A GBAS integrity risk modeling method considering ionospheric scintillation

Abstract

This invention provides a GBAS integrity risk modeling method that takes into account the impact of ionospheric scintillation, belonging to the field of navigation technology. The method includes: constructing a receiver tracking loop error model based on real-time scintillation parameters; decoupling the calculation of the standard deviation of the code tracking loop error and the standard deviation of the carrier tracking loop error under scintillation conditions; dynamically and adaptively adjusting the first monitoring threshold of the SQM and the second monitoring threshold of the carrier smoothness consistency monitor; performing fault detection on the raw observation data and removing abnormal satellites; calculating the ionospheric scintillation error term; obtaining the corrected ground system error standard deviation based on the ionospheric scintillation error term, receiver internal noise, and multipath error, and broadcasting it through the data broadcasting unit. This invention ensures high integrity of GBAS in complex ionospheric environments while suppressing false alarms caused by ionospheric scintillation and improving system availability.

Description

Technical Field

[0001] This invention belongs to the field of navigation technology, and in particular relates to a GBAS integrity risk modeling method that takes into account the influence of ionospheric scintillation. Background Technology

[0002] Global Navigation Satellite System (GNSS) has become a key technology in the field of aviation navigation due to its advantages such as all-weather operation, high precision, and wide coverage. This is especially true during aircraft approach and landing, where extremely stringent requirements are placed on the accuracy, integrity, and availability of navigation. Category III precision approaches, in particular, have integrity risk requirements as high as 10. -7 The magnitude of this is difficult to directly meet with the current accuracy of GNSS single-point positioning. To address this need, Ground Based Augmentation System (GBAS) was developed. This system deploys ground reference stations near airports to receive GNSS signals, calculate differential correction values ​​and integrity information, and broadcasts this information to airborne users via VHF data links. This differential correction technology significantly improves the accuracy and integrity of navigation and positioning, giving it the potential to meet Category III precision approach performance requirements. Currently, the International Civil Aviation Organization (ICAO) has recognized GBAS as a key precision approach technology and incorporated it into the Global Air Navigation Plan (GANP), explicitly identifying it as one of the core technologies for future air navigation. Globally, the deployment of GBAS ground stations is accelerating, and its flight guidance service capabilities are gradually increasing with the iteration of GNSS satellites and the improvement of terminal signal processing capabilities. However, GBAS still faces many challenges in providing precision flight guidance services. In addition to heterogeneous error sources at the satellite, ground, and airborne ends, the complex atmospheric environment, especially ionospheric anomalies, poses a serious threat to the performance of GBAS.

[0003] The ionosphere is a crucial medium in the propagation path of GNSS signals, and changes in its electron density can lead to signal delay and distortion. Ionospheric monitoring of the Global Guard Satellite System (GBAS) has always been a core technical challenge in ensuring its integrity and availability. Current ionospheric monitoring within the GBAS framework primarily focuses on two types of anomalies: Total Electron Content (TEC) spatial gradient anomalies and plasma irregularity anomalies. For TEC spatial gradient anomalies, GBAS employs various monitoring methods, including the Code Carrier Diversion (CCD) algorithm based on a single-frequency signal, the dual-solution ionospheric gradient monitoring algorithm, and the Ionospheric Gradient Monitoring (IGM) algorithm based on dual-frequency signals. These algorithms detect changes in the ionospheric TEC gradient by detecting pseudorange and carrier phase differences in GNSS signals at different frequencies or with different receivers. In the GBAS integrity risk model, the risk of ionospheric anomalies mainly stems from the ionospheric gradient. Existing approaches include threat modeling of the ionospheric gradient, eliminating its impact through dual-frequency ionospheric-free combinations, and suppressing its impact by constraining the satellite constellation geometry.

[0004] However, these methods have significant limitations. The ionospheric gradient standard deviation currently used at the GAST D ground end is typically a fixed value determined in advance based on long-term observation data or specific ionospheric anomalies. While this conservative fixed threshold ensures safety, its slow variability makes it overly stringent in non-extreme situations, severely limiting the usability of the GBAS system. Although recent research has begun to explore dynamic threat models of the ionospheric gradient to reduce reliance on conservative assumptions, such as dynamically updating threat parameters through real-time monitoring networks, the update cycle of existing ionospheric gradient standard deviations often fails to keep up with such rapid changes when space weather events cause drastic shifts in the ionospheric gradient, potentially leading to integrity risks. More critically, the aforementioned gradient monitoring algorithms are primarily sensitive to large-scale spatial gradient changes in the ionosphere, and their ability to monitor small- to medium-scale, irregular fluctuations in ionospheric electron density, i.e., ionospheric scintillation, is severely inadequate.

[0005] Ionospheric scintillation refers to the rapid and random fluctuations in the amplitude and phase of a GNSS signal as it passes through small-scale irregularities in the ionosphere, caused by random changes in refractive index. These fluctuations can manifest as deep signal fading or even signal loss of lock. Currently, a complete monitoring methodology for ionospheric scintillation anomalies has not been established domestically or internationally. The existing GBAS framework does not explicitly consider the impact of ionospheric scintillation and lacks an ionospheric scintillation integrity risk model. When ionospheric scintillation occurs, it severely impacts the availability and integrity of the GBAS system. The signal strength fading and rapid phase jitter caused by scintillation increase the tracking loop noise of the GNSS receiver, leading to frequent alarm triggering by existing GBAS signal quality monitors (SQM) or code-carrier smoothing consistency monitors (CSCIT) due to their fixed monitoring thresholds. During periods of strong scintillation, frequent false alarms can result in prolonged satellite signal rejection, rendering the system unavailable and significantly reducing GBAS availability. Especially in low-latitude regions, due to high ionospheric activity and frequent ionospheric scintillation, the availability of GBAS falls far short of ICAO requirements. Furthermore, scintillation increases receiver tracking loop errors, leading to pseudorange and carrier observation errors that propagate into the positioning domain. However, existing GBAS system integrity models only consider the ionospheric gradient component for ionospheric errors, failing to clearly define and encapsulate errors caused by scintillation. This means that in scintillation scenarios, existing Protection Level (PL) calculations may not fully cover actual positioning errors, posing a potential integrity risk and jeopardizing flight safety.

