Low-altitude navigation position credible early warning method and system

Abstract

The invention relates to the technical field of low-altitude navigation and flight safety, in particular to a low-altitude navigation position credible early warning method and system. Resolving to obtain a spatial reference position and a credible level of a corresponding position, and acquiring external standard time by integrating a satellite clock and a time service center; carrying out real-time modeling on risk or anomaly labeling to generate a dynamic quality field; generating a multi-dimensional credible factor; inputting the multi-dimensional credibility factors into a fusion decision engine based on a weight or a machine learning model, outputting a comprehensive credibility score, and triggering a graded early warning response according to the comprehensive credibility score and the over-limit condition of each independent credibility factor; and generating a structured early warning message according to the judged early warning response. A multi-dimensional cross validation and flight environment situation awareness model is constructed, comprehensive credibility evaluation and graded early warning are achieved, upgrading from passive receiving correction to active verification credibility is achieved, and therefore the safety and robustness of low-altitude flight are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of low-altitude navigation and flight safety technology, specifically to a reliable low-altitude navigation position early warning method and system. Background Technology

[0002] With the opening and commercialization of low-altitude airspace, applications such as drone logistics, manned eVTOL, and aerial inspection are increasing. As the core navigation and positioning method for low-altitude aircraft, GNSS signals are highly susceptible to electromagnetic interference, multipath effects, and malicious spoofing attacks in the complex electromagnetic and geographical environment of low altitudes. These threats can cause aircraft to acquire incorrect position, velocity, and time (PVT) information, leading to serious accidents such as collisions, yawing, or crashes. Existing technologies mainly have the following limitations: 1. Passive protection measures: Most solutions, such as receiver autonomous integrity monitoring (RAIM) or signal quality monitoring, only issue local alarms after an anomaly occurs, lacking the ability to actively verify based on external high-reliability location references.

[0003] 2. Limited information dimensions: The assessment relies heavily on the aircraft's own GNSS observations, failing to fully utilize networked infrastructure (such as ground-based augmentation networks) and group perception information provided by other aircraft, resulting in insufficient early detection capabilities for environmental risks.

[0004] 3. Lack of foresight in early warning: Traditional methods are unable to predict the signal environment risks ahead of the aircraft, and cannot support forward-looking path planning or risk avoidance.

[0005] Therefore, there is an urgent need for a comprehensive solution that can proactively, collaboratively, and intelligently assess the reliability of low-altitude navigation positions and provide tiered early warnings. Summary of the Invention

[0006] To address the problems existing in the prior art, the purpose of this invention is to provide a reliable early warning method for navigation positions of low-altitude aircraft such as eVTOL, logistics transportation, and engineering inspection vehicles. This method uses multi-source data fusion and collaborative verification technology to conduct real-time reliability assessment of the aircraft's Global Navigation Satellite System (GNSS) positioning results and time delay, and implements graded early warning when deception, interference, or performance degradation is detected.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is: a reliable early warning method for low-altitude navigation positions, comprising the following steps: Step 1: Acquire multi-source collaborative data, including target aircraft data, ground-based augmentation network data, and environmental perception data, through the air-to-ground security data link; Step 2: Based on the ground-based augmentation network CORS data and the target aircraft's raw GNSS observations, calculate the high-precision, high-reliability spatial reference position and the corresponding position's reliability level, and obtain the external standard time by integrating satellite clocks and time service centers; Step 3: Aggregate and analyze GNSS observation data from multiple sources, perform signal quality analysis, mark risks or anomalies, generate dynamic quality fields in real time, and update the interference source database; Step 4: Calculate multiple independent confidence factors through parallel channels, and combine them with the spatial reference positions calculated in Step 2 for cross-validation to generate multi-dimensional confidence factors; Step 5: Input the multi-dimensional credibility factors into the fusion decision engine based on weights or machine learning models, output a comprehensive credibility score, and trigger a graded early warning response based on the comprehensive credibility score and the exceeding of limits of each individual credibility factor; Step 6: Based on the determined early warning response, generate a structured early warning message and distribute it to the target aircraft, ground control station, and low-altitude traffic control center through the low-altitude communication network.

