection level, monitoring the false alarm rate and the missed alarm rate, adjusting the weight coefficient of the ionospheric pseudorange error limit EIG which is not detected Next, the closed-loop verification of the dual-risk joint optimization is realized, the balance between the false alarm rate and the missed alarm rate in the complex ionospheric scene is verified, and finally the improvement of the GBAS integrity in the risk control framework is realized.

2. The ionospheric feature based GBAS integrity augmentation method of claim 1, wherein, In S2, when cleaning multi-source monitoring data to form an effective monitoring dataset, a two-level cascaded filtering architecture is used for cleaning and screening: The primary filtering architecture is as follows: Multi-source monitoring data is filtered through physically effective thresholds. Real-time filtering, exceeding the physical effective threshold Monitored elements are removed and alerts are issued in real time; The two-level filtering architecture is as follows: The physical effective thresholds in the first-level filtering architecture are... A dynamically adjustable physical boundary determination mechanism is adopted. ,in, Set a threshold based on the baseline; To dynamically determine the confidence level, the effective threshold is relaxed or tightened based on the increase or decrease of the confidence level, thereby accurately determining the credibility of each element in the multi-source monitoring data.

3. The GBAS integrity enhancement method based on ionospheric characteristics according to claim 1, characterized in that, In S3, the core feature parameters for characterizing ionospheric anomalies extracted from the spatial and temporal domains include: Capturing the propagation speed of ionospheric anomalies in spatial dimensions With abnormal gradient magnitude ; Calculate the dual-threat interaction factor DT: in, The steep gradient of the ionospheric front. For the speed of transmission, This represents the gradient amplitude of the plasma bubble. The velocity of the plasma bubble; An adaptive filter is used to detect time-frequency anomalies in carrier signals from a time dimension; these time-frequency anomalies are used to detect rapid signal disturbances caused by ionospheric scintillation. The dual-threat interaction factor DT output from the spatial domain channel and the time-frequency anomaly features of the carrier signal output from the time domain channel together constitute a high-dimensional anomaly feature vector.

4. The GBAS integrity enhancement method based on ionospheric characteristics according to claim 1, characterized in that, In S4, the 4D decision space is determined by the ionospheric anomaly propagation velocity. Ionospheric gradient magnitude Direction of dissemination Spatial related radius The four-dimensional decision space constitutes the structure. Dynamically adjust the standard deviation of the vertical gradient error, a key integrity parameter of GBAS. This includes establishing a dynamic correction model according to the following formula. : in, for Weighting coefficients The standard deviation of the vertical gradient error is the key integrity parameter of GBAS. This is the corrected standard deviation of the error.

5. The GBAS integrity enhancement method based on ionospheric characteristics according to claim 4, characterized in that, It also includes, using Dynamic weighting algorithm to dynamically adjust the EIG (extra-inclusive error limit) of undetected ionospheric pseudorange; Will Embedded into the error bound of the undetected ionospheric gradient Calculation in progress: in, This is the nominal value. This represents the coupling effect value. To enhance the protection margin against complex threats, the coupling sensitivity coefficient is used. This is the orientation-spatial weighting function. These are the weighting coefficients; This is the dynamically adjusted EIG.

6. The GBAS integrity enhancement method based on ionospheric characteristics according to claim 5, characterized in that, The weighting coefficients are established according to the following formula. Adaptive model: in, , Dynamic adjustment by fuzzy rules significantly enhances strong gradients. Error suppression capability in various scenarios Due to the abnormal propagation speed, This represents the ionospheric gradient magnitude. To determine the critical value of the gradient , velocity critical value for .

7. The GBAS integrity enhancement method based on ionospheric characteristics according to claim 1, characterized in that, In S5, the integrity parameter is dynamically corrected, and a real-time compensation algorithm for the protection level is executed. This real-time compensation algorithm includes calculating the vertical protection level according to the following formula: in, It is a vertical protection level. The coefficient for no-fault missed detections; For the first The projection of the satellite ranging source error in the vertical direction. To be based on the corrected standard deviation of error The calculated standard deviation of the residual pseudorange measurement error. This is the protection level calculated after dynamic correction and compensation.

8. The GBAS integrity enhancement method based on ionospheric characteristics according to claim 1, characterized in that, In S6, the weighting coefficients of the undetected ionospheric pseudorange error bound EIG. The following steps are used to achieve closed-loop verification of the dual-risk joint optimization, including: utilizing the weighting coefficients of the undetected ionospheric pseudorange error bounding box (EIG). Simultaneously correlate ionospheric characteristic parameters with risk indicators, where when When it increases, Reduction leads to increased risk of damage to integrity; when When decreasing, Increased risk leads to a higher level of continuity. Calculate the minimum value of the objective function using the following formula. in, The false alarm rate is used to characterize the risk of integrity failure. False alarm rate is used to characterize continuous risk.

9. A GBAS integrity enhancement system based on ionospheric characteristics, characterized in that, include: The system includes a multi-source monitoring data synchronization module, a data cleaning and filtering module, a dual-channel gradient sensing module, a 4D decision space, a protection-level compensation module, and a risk closed-loop verification module. The multi-source monitoring data synchronization module is used to jointly synchronize the GBAS ground station and the ionospheric monitoring station to obtain multi-source monitoring data. The data cleaning and filtering module uses a two-level cascaded filtering architecture to clean the monitoring data and form an effective monitoring dataset. The dual-channel gradient sensing module includes spatial and temporal channels. Based on an effective surveillance dataset, it extracts core feature parameters representing ionospheric anomalies from the spatial and temporal domains and outputs a high-dimensional anomaly feature vector containing the dual threat interaction factor DT. In a 4D decision space, when the dual-threat interaction factor DT exceeds a preset threshold, the standard deviation of the vertical gradient error of the GBAS key integrity parameter is dynamically adjusted. Weighting coefficients for the undetected ionospheric pseudorange error limit (EIG) This enables adaptive mapping of integrity parameters under multiple operating conditions; The protection level compensation module is used to realize adaptive mapping of integrity parameters under multiple operating conditions, correct the integrity parameters used to calculate the protection level, improve the accuracy of error estimation of undetected abnormal ionosphere, and reduce integrity risk. The risk closed-loop verification module is used to realize the closed-loop verification of dual-risk joint optimization, verify the balance between false alarm rate and false alarm rate in complex ionospheric scenarios, and ultimately improve the integrity of GBAS under the risk control framework.

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