[0006] The current GBAS (Guardian Airway System) framework has significant shortcomings in ionospheric monitoring, especially under the influence of ionospheric scintillation. The fixed monitoring thresholds of existing monitors lead to a substantial decrease in system availability, while the lack of an ionospheric scintillation error term in the integrity risk model poses a potential threat to flight safety. Therefore, overcoming these challenges to achieve accurate GBAS ionospheric anomaly monitoring in scintillation-prone areas and provide robust precision approach services has become a critical issue that urgently needs to be addressed. To address the aforementioned technical problems in the existing GBAS integrity monitoring framework, particularly the deficiency that existing ionospheric monitoring mechanisms only focus on TEC (Temperature Change Domain) gradient changes while ignoring the influence of ionospheric scintillation, this invention proposes a GBAS integrity monitoring and dynamic threshold optimization method that considers the influence of ionospheric scintillation. Summary of the Invention

[0007] The purpose of this invention is to provide a GBAS integrity risk modeling method that takes into account the impact of ionospheric scintillation, in order to solve the problems of frequent false alarms and sharp decline in system availability caused by the failure of the existing GBAS framework to consider ionospheric scintillation.

[0008] To achieve the above objectives, this invention provides a GBAS integrity risk modeling method that takes into account the impact of ionospheric scintillation. The method includes: acquiring raw observation data from a GBAS reference receiver and real-time scintillation parameters from an ionospheric scintillation monitoring receiver, including amplitude scintillation index, phase scintillation spectrum slope, phase spectrum intensity, and carrier-to-noise ratio; constructing a receiver tracking loop error model based on the real-time scintillation parameters, and decoupling the calculation of the code tracking loop error standard deviation and the carrier tracking loop error standard deviation under scintillation conditions; dynamically and adaptively adjusting the first monitoring threshold of the signal quality monitor (SQM) and the second monitoring threshold of the carrier smoothing consistency monitor using the code tracking loop error standard deviation and the carrier tracking loop error standard deviation; performing fault detection on the raw observation data and removing abnormal satellites based on the adjusted first and second monitoring thresholds; obtaining the ionospheric scintillation error term based on the code tracking loop tracking error standard deviation and the carrier tracking loop tracking error standard deviation; and obtaining the corrected ground system error standard deviation based on the ionospheric scintillation error term, receiver internal noise, and multipath error, and broadcasting it through a data broadcasting unit.

[0009] The beneficial effects of this invention are as follows:

[0010] 1. By introducing real-time monitoring data such as amplitude scintillation index, phase spectrum parameters and carrier-to-noise ratio, the intensity of small-scale irregularities in the ionosphere is directly quantified, which enhances the perception dimension of the GBAS ground station to the complex ionospheric environment and breaks through the limitation of traditional methods that rely solely on TEC gradient and cannot effectively identify scintillation interference.

[0011] 2. By decoupling the specific effects of amplitude flicker and phase flicker on the code tracking loop and carrier tracking loop, the precise distinction and quantification of thermal noise, crystal oscillator noise and flicker noise in the receiver observations are realized, providing accurate theoretical input for subsequent monitoring threshold adjustment and error modeling;

[0012] 3. By using the real-time estimated code tracking loop tracking error to adaptively expand the detection threshold, the monitoring threshold is dynamically adjusted in real time according to the activity level of the ionosphere, which effectively solves the problem of frequent false alarms caused by signal amplitude fluctuations in traditional fixed threshold scenarios.

[0013] 4. By analyzing the transmission characteristics of flicker noise in the carrier smoothing filter and constructing a threshold correction term, the false alarm rate was significantly reduced while ensuring the sensitivity of fault detection such as step and ramp, thus avoiding the decrease in system availability caused by non-faulty environmental interference.

[0014] 5. By establishing an explicit transmission link from scintillation parameters to smooth pseudorange error, the standard deviation of scintillation error in the positioning domain was quantitatively calculated, the specific contribution of ionospheric scintillation to the final ranging accuracy was clarified, and the ambiguity in the treatment of environmental errors in the traditional model was eliminated.

[0015] 6. By adding an ionospheric scintillation error term to the calculation of the standard deviation of ground system error, the integrity model achieves comprehensive coverage of environmental factors, filling the gap in the existing GBAS framework that does not consider the risk of ionospheric scintillation integrity.

[0016] 7. By statistically analyzing historical scintillation data, the error boundary at a high confidence level was determined, thus achieving an effective envelope of the error distribution under extreme scintillation conditions and ensuring that the generated protection level can meet the stringent integrity requirements of precision approach.

[0017] 8. By using dynamic thresholds to reduce false alarm rates and error modeling to correct protection levels, the GBAS system has achieved robust operation in low-latitude ionospheric active regions, resolving the contradiction between availability and integrity in complex ionospheric environments. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0019] Figure 1 This is a flowchart of a GBAS integrity risk modeling method that takes into account the effects of ionospheric scintillation, provided by the present invention.

[0020] Figure 2 This is the overall processing flow of the GBAS ground system for ionospheric scintillation provided by the present invention;

[0021] Figure 3 This invention provides a receiver tracking loop error decoupling modeling process under ionospheric scintillation environment.

[0022] Figure 4 This is the flowchart of the ground station signal strength monitoring method based on dynamic threshold provided by the present invention;

[0023] Figure 5 This invention provides a threshold adaptive adjustment process for a carrier smoothing consistency monitor.

[0024] Figure 6 This invention provides a process for optimizing the integrity error of the GBAS ground system under scintillation conditions. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other. To achieve the above objectives, this invention adopts the following technical solution.