[0008] In the aforementioned reliable low-altitude navigation position warning method, in step 1, the target aircraft data includes: raw GNSS observations, real-time position Pu, velocity Vu, time Tu, flight attitude and state parameters calculated by the aircraft itself, and attitude angles, barometric altitude, and airspeed from the flight control system. The ground-based augmentation network data includes: real-time acquisition of reference station status, satellite health status, precise ephemeris, differential corrections, and integrity information provided by the ground-based augmentation network through a stable and reliable fiber optic private network communication link; The environmental perception data includes: pre-set flight mission plans, airspace structure information, temporary no-fly zones, the integrity status of broadcasts from other cooperating aircraft, and database information on known interference sources.

[0009] In the aforementioned low-altitude navigation position reliability early warning method, step 4 includes a reliability factor comprising a position error factor α_P, a time delay verification factor α_T, a dynamic consistency factor α_K, a signal quality and environment factor α_S, and a spatial domain and rule compliance factor α_F.

[0010] In the aforementioned low-altitude navigation position reliability early warning method, the position error factor α_P is obtained by calculating the aircraft's self-reported position Pu and the high reliability reference position Pref, and the time delay verification factor α_T is obtained by calculating the aircraft's self-reported time Tu and the standard time Tref. When the deviation exceeds the dynamic threshold set based on the protection level, the position error factor and the time delay factor respectively indicate an anomaly. The dynamic consistency factor α_K is calculated using the motion state derived from the high-confidence reference position Pref sequence or inertial measurement unit (IMU) data. It is then used to verify the dynamic consistency with the velocity Vu and attitude information reported by the aircraft, and the vector difference between the position-derived acceleration and the IMU-measured acceleration is calculated. The signal quality and environmental factor α_S are obtained by comprehensively evaluating the quality of the GNSS signal received by the aircraft and its conformity with the dynamic signal quality field. The airspace and rule compliance factor α_F determines whether the aircraft's self-calculated real-time position Pu or high-confidence reference position Pref deviates from the predetermined flight plan route, intrudes into restricted airspace, or conflicts with the surrounding traffic situation.

[0011] In the aforementioned reliable low-altitude navigation position warning method, step 5 includes the following warning response: Level 1: Under normal or monitoring status, the overall credibility score is higher than the safety threshold, there are no warnings, and continuous monitoring is maintained. Level 2, in a warning or downgraded state. At this time, the overall credibility score decreases or a single non-critical factor is abnormal, and an operation prompt is issued, suggesting that you pay attention to navigation performance. Level 3, in a warning or suspected state, at which point the overall credibility score Score drops significantly, or the error verification factor α_P exceeds the limit significantly but the dynamics are not abnormal for the time being, and a deception or serious interference warning is issued, prompting a switch to an alternative navigation source; Level 4 indicates an emergency or failure state. At this point, the overall credibility score is extremely low, and multiple key factors fail simultaneously, confirming a navigation system failure. The flight control system switches to backup navigation mode and triggers an emergency avoidance procedure.

[0012] A reliable low-altitude navigation position early warning system includes: Airborne intelligent terminal module: integrates GNSS receiver, status sensor and data communication unit at the aircraft end, used to collect, preprocess and upload raw GNSS data and status data, and receive and execute early warning commands; Ground-based augmentation network module: Through GNSS reference stations and monitoring stations evenly distributed within a certain area, it provides high-precision differential correction data (RTCM) data streams, as well as integrity information such as satellite health status, geometric factors, signal quality, and availability; Trusted Situation Awareness Processing Center Module: This is the core computing unit that executes the low-altitude navigation position trusted early warning method as described in any one of claims 1-5; Secure data communication network module: responsible for real-time and reliable data interaction between various modules; User interaction terminal module: includes aircraft cockpit display, operator remote control, and low-altitude traffic control platform, used to visually display aircraft position, reliability status and early warning information.