[0026] Figure 1 This is a flowchart of a GBAS integrity risk modeling method that takes into account the effects of ionospheric scintillation, as provided by the present invention. Figure 1 As shown, the method includes:

[0027] The raw observation data from the ground-based augmentation system's GBAS reference receiver and the real-time scintillation parameters from the ionospheric scintillation monitoring receiver were acquired. These real-time scintillation parameters included the amplitude scintillation index. Phase scintillation spectrum slope Phase spectral intensity and carrier-to-noise ratio ;

[0028] A receiver tracking loop error model is constructed based on real-time flicker parameters, and the standard deviation of code tracking loop error and carrier tracking loop error under flicker conditions are calculated in a decoupled manner.

[0029] The first monitoring threshold of the signal quality monitor (SQM) is determined using the standard deviation of the code tracking loop error and the standard deviation of the carrier tracking loop error. The second monitoring threshold of the carrier smoothness consistency monitor Perform dynamic adaptive adjustment;

[0030] Fault detection and removal of abnormal satellites are performed on the original observation data based on the adjusted first and second monitoring thresholds;

[0031] The ionospheric scintillation error term is obtained based on the standard deviation of the code tracking loop tracking error and the standard deviation of the carrier tracking loop tracking error;

[0032] The corrected standard deviation of the ground system error is obtained based on the ionospheric scintillation error term, receiver internal noise, and multipath error, and then broadcast through the data broadcasting unit.

[0033] The present invention will now be described in detail with reference to the accompanying drawings.

[0034] 1. GBAS Ground System Architecture for Ionospheric Scintillation:

[0035] This invention proposes a comprehensive processing architecture for a GBAS ground system oriented towards ionospheric scintillation. Based on the traditional GBAS ground subsystem, this system introduces real-time ionospheric scintillation sensing and a closed-loop error correction mechanism, forming an enhanced monitoring system that balances availability and integrity. The system first acquires physical input through a multi-source data access layer. The GBAS reference receiver provides conventional raw satellite navigation observation data, including pseudorange and carrier phase measurements. Simultaneously, ionospheric scintillation monitoring receivers deployed at ground stations output key parameters characterizing the current ionospheric environmental state in real time, including amplitude scintillation index, phase spectrum index, and carrier-to-noise ratio. These two data streams serve as the system's sensing foundation and are jointly input to the core processing layer, aiming to solve the problem that traditional systems relying on single observations cannot effectively distinguish between environmental interference and equipment failure.

[0036] In the core processing flow, the system first initiates the receiver tracking loop error modeling module. This module, acting as the hub connecting environmental perception and monitoring execution, decouples and calculates the thermal noise and jitter errors of the code tracking loop and carrier tracking loop under the current environment based on the input scintillation parameters, outputting precisely quantified standard deviations of the code tracking loop error and carrier tracking loop error. These real-time estimated loop error data are then distributed to the dynamic threshold generator and integrity parameter correction module. In the dynamic threshold generation stage, the system adaptively calculates the dynamic threshold values ​​required by the signal strength monitor (SQM) and carrier smoothness consistency monitor (CSCIT) based on the current loop error level. This ensures that the monitoring thresholds can be dynamically adjusted according to changes in ionospheric scintillation intensity, thereby effectively avoiding frequent false alarms caused by non-faulty scintillation fluctuations while maintaining fault detection sensitivity.

[0037] During the monitoring and output phase, the system rigorously verifies the reference receiver's observation data using generated dynamic thresholds. The signal strength monitor, combining the maximum dynamic stress parameter with the dynamic threshold, eliminates signal distortion anomalies; the carrier smoothing consistency monitor uses a corrected threshold to determine the smoothed pseudorange residuals, further filtering out unusable satellite data. Simultaneously, the integrity protection-level error envelope correction module, based on statistical analysis, calculates a specific integrity error term for ionospheric scintillation and quantitatively integrates it into the calculation of the ground system error standard deviation. Finally, the data broadcast unit transmits the fault-free satellite differential correction information, filtered by the monitors, along with integrity parameters including the scintillation correction term, to the airborne user via a VHF data broadcast link, thereby supporting highly safe and precise approach and landing of the aircraft in complex ionospheric environments.

[0038] Figure 2This invention provides the overall processing flow of the GBAS ground system for ionospheric scintillation, such as... Figure 2 As shown, the GBAS reference receiver observations include code pseudorange observations and carrier phase observations, while the ionospheric scintillation monitoring receiver output parameters include amplitude scintillation index. Phase scintillation power spectrum slope Phase scintillation power spectral intensity coefficient and carrier-to-noise ratio The GBAS reference receiver observations and the ionospheric scintillation monitoring receiver output parameters are input together into the receiver tracking loop error modeling module to obtain the standard deviation of the code tracking loop error. and carrier tracking loop error standard deviation The standard deviation of the code tracking loop error is used as the first monitoring threshold for the SQM (Signal Quality Monitor). The generation of the code tracking loop error standard deviation and the carrier tracking loop error standard deviation are used together as the second monitoring threshold of the carrier smoothness consistency monitor. The generation of data and the correction of the ground system error standard deviation envelope are performed. Historical ephemeris and station locations are used to calculate the maximum dynamic stress. The maximum dynamic stress, along with the dynamic signal strength monitoring thresholds (first monitoring threshold and second monitoring threshold), is input to the signal quality monitor. The carrier smoothness consistency test statistic, along with the dynamic signal strength monitoring threshold, is input to the carrier smoothness consistency monitor. The two monitors detect and remove abnormal satellites. The differential correction information and the corrected ground system error standard deviation parameters that pass the monitoring are broadcast by the data broadcasting unit.

[0039] A receiver tracking loop error model is constructed based on real-time scintillation parameters. The standard deviations of the code tracking loop tracking error and the carrier tracking loop tracking error are calculated decoupled under scintillation conditions. This includes: calculating the standard deviation of the code tracking loop error based on the amplitude scintillation index, carrier-to-noise ratio, code tracking loop noise bandwidth (i.e., the one-sided equivalent noise bandwidth of the code tracking loop), pre-detection coherence integration time, and correlation interval of the code tracking loop phase detector; calculating the thermal noise error variance based on the carrier tracking loop noise bandwidth, pre-detection coherence integration time, carrier-to-noise ratio, and amplitude scintillation index; calculating the phase scintillation error variance based on the phase spectrum intensity, loop order, loop natural frequency, and spectral slope; and calculating the standard deviation of the carrier tracking loop error based on the thermal noise error variance, phase scintillation error variance, and the inherent crystal oscillator noise variance of the receiver.

[0040] 2. Decoupling Modeling of Receiver Tracking Loop Error in Ionospheric Scintillation Environment:

[0041] In this invention, the receiver tracking loop error modeling module is the core algorithm unit connecting environmental sensing parameters and system monitoring thresholds. Given the significant differences in the impact mechanisms of ionospheric scintillation on different types of tracking loops, this invention employs a dual-path decoupling architecture, establishing refined error variance models for both the code tracking loop and the carrier tracking loop to ensure accurate estimation of the receiver's internal noise level under complex ionospheric environments.

[0042] Figure 3 This invention provides a receiver tracking loop error decoupling modeling process under ionospheric scintillation environment, such as... Figure 3 As shown, the receiver tracking loop error decoupling modeling includes a code tracking loop error modeling branch and a carrier tracking loop error modeling branch. In the code tracking loop error modeling branch, the amplitude flicker index is... Carrier-to-noise ratio Code tracking loop noise bandwidth Pre-inspection coherence integration time and the relevant interval of the code tracking loop phase detector Input the incoherent lead-lag power phase detector model, and then... After weighted thermal noise correction, the standard deviation of the code tracking loop error is obtained. In the carrier tracking loop error modeling branch, based on the phase scintillation power spectrum slope... Phase scintillation power spectral intensity coefficient Carrier tracking loop noise bandwidth and carrier-to-noise ratio Phase flicker noise, thermal noise, and crystal oscillator noise were calculated separately, and the standard deviation of the carrier tracking loop error was obtained by combining them using the root-sum-squares method. .

[0043] 2.1 Code tracking loop error modeling:

[0044] The code tracking loop typically employs a non-coherent early-minus-late (EL) phase detector, whose main source of interference is signal power fading caused by amplitude flicker. During implementation, the module first reads the amplitude flicker index S4 output by the ionospheric flicker monitoring receiver and the carrier-to-noise ratio measured by the reference receiver in real time. Since amplitude flicker manifests as rapid, random fluctuations in signal strength, it degrades the effective signal-to-noise ratio entering the phase detector. Traditional DLL (Delay Loop Lock) thermal noise variance models only consider the effects of Gaussian white noise and cannot reflect this signal fading effect. Therefore, this invention introduces a Conker correction model to adjust the amplitude flicker exponent. As a key parameter characterizing the signal fading depth, the effective signal power entering the correlator is processed. Specifically, the model assumes that the signal power attenuation is related to... Proportional to this, and in incoherent processing, the squared loss is exacerbated by flicker.

[0045] Based on the above mechanism, this invention constructs the standard deviation of the code tracking loop error under amplitude flicker modulation. The computational model comprehensively considers the code tracking loop noise bandwidth. Pre-inspection coherence integration time and the relevant interval of the code tracking loop phase detector (In chips). This is achieved by monitoring the amplitude flicker index in real time. and carrier-to-noise ratio Substituting into the thermal noise jitter formula, the following mathematical expression is derived:

[0046] (1)

[0047] Among them, the denominator term It intuitively reflects the signal-to-noise ratio loss caused by amplitude flicker; as the amplitude flicker exponent... As the value increases, the effective signal-to-noise ratio drops sharply, leading to a significant increase in code tracking error; the second term within the parentheses in the numerator describes the squared loss effect introduced by the incoherent phase detector, which is also affected by... The modulation effect. Using this formula, the system can accurately quantify the code tracking loop measurement noise under different flicker intensities, providing a physical basis for the dynamic threshold generation of the subsequent signal strength monitor.

[0048] 2.2 Carrier tracking loop error modeling:

[0049] Unlike code tracking loops, carrier tracking loops are extremely sensitive to phase changes and are affected by both amplitude and phase flicker. To accurately describe the tracking performance of a PLL (Phase Loop Lock) in a flickering environment, this invention proposes a three-component decoupling modeling method to incorporate the total carrier tracking error... The error is decomposed into three independent physical components: thermal noise error, crystal oscillator noise error, and phase flicker error, which are calculated separately. First, the variance of the thermal noise error is... Although primarily caused by receiver thermal noise, it is also strongly modulated by amplitude flicker. When deep fading occurs, the instantaneous carrier-to-noise ratio may drop to near the loop unlock threshold. This invention uses the fading correction logic in the DLL, but combines it with the arctangent or dot-product cross-phase detector characteristics unique to the PLL to establish the following thermal noise error variance model:

[0050] (2)

[0051] in, This represents the carrier tracking loop noise bandwidth. This quantifies the phase jitter caused by signal strength reduction.

[0052] Phase flicker noise component Direct phase perturbations caused by ionospheric irregularity drift. Since a PLL is essentially a low-pass filter, it can only track low-frequency phase changes. However, due to the power-law spectrum characteristic of phase scintillation, its high-frequency components will penetrate the loop filter and become residual errors. This invention utilizes the phase spectrum intensity output from an ionospheric scintillation receiver. Slope of the spectrum Using phase screen theory, the phase flicker energy that cannot be filtered out by the loop is calculated. For a typical second- or third-order PLL, its phase flicker error variance can be expressed as:

[0053] (3)

[0054] In the formula The order of the loop is . This is the natural frequency of the loop. The formula indicates that the spectral slope... The smaller the value or the narrower the loop noise bandwidth, the greater the residual high-frequency phase noise. Finally, the above two factors are compared with the receiver's inherent crystal oscillator noise variance. (Determined by the Elan variance parameter) Variance synthesis is performed to obtain the final standard deviation of the total carrier tracking error:

[0055] (4)

[0056] Through this decoupled modeling, the system can not only identify errors caused by signal weakening, but also errors caused by severe signal phase jitter, thus providing a more targeted dynamic threshold correction term for the carrier smoothness consistency monitor.