[0013] The aforementioned low-altitude navigation position reliable early warning system, wherein the reliable situational awareness processing center module includes: Data receiving and processing interface: Receives data streams and environmental sensing data from the airborne terminal and ground-based augmentation network in step 1; Spatiotemporal location calculation engine: used to execute step 2, calculate the high-confidence reference location Pref and the standard time Tref; Dynamic signal quality field modeling engine: used to perform step 3, integrate crowdsourced data, build and update the dynamic signal environment simulation field map of the service area in real time, and update the interference source database; Multidimensional verification engine: used to execute step 4, and to perform parallel calculations of multidimensional credibility factors including position error factor α_P, time delay verification factor α_T, dynamic consistency factor α_K, signal quality and environment factor α_S, and spatial domain and rule compliance factor α_F. Intelligent Fusion Decision Maker: Used to execute step 5, integrating a fusion decision engine to achieve comprehensive evaluation and early warning level determination; Early warning information interaction interface: used to execute step 6, providing standardized early warning information output to aircraft, operators and low-altitude traffic control centers.

[0014] The beneficial effects of the low-altitude navigation position reliability early warning method and system of the present invention are as follows: by integrating the aircraft's own navigation data, ground-based augmentation station network data and group observation data, a multi-dimensional cross-validation and flight environment situational awareness model is constructed, realizing the comprehensive reliability assessment and graded early warning of GNSS positioning results and time delay, realizing the upgrade from "passive reception correction" to "active verification reliability", thereby significantly improving the safety and robustness of low-altitude flight.

[0015] By introducing an external, highly reliable position reference and standard time independent of the aircraft's own GNSS receiver, and through real-time comparison, it can effectively identify single-point spoofing and interference attacks, thereby achieving proactive defense and enhancing proactive defense capabilities.

[0016] The multi-dimensional cross-validation mechanism, which considers location, time, dynamics, signal environment, and spatial rules, avoids the limitations of a single judgment criterion, significantly reduces false alarm and false negative rates, and demonstrates high robustness in evaluation.

[0017] By constructing a "dynamic signal quality field" for navigation satellites in real time using crowdsourced data, the system can not only assess the current state but also perceive and predict signal environment risks ahead of the route, supporting predictive decision-making and providing environmental situational awareness.

[0018] Implement tiered early warnings based on threat levels, provide differentiated response recommendations for users at different levels (pilots, operators, regulators), balance automated handling with manual intervention, optimize security decision-making processes, and make response mechanisms intelligent.

[0019] The architecture is compatible with existing ground-based augmentation facilities and various communication links, making it easy to integrate into the existing low-altitude monitoring system. It also provides interfaces for future access to more sensors (such as vision and lidar), resulting in good system compatibility and scalability. Attached Figure Description

[0020] Figure 1 This is a general flowchart of the method in the embodiments of the present invention; Figure 2 This is a schematic diagram of the overall architecture and data flow of the system in an embodiment of the present invention; Figure 3 This is a logical diagram illustrating the generation and fusion decision of multidimensional credibility factors in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the dynamic signal quality field modeling and early warning application in an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be described below in conjunction with specific embodiments and accompanying drawings.

[0022] Example 1 like Figure 1 As shown, a reliable early warning method for low-altitude navigation positions is proposed. The core of this method is to use a low-altitude navigation position reliability assessment method that combines highly reliable external reference position calculation with multi-dimensional cross-validation. The multi-dimensional aspects include at least position difference, time delay, dynamic consistency, signal environment compliance, and airspace rule compliance.

[0023] It includes the following steps.

[0024] Step 1 M10: Multi-source collaborative data acquisition.