[0057] 3. Design of a GBAS ground integrity monitoring framework for flicker-oriented systems:

[0058] Based on accurate modeling of receiver loop errors, this invention further constructs a ground integrity monitoring framework that takes into account the effects of ionospheric scintillation. The core of this framework lies in abandoning the monitoring logic of traditional GBAS, which relies solely on fixed thresholds, and instead adopting a dynamic threshold mechanism based on real-time environmental perception to resolve the contradiction between system availability and integrity in strong scintillation scenarios.

[0059] The first monitoring threshold of the signal quality monitor (SQM) The calculation process includes: calculating the flicker-free reference monitoring threshold based on the correlation interval of the code tracking loop phase detector, the distance between the satellite and the receiver, and the code tracking loop noise bandwidth; multiplying the flicker-free reference monitoring threshold by the expansion factor of the signal strength test statistic to obtain the first monitoring threshold of the signal quality monitor (SQM). The expansion factor of the signal strength test statistic is determined based on the standard deviation of the code tracking loop error under flicker-free conditions and the standard deviation of the code tracking loop error under the current flicker conditions.

[0060] The GBAS integrity risk modeling method provided by this invention, which takes into account the influence of ionospheric scintillation, further includes: using real-time calculated signal strength statistics. The first monitoring threshold of the signal quality monitor (SQM) Comparison; in real-time signal strength statistics Greater than the first monitoring threshold of the signal quality monitor (SQM) In such cases, an alarm will be issued.

[0061] 3.1 Signal strength monitor reconstruction:

[0062] The reconstruction of the signal strength monitor aims to distinguish between non-faulty flicker signal fluctuations and faulty signal distortions. In practice, the system first constructs a signal strength test statistic based on the observation data of the current epoch. This statistic reflects the effective signal-to-noise ratio quality of the currently received signal, and its calculation fully considers the amplitude flicker index. The signal power attenuation and the squared loss during the incoherent integration process. Based on the carrier-to-noise ratio output in real time from the reference receiver. and flicker monitoring receiver provided The test statistic defined in this invention As shown in the following formula:

[0063] (5)

[0064] in, It is the pre-detection coherence integration time used in the calculation of the signal strength test statistic.

[0065] This statistic essentially characterizes the equivalent noise power level modulated by flicker. To determine the test statistic... To determine if an anomaly is detected, the system needs to synchronously generate the first monitoring threshold for the Signal Quality Monitor (SQM). The calculation of the first monitoring threshold is no longer a static constant, but rather incorporates the satellite's geometry and the physical limitations of the receiver itself. The system uses historical ephemeris data and station coordinates to calculate the distance between the satellite and the receiver. And the distance between the satellite and the receiver Find the third derivative to determine the maximum dynamic stress. Based on the code tracking loop phase detector related interval Based on the principle that the signal strength fluctuation must be enveloped by the sum of three times the noise standard deviation and the maximum dynamic stress, the system derives the upper limit of the allowable signal strength fluctuation at this time, which is the flicker-free reference monitoring threshold. :

[0066] (6)

[0067] in, This is the bandwidth of the code tracking loop noise. Furthermore, it is determined based on the signal strength test statistic inflation factor. ,in The standard deviation of the code tracking loop error under flicker-free conditions is used. Then, the flicker-free baseline monitoring threshold is adjusted to obtain the first monitoring threshold of the signal quality monitor (SQM).

[0068] (7)

[0069] During the monitoring execution phase, the system will calculate real-time signal strength statistics. The first monitoring threshold of the signal quality monitor (SQM) Comparison: If If the signal quality deteriorates beyond the permissible range under flickering conditions, it is determined to be abnormal interference or equipment failure. The data is then marked as unusable and an alarm is issued. If the signal fluctuation is determined to be a normal physical phenomenon under ionospheric scintillation, it is retained. Using this method, the system can detect strong scintillation (…). Enlargement leads to When the threshold is raised, the threshold determination logic can adapt based on the physical model, avoiding mistaking flickering features for faults.

[0070] Figure 4 This is the flowchart of the ground station signal strength monitoring method based on dynamic threshold provided by the present invention, such as... Figure 4 As shown, the signal strength test statistic is first calculated based on the current historical data. Standard deviation of tracking loop error And set the baseline parameters. Calculate the maximum dynamic stress based on historical ephemeris and station location. The flicker-free baseline monitoring threshold was determined by combining the correlation interval of the code tracking loop phase detector and the noise bandwidth of the code tracking loop. Based on the relationship between the current code tracking loop error standard deviation and the code tracking loop error standard deviation under flicker-free conditions, calculate the test statistic inflation factor. Then, the first monitoring threshold is synthesized. Signal strength test statistic With the first monitoring threshold Compare; when When abnormal interference is detected, the corresponding satellite data is marked as unavailable, and an alarm is issued; when When the fluctuation is determined to be within the permissible range caused by ionospheric scintillation, the corresponding satellite data is retained.

[0071] The second monitoring threshold of the carrier smoothness consistency monitor The calculation includes: calculating the threshold correction term based on the standard deviation of the code tracking loop error, the standard deviation of the carrier tracking loop error, the smoothing coefficient, the coverage factor, and the wavelength; and calculating the second monitoring threshold of the carrier smoothing consistency monitor based on the threshold correction term and the original fixed threshold.

[0072] 3.2 Dynamic adjustment of the carrier smoothness consistency monitor threshold:

[0073] Carrier smoothing consistency monitors are primarily used to detect faults such as code carrier divergence. In ionospheric scintillation environments, noise in both the code tracking loop and carrier tracking loop increases significantly and propagates through the carrier smoothing filter into the detection statistics, causing issues with traditional fixed thresholds. Frequent failures. This invention proposes a threshold correction strategy, which first models the smoothing process of the carrier smoothing filter, and its smoothing pseudorange... The recursive formula is ,in This is the difference between the carrier phase observations of the preceding and following epochs. To quantify the residual amount of flicker noise in the filter, this invention utilizes the Z-transform to analyze the filter's error transfer function and constructs a threshold correction term. Threshold correction term The standard deviation of the code tracking loop error directly output from the previous module. and carrier tracking loop error standard deviation The decision is made using the following formula:

[0074] (8)

[0075] In the formula For smoothing coefficients, Coverage factor (set according to false alarm rate requirements). For wavelength. This correction term intuitively reflects the code tracking loop error as flicker intensifies. and carrier tracking loop error Enlargement, leading to A significant increase. In the final decision-making stage, the system compares this correction with the original fixed threshold under flicker-free conditions. Geometric synthesis is performed to generate the second monitoring threshold for the final carrier smoothness consistency monitor. :

[0076] (9)

[0077] Based on this, the system calculates the current pseudorange residual test statistic (i.e., the difference between the original code pseudorange and the carrier smoothing pseudorange) and compares it with the second monitoring threshold of the carrier smoothing consistency monitor. A comparison is then made. When the test statistic exceeds the second monitoring threshold of the carrier smoothness consistency monitor... If a step or ramp fault is detected, an alarm is output; otherwise, the test is considered successful. This dynamic adjustment mechanism ensures that the monitor maintains high sensitivity during periods of ionospheric quiescence (when...). During periods of active flickering, the monitoring threshold is automatically expanded, thereby maximizing the continuity and availability of the system while ensuring that integrity risks are controllable.

[0078] The GBAS integrity risk modeling method provided by the present invention, which takes into account the influence of ionospheric scintillation, further includes: calculating the absolute value of the difference between the original code pseudorange and the carrier smooth pseudorange to obtain the carrier smooth consistency test statistic; when the carrier smooth consistency test statistic is greater than the second monitoring threshold of the carrier smooth consistency monitor, the carrier smooth consistency monitor outputs an alarm.

[0079] Figure 5 This invention provides a carrier smoothness consistency monitor threshold adaptive adjustment process, such as... Figure 5 As shown, the original code pseudorange and carrier phase are first obtained, and the carrier smoothed pseudorange is obtained through carrier smoothing filtering. The absolute value of the difference between the original code pseudorange and the carrier smoothed pseudorange is then calculated to obtain the carrier smoothing consistency test statistic. On the other hand, the standard deviations of the code tracking loop error and the carrier tracking loop error are uniformly transformed to the range domain, and the threshold correction term is calculated based on the error transfer function of the carrier smoothing filter. ; Compare the threshold correction term with the original fixed threshold The second monitoring threshold of the carrier smoothness consistency monitor is obtained by synthesizing the square root of the sum of squares. When the carrier smoothness consistency test statistic When the carrier smoothness consistency monitor outputs an alarm; when At that time, the monitoring was deemed successful.

[0080] The calculation process for the ionospheric scintillation error term includes: calculating the standard deviation of the ionospheric scintillation fundamental error based on the standard deviation of the code tracking loop tracking error and the standard deviation of the carrier tracking loop tracking error; and then applying the standard deviation of the ionospheric scintillation fundamental error to the expansion coefficient. The ionospheric scintillation error term is obtained by multiplication.

[0081] The ionospheric scintillation error term is obtained based on the standard deviation of the code tracking loop tracking error and the standard deviation of the carrier tracking loop tracking error, including: converting the standard deviation of the code tracking loop error and the standard deviation of the carrier tracking loop error into the standard deviation of the range domain code tracking error and the standard deviation of the range domain carrier tracking error, respectively; and obtaining the ionospheric scintillation error term based on the standard deviation of the range domain code tracking loop tracking error and the standard deviation of the range domain carrier tracking loop tracking error.

[0082] The process of calculating the expansion coefficient includes: obtaining standardized error samples based on the standard deviation of the ionospheric scintillation error and the detrended ionospheric scintillation error samples; estimating the cumulative distribution function based on the standardized error samples; and determining the expansion coefficient based on the preset tail risk probability and the cumulative distribution function.

[0083] 4. Optimization of GBAS ground system integrity error under flickering environment:

[0084] After completing the bottom-layer loop error modeling and mid-layer monitoring threshold adjustment, this invention further introduces a dedicated ionospheric scintillation error term in the top-layer integrity parameter generation stage. To construct a full-link integrity protection mechanism, this process begins with in-depth statistical analysis of ionospheric scintillation error samples. The system first acquires historically accumulated or real-time monitored ionospheric scintillation error data, and then performs detrending processing using a high-pass filter to remove low-frequency geometric changes caused by satellite motion, extracting the random error components purely caused by ionospheric irregularities. Subsequently, the module performs probability density function statistical analysis on these error samples and calculates the mean. ,variance Maximum error Features such as skewness and kurtosis. If the error distribution is unknown, the probability density function can be estimated using kernel density estimation (KDE) or histogram methods. The formula for the KDE method is:

[0085] (10)

[0086] In the formula, To estimate the position of the probability density function The probability density obtained at the estimated location; This represents the total number of ionospheric scintillation error samples. Bandwidth estimation for kernel density; For kernel functions; This refers to the error sample number; The location to be estimated is the probability density function; For the first One detrended ionospheric scintillation error sample.

[0087] Because error distributions under strong flicker conditions often exhibit heavy-tailed characteristics (i.e., the probability of extreme errors occurring is higher than that of a standard normal distribution), directly using a simple Gaussian model may underestimate the integrity risk. Therefore, this invention employs a probability density function estimation method to calculate the inflation coefficient that can completely cover the tail of the actual error distribution at a specified integrity risk level. .

[0088] Based on the above statistical envelope results, the system combines the real-time loop tracking error output by the preceding module to determine the final standard deviation of the ionospheric scintillation error. At the physical level, this error term is mainly composed of the thermal noise error of the code tracking loop affected by flicker and the noise error of the carrier tracking loop. However, in the integrity model, to address model uncertainties, it needs to be multiplied by an expansion coefficient. Its calculation logic can be expressed as follows: First, the standard deviation of the ionospheric flicker fundamental error is calculated using the aforementioned loop error decoupling model. Then, the ionospheric scintillation error term is obtained by applying the expansion coefficient. This process ensures that even in the worst flickering environments, the error parameters broadcast by the system provide users with sufficient safety margins, preventing dangerous and misleading information from being generated due to excessively low protection level calculations.