[0025] The following data can be obtained in real time through the air-to-ground security data link: Target vehicle data: raw GNSS observations (pseudorange, carrier phase, Doppler shift, etc.), real-time position (Pu), velocity (Vu), time (Tu), flight attitude and state parameters calculated by the vehicle itself, attitude angles (pitch, roll, yaw), barometric altitude, airspeed, etc. from the flight control system.

[0026] Ground-based augmentation network CORS data: Real-time acquisition of reference station status, satellite health status, precise ephemeris, differential corrections, and integrity information provided by the ground-based augmentation network through a stable and reliable fiber optic private network communication link.

[0027] Environmental perception data: Acquire preset flight mission plans, airspace structure information (electronic fences, flight routes), temporary no-fly zones, integrity status of broadcasts from other cooperating aircraft, and database information on known interference sources.

[0028] Step 2 M20: High-reliability spatiotemporal position calculation. Based on ground-based augmentation network data and the target spacecraft's original observations, network RTK or precise point positioning (PPP) technology is used to calculate a high-precision, high-reliability spatial reference position (Pref) and the corresponding position's reliability level (Prms). External standard time (Tref) is obtained by integrating satellite clocks and time service centers.

[0029] Step 3 M30: Dynamic Signal Quality Field Modeling. This involves aggregating and analyzing GNSS observation data from multiple sources (multiple base stations, multiple aircraft), performing quality analysis (such as signal-to-noise ratio levels and multipath markers), labeling risks or anomalies, and generating a dynamic quality field in real time. This field describes the spatiotemporal distribution of signal interference, multipath risks, and potential deception or interference points within the service area, and updates the interference source database.

[0030] Part 4 M40: Generation of Multidimensional Credibility Factors.

[0031] Calculate multiple independent confidence factors through parallel channels: Position error and time delay verification factors (α_P, α_T): Calculate the position deviation α_P and time delay α_T between the aircraft's self-reported position (Pu) and time (Tu) and the high-confidence reference position (Pref) and standard time (Tref). When the deviation exceeds the dynamic threshold set based on the protection level, the position error and time delay factors indicate anomalies, and the confidence level decreases.

[0032] Dynamic consistency factor (α_K): The dynamic consistency of the motion state (velocity, acceleration) calculated using (Pref) sequences or inertial measurement unit (IMU) data with the aircraft's self-reported (Vu) and attitude information is verified. This includes calculating the vector difference between the position-derived acceleration and the IMU-measured acceleration.

[0033] Signal quality and environmental factor (α_S): A comprehensive evaluation of the quality of the GNSS signal received by the aircraft (such as signal-to-noise ratio, carrier noise, multipath error index, and rate of change of satellite geometric precision factor (DOP)) and its conformity with the dynamic signal quality field.

[0034] Airspace and rule compliance factor (α_F): Determines whether (Pu) or (Pref) deviates from the planned flight path, intrudes into restricted airspace, or conflicts with surrounding traffic conditions. Compliance indicates high confidence; deviation triggers an alarm.

[0035] Step 5 M50: Credibility assessment and graded early warning.

[0036] The aforementioned credibility factors (α_P, α_T, α_K, α_S, α_F) are input into a fusion decision engine based on a weighted or machine learning model, outputting a comprehensive credibility score (Score, ranging from 0 to 1). The system triggers tiered early warning responses based on the Score and the extent to which each factor exceeds its limits. Level 1 (Normal / Monitoring): Score is higher than the safety threshold, no warning, continuous monitoring.

[0037] Level 2 (Prompt / Degradation): Score decreases or a single non-critical factor is abnormal. An action prompt is issued, suggesting monitoring navigation performance.

[0038] Level 3 (Warning / Suspicion): The score has decreased significantly, or the error verification factor (α_P) has significantly exceeded the limit, but the dynamics are not abnormal at present. A deception or serious interference warning is issued, prompting a switch to an alternative navigation source.