[0089] Historical ionospheric scintillation error samples are obtained, and the error samples are detrended. The results are then calculated based on the baseline error standard deviation corresponding to each error sample. Constructing standardized error samples Where e represents the detrended ionospheric scintillation error sample; the cumulative distribution function is estimated based on the standardized error sample. And based on the preset tail risk probability Determine the expansion coefficient, i.e., the integrity envelope expansion coefficient. The standard deviation of ionospheric scintillation error was calculated based on the integrity envelope expansion coefficient. .

[0090] Finally, the system calculates the ionospheric scintillation error term. The existing GBAS ground system error model is incorporated, and the root sum squares (RSS) method is used for variance synthesis. In the traditional GBAS protocol, the standard deviation of the ground system error... Includes only receiver internal noise and multipath error This invention extends the model to construct a new formula for synthesizing ground errors:

[0091] (11)

[0092] This formula explicitly quantifies the independent contribution of ionospheric scintillation to the ranging accuracy of ground-based systems. Ultimately, the system will use the corrected... The parameters are encoded into the GBAS broadcast message and broadcast to aircraft within the approach area. Upon receiving the parameters, airborne users will automatically factor in the additional error risk caused by scintillation when calculating the protection level. This allows them to increase the protection level radius during periods of ionospheric activity to ensure the safety of the approach process, thus achieving a closed-loop integrity system from ground monitoring to airborne applications.

[0093] Figure 6 This invention provides a process for optimizing the integrity error of the GBAS ground system under scintillation conditions, such as... Figure 6 As shown, historical or real-time ionospheric scintillation error samples are first acquired. These error samples are then detrended and subjected to probability density function statistical analysis. Finally, the integrity envelope expansion coefficient is calculated based on the specified tail risk probability. Simultaneously, the standard deviations of the code tracking loop error and the carrier tracking loop error are transformed to the range domain, and the fundamental standard deviation of the ionospheric scintillation error is calculated based on the error propagation characteristics of the carrier smoothing filter. Multiplying the standard deviation of the basic ionospheric scintillation error by the integrity envelope expansion coefficient yields the ionospheric scintillation error term. Finally, the standard deviation of ionospheric scintillation error is combined with the existing error terms such as receiver noise error standard deviation and ground multipath error standard deviation using RSS variance analysis to obtain the corrected standard deviation of GBAS ground system error. .

[0094] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention.

[0095] The objective of this invention is mainly achieved through the following technical solutions:

[0096] 1. By deploying an ionospheric scintillation monitoring receiver at the GBAS ground reference station, parameters such as scintillation index and phase spectrum slope index are monitored and output in real time. At the same time, the carrier-to-noise ratio measured by the receiver is used as an independent input source for subsequent tracking loop error estimation and monitoring threshold adjustment.

[0097] 2. For the Delay Loop Lock (DLL), the standard deviation of the tracking error of the code tracking loop is constructed based on the amplitude flicker index and the carrier-to-noise ratio; for the Phase Loop Lock (PLL), the effects of amplitude flicker and phase flicker are decoupled at the same time, and the standard deviation of the carrier tracking error, which includes thermal noise, crystal noise and flicker noise, is constructed by combining the phase noise power spectral density.

[0098] 3. By expanding the standard deviation of the tracking error of the code tracking loop and combining it with the receiver's maximum dynamic stress term, a test statistic for the SQM dynamic signal strength monitor is established, and a monitoring threshold is defined. This ensures fault detection capability while avoiding false alarms triggered by signal fluctuations caused by flicker.

[0099] 4. The carrier smoothing pseudorange process in the carrier smoothing consistency monitor is modeled to analyze the propagation characteristics of flicker noise in the smoothing filter. Based on this, a threshold correction term is constructed and superimposed on the original threshold of the carrier smoothing consistency monitor to generate a dynamic monitoring threshold.

[0100] 5. By combining the receiver code tracking loop error and the carrier tracking loop error caused by scintillation, a carrier smoothing pseudorange scintillation error propagation model with multi-source error decoupling is established to quantify the impact of ionospheric scintillation on the positioning domain.

[0101] 6. By statistically analyzing the probability density function of historical ionospheric scintillation error data, the error boundary at a high confidence level is quantified, and the expansion coefficient of scintillation error is determined accordingly to ensure that the model fully encapsulates the tail risk under extreme scintillation conditions.

[0102] 7. By quantitatively introducing the envelope-processed ionospheric scintillation error term into the GBAS integrity monitoring framework, it is combined with existing error terms such as multipath error and receiver noise to form a new logarithmic integrity risk model, thus correcting the standard deviation of the ground system error. The calculation formula.

[0103] Specifically, addressing the problem that existing ground subsystems using fixed monitoring thresholds cannot distinguish between environmental noise and system faults when ionospheric scintillation causes signal amplitude fading or phase jitter, leading to frequent false alarms and incorrect removal of satellite channels, this invention constructs a threshold adaptive adjustment mechanism based on real-time scintillation index. This mechanism dynamically relaxes the detection threshold for channels affected by scintillation while ensuring fault detection capabilities, significantly suppressing non-fault-related false alarms. Simultaneously, it quantitatively models the ranging error introduced by ionospheric scintillation, introducing an ionospheric scintillation error term into the GBAS ground system error standard deviation calculation model. Through this dual mechanism of dynamic threshold monitoring and integrity risk modeling, high integrity of the GBAS system is achieved in ionospheric scintillation-active regions, effectively solving the problem of service continuity interruption in complex ionospheric environments using traditional fixed threshold strategies, and providing a solid technical guarantee for GBAS to support higher-level precision approaches.

[0104] This invention adaptively adjusts the monitoring threshold by sensing the scintillation intensity in real time. Based on this, it models the GNSS receiver loop tracking error caused by ionospheric scintillation, and then derives the carrier smoothing pseudorange error propagation process. It also quantitatively introduces the ionospheric scintillation error term to correct the standard deviation of the ground system error, thereby achieving technical assurance of high availability and high integrity of the GBAS system in the ionospheric active region.

[0105] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not describe the various possible combinations separately.

[0106] Furthermore, various different implementations of the present invention can be combined arbitrarily, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed in the present invention.

Claims

1. A GBAS integrity risk modeling method that takes into account the effects of ionospheric scintillation, characterized in that, The method includes: The raw observation data from the ground-based augmentation system GBAS reference receiver and the real-time scintillation parameters from the ionospheric scintillation monitoring receiver are acquired. These real-time scintillation parameters include amplitude scintillation index, phase scintillation spectrum slope, phase spectrum intensity, and carrier-to-noise ratio. Based on the real-time flicker parameters, a receiver tracking loop error model is constructed, and the standard deviation of code tracking loop error and carrier tracking loop error under flicker conditions are calculated in a decoupled manner. The first monitoring threshold of the signal quality monitor (SQM) and the second monitoring threshold of the carrier smoothness consistency monitor are dynamically and adaptively adjusted using the standard deviation of the code tracking loop error and the standard deviation of the carrier tracking loop error. Fault detection and removal of abnormal satellites are performed on the original observation data based on the adjusted first and second monitoring thresholds; The ionospheric scintillation error term is obtained based on the standard deviation of the code tracking loop tracking error and the standard deviation of the carrier tracking loop tracking error; The corrected standard deviation of the ground system error is obtained based on the ionospheric scintillation error term, receiver internal noise, and multipath error, and then broadcast through the data broadcasting unit.

2. The GBAS integrity risk modeling method that takes into account the influence of ionospheric scintillation according to claim 1, characterized in that, Based on the real-time flicker parameters, a receiver tracking loop error model is constructed, and the standard deviation of the code tracking loop tracking error and the standard deviation of the carrier tracking loop tracking error under flicker conditions are calculated in a decoupled manner, including: The standard deviation of the code tracking loop error is calculated based on the amplitude flicker index, carrier-to-noise ratio, code tracking loop noise bandwidth, pre-detection coherence integration time, and code tracking loop phase detector correlation interval. The thermal noise error variance is calculated based on the carrier tracking loop noise bandwidth, pre-detection coherence integration time, carrier-to-noise ratio, and amplitude flicker index. The phase scintillation error variance is calculated based on the phase spectral intensity, loop order, loop natural frequency, and spectral slope. The standard deviation of the carrier tracking loop error is calculated based on the variance of thermal noise error, the variance of phase flicker error, and the variance of the receiver's inherent crystal oscillator noise.

3. The GBAS integrity risk modeling method that takes into account the influence of ionospheric scintillation according to claim 2, characterized in that, The calculation process for the first monitoring threshold of the Signal Quality Monitor (SQM) includes: The flicker-free reference monitoring threshold is calculated based on the correlation interval of the phase detector in the code tracking loop, the distance between the satellite and the receiver, and the noise bandwidth of the code tracking loop. The first monitoring threshold of the signal quality monitor (SQM) is obtained by multiplying the flicker-free baseline monitoring threshold by the expansion factor of the signal strength test statistic.

4. The GBAS integrity risk modeling method that takes into account the influence of ionospheric scintillation according to claim 3, characterized in that, The expansion factor of the signal strength test statistic is determined based on the standard deviation of the code tracking loop error under flicker-free conditions and the standard deviation of the code tracking loop error under the current flicker conditions.

5. The GBAS integrity risk modeling method that takes into account the influence of ionospheric scintillation according to claim 4, characterized in that, The calculation of the second monitoring threshold for the carrier smoothness consistency monitor includes: The threshold correction term is calculated based on the standard deviation of code tracking loop error, standard deviation of carrier tracking loop error, smoothing coefficient, coverage factor, and wavelength. The second monitoring threshold of the carrier smoothness consistency monitor is calculated based on the threshold correction term and the original fixed threshold.

6. The GBAS integrity risk modeling method that takes into account the influence of ionospheric scintillation according to claim 5, characterized in that, The calculation process for the ionospheric scintillation error term includes: The standard deviation of the fundamental error of ionospheric scintillation is calculated based on the standard deviation of the tracking error of the code tracking loop and the standard deviation of the tracking error of the carrier tracking loop. The standard deviation of the ionospheric scintillation error and the expansion coefficient The ionospheric scintillation error term is obtained by multiplication.

7. The GBAS integrity risk modeling method that takes into account the influence of ionospheric scintillation according to claim 6, characterized in that, The ionospheric scintillation error term is obtained based on the standard deviation of the code tracking loop tracking error and the standard deviation of the carrier tracking loop tracking error, including: The standard deviation of the code tracking loop error and the standard deviation of the carrier tracking loop error are converted into the standard deviation of the code tracking error in the range domain and the standard deviation of the carrier tracking error in the range domain, respectively. The ionospheric scintillation error term is obtained based on the standard deviation of the tracking error of the range domain code tracking loop and the standard deviation of the tracking error of the range domain carrier tracking loop.

8. The GBAS integrity risk modeling method that takes into account the influence of ionospheric scintillation according to claim 6, characterized in that, The process of calculating the coefficient of thermal expansion includes: Standardized error samples are obtained based on the standard deviation of the ionospheric scintillation error and the detrended ionospheric scintillation error samples. Estimate the cumulative distribution function based on the standardized error samples; The inflation coefficient is determined based on the preset tail risk probability and cumulative distribution function.

9. The GBAS integrity risk modeling method that takes into account the influence of ionospheric scintillation according to claim 8, characterized in that, The method also includes: The real-time calculated signal strength statistics are compared with the first monitoring threshold of the signal quality monitor (SQM). An alarm is issued when the real-time signal strength statistic exceeds the first monitoring threshold of the signal quality monitor (SQM).

10. A GBAS integrity risk modeling method that takes into account the effects of ionospheric scintillation, as described in claim 9, is characterized in that... The method also includes: The absolute value of the difference between the original code pseudorange and the carrier smoothing pseudorange is used to obtain the carrier smoothing consistency test statistic. When the carrier smoothness consistency test statistic exceeds the second monitoring threshold of the carrier smoothness consistency monitor, the carrier smoothness consistency monitor outputs an alarm.

Images