[0039] Level 4 (Emergency / Failure): Extremely low score, and multiple critical factors fail simultaneously. If the navigation system is confirmed to be faulty, the system may suggest that the flight control system switch to a backup navigation mode (such as pure inertial or visual navigation) and trigger emergency avoidance procedures (such as automatically executing a preset emergency route, forced landing, or hovering).

[0040] Step 6 M60: Early warning information generation and distribution.

[0041] Based on the determined warning level, a structured warning message containing the aircraft ID, anomaly type, confidence level, and recommended actions is generated and distributed via the low-altitude communication network to: the target aircraft (for airborne system response), the ground control station (for manual monitoring and intervention), and the low-altitude traffic control center (for airspace collaborative management and conflict resolution).

[0042] In this embodiment, the system is innovative: it includes a cloud-based collaborative processing architecture that incorporates a dynamic signal quality field modeling engine and an intelligent fusion decision-maker, enabling the fusion of group data to achieve environmental situational awareness and intelligent early warning.

[0043] Early warning mechanism: A multi-level early warning response mechanism with clear operational guidelines, triggered by a comprehensive credibility score generated by a multi-factor fusion decision-making model.

[0044] Application scenarios: Specific applications of this method and system in anti-spoofing, anti-interference, and navigation integrity monitoring of low-altitude aircraft and aircraft swarms (especially UAVs and eVTOL).

[0045] Example 2 A reliable low-altitude navigation position early warning system that implements the above method includes the following features.

[0046] 1. Airborne Intelligent Terminal Module: Integrates GNSS receiver, status sensor, data communication unit (such as 4G / 5G, data radio) and other components on the aircraft end, used to collect, preprocess and upload raw GNSS data and status data, and to receive and execute early warning commands.

[0047] II. Ground-based augmentation network module: GNSS reference stations and monitoring stations evenly distributed within a certain area provide high-precision differential correction data (RTCM) data streams, as well as integrity information such as satellite health status, geometric factors, signal quality, and availability.

[0048] III. Trusted Situation Awareness Processing Center Module: This is the core computing unit and further includes: ① Data receiving and processing interface: Receives data streams and environmental perception data from the M10 airborne terminal, the ground-based augmentation network.

[0049] ② Spatiotemporal position calculation engine: Executes M20 to calculate the high-confidence reference position (Pref) and standard time (Tref).

[0050] ③ Dynamic signal quality field modeling engine: Execute M30, integrate crowdsourced data, build and update the dynamic signal environment simulation field map of the service area in real time, and update the interference source database.

[0051] ④ Multi-dimensional verification engine: Executes M40 to calculate multi-dimensional credibility factors such as position error, time delay, dynamics, signal environment and spatial rules in parallel.

[0052] ⑤ Intelligent Fusion Decision Maker: Executes M50, integrates a fusion decision engine, and realizes comprehensive evaluation and early warning level determination.

[0053] ⑥ Warning information interaction interface: Execute M60 to provide standardized warning information output to aircraft, operators and low-altitude traffic control centers.

[0054] IV. Secure Data Communication Network Module: Responsible for real-time and reliable data interaction between various units of the system.

[0055] V. User Interaction Terminal Module: This includes the aircraft cockpit display, operator remote control, low-altitude traffic control platform, etc., used to visually display the aircraft's position, reliability status, and early warning information.

[0056] Example 3 Multiple drones are conducting inspections in the urban traffic protection zone for a certain project.

[0057] I. Data aggregation.

[0058] All operational drones, through their onboard terminals, transmit raw GNSS observation data, calculated CGCS2000 coordinates, real-time airspeed, attitude angles, and the planned flight path to the Trusted Situational Awareness Processing Center in real time via a 5G network. Simultaneously, the center accesses data from the Urban BeiDou Ground-Based Augmentation Network (CORS) to obtain regional reference station status, differential correction information, and integrity parameters.

[0059] II. Collaborative Processing and Verification.

[0060] The center calculates a high-precision reference position (Pref) for each UAV.

[0061] Based on signal reports from all drones and CORS stations, the dynamic signal quality field modeling engine detected an abnormal L1 band multipath interference risk in a certain intersection area and updated the quality field.

[0062] For UAV 001 about to enter the area, multi-dimensional verification engine calculations revealed that its reported position (Pu) and (Pref) position errors, and reported time (Tu) and (Tref) time delays were still within the thresholds (α_P, α_T normal), and the dynamics were also consistent (α_K normal). However, signal quality and environmental factor (α_S) calculations showed that the currently observed signal quality was too "perfect," which was seriously inconsistent with the "moderate multipath risk" predicted by the quality field for this area, triggering an anomaly flag.

[0063] III. Decision-making and early warning.

[0064] After comprehensive evaluation, the intelligent fusion decision-maker determined that there were "high-quality signals inconsistent with the expected environment," which may be a targeted intelligent deception attack. Although the position had not yet shifted significantly, the system decisively issued a Level 3 (warning / suspect) alert to UAV No. 01, instructing it to immediately switch to visual-assisted navigation mode and notifying the ground operator to take over.

[0065] IV. Results.

[0066] Guided by the early warning, the operator confirmed via video feedback that the drone's actual position had slightly deviated, and intervened promptly to prevent a potential collision with a building. Simultaneously, the warning information was transmitted to the UTM system, providing risk alerts to other aircraft in the area.

[0067] The above embodiments are merely illustrative of the structural concept and features of the present invention, intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A reliable early warning method for low-altitude navigation positioning, characterized in that, Includes the following steps: Step 1: Acquire multi-source collaborative data, including target aircraft data, ground-based augmentation network data, and environmental perception data, through the air-to-ground security data link; Step 2: Based on the ground-based augmentation network CORS data and the target aircraft's raw GNSS observations, calculate the high-precision, high-reliability spatial reference position and the corresponding position's reliability level, and obtain the external standard time by integrating satellite clocks and time service centers; Step 3: Aggregate and analyze GNSS observation data from multiple sources, perform signal quality analysis, mark risks or anomalies, generate dynamic quality fields in real time, and update the interference source database; Step 4: Calculate multiple independent confidence factors through parallel channels, and combine them with the spatial reference positions calculated in Step 2 for cross-validation to generate multi-dimensional confidence factors; Step 5: Input the multi-dimensional credibility factors into the fusion decision engine based on weights or machine learning models, output a comprehensive credibility score, and trigger a graded early warning response based on the comprehensive credibility score and the exceeding of limits of each individual credibility factor; Step 6: Based on the determined early warning response, generate a structured early warning message and distribute it to the target aircraft, ground control station, and low-altitude traffic control center through the low-altitude communication network.

2. The reliable early warning method for low-altitude navigation position according to claim 1, characterized in that, In step 1, the target aircraft data includes: raw GNSS observations, real-time position Pu, velocity Vu, time Tu, flight attitude and state parameters calculated by the aircraft itself, attitude angles, barometric altitude, and airspeed from the flight control system; The ground-based augmentation network data includes: real-time acquisition of reference station status, satellite health status, precise ephemeris, differential corrections, and integrity information provided by the ground-based augmentation network through a stable and reliable fiber optic private network communication link; The environmental perception data includes: pre-set flight mission plans, airspace structure information, temporary no-fly zones, the integrity status of broadcasts from other cooperating aircraft, and database information on known interference sources.

3. The reliable early warning method for low-altitude navigation position according to claim 1, characterized in that, In step 4, the credibility factors include the position error factor α_P, the time delay verification factor α_T, the dynamic consistency factor α_K, the signal quality and environment factor α_S, and the spatial domain and rule compliance factor α_F.

4. The reliable early warning method for low-altitude navigation position according to claim 3, characterized in that, The position error factor α_P is obtained by calculating the aircraft's self-reported position Pu and the high-confidence reference position Pref, and the time delay verification factor α_T is obtained by calculating the aircraft's self-reported time Tu and the standard time Tref. When the deviation exceeds the dynamic threshold set based on the protection level, the position error factor and the time delay factor indicate anomalies, respectively. The dynamic consistency factor α_K is calculated using the motion state derived from the high-confidence reference position Pref sequence or inertial measurement unit (IMU) data. It is then used to verify the dynamic consistency with the velocity Vu and attitude information reported by the aircraft, and the vector difference between the position-derived acceleration and the IMU-measured acceleration is calculated. The signal quality and environmental factor α_S are obtained by comprehensively evaluating the quality of the GNSS signal received by the aircraft and its conformity with the dynamic signal quality field. The airspace and rule compliance factor α_F determines whether the aircraft's self-calculated real-time position Pu or high-confidence reference position Pref deviates from the predetermined flight plan route, intrudes into restricted airspace, or conflicts with the surrounding traffic situation.

5. The reliable early warning method for low-altitude navigation position according to claim 1, characterized in that, In step 5, the early warning response includes: Level 1: Under normal or monitoring status, the overall credibility score is higher than the safety threshold, there are no warnings, and continuous monitoring is maintained. Level 2, in a warning or downgraded state. At this time, the overall credibility score decreases or a single non-critical factor is abnormal, and an operation prompt is issued, suggesting that you pay attention to navigation performance. Level 3, in a warning or suspected state, at which point the overall credibility score Score drops significantly, or the error verification factor α_P exceeds the limit significantly but the dynamics are not abnormal for the time being, and a deception or serious interference warning is issued, prompting a switch to an alternative navigation source; Level 4 indicates an emergency or failure state. At this point, the overall credibility score is extremely low, and multiple key factors fail simultaneously, confirming a navigation system failure. The flight control system switches to backup navigation mode and triggers an emergency avoidance procedure.

6. A reliable low-altitude navigation positioning early warning system, characterized in that, include: Airborne intelligent terminal module: integrates GNSS receiver, status sensor and data communication unit at the aircraft end, used to collect, preprocess and upload raw GNSS data and status data, and receive and execute early warning commands; Ground-based augmentation network module: Through GNSS reference stations and monitoring stations evenly distributed within a certain area, it provides high-precision differential correction data (RTCM) data streams, as well as integrity information such as satellite health status, geometric factors, signal quality, and availability; Trusted Situation Awareness Processing Center Module: This is the core computing unit that executes the low-altitude navigation position trusted early warning method as described in any one of claims 1-5; Secure data communication network module: responsible for real-time and reliable data interaction between various modules; User interaction terminal module: includes aircraft cockpit display, operator remote control, and low-altitude traffic control platform, used to visually display aircraft position, reliability status and early warning information.

7. The low-altitude navigation position reliable early warning system according to claim 6, characterized in that, The trusted situation awareness processing center module includes: Data receiving and processing interface: Receives data streams and environmental perception data from the airborne terminal and ground-based augmentation network in step 1; Spatiotemporal location calculation engine: used to execute step 2, calculate the high-confidence reference location Pref and the standard time Tref; Dynamic signal quality field modeling engine: used to perform step 3, integrate crowdsourced data, build and update the dynamic signal environment simulation field map of the service area in real time, and update the interference source database; Multidimensional verification engine: used to execute step 4, and to perform parallel calculations of multidimensional credibility factors including position error factor α_P, time delay verification factor α_T, dynamic consistency factor α_K, signal quality and environment factor α_S, and spatial domain and rule compliance factor α_F. Intelligent Fusion Decision Maker: Used to execute step 5, integrating a fusion decision engine to achieve comprehensive evaluation and early warning level determination; Early warning information interaction interface: used to execute step 6, providing standardized early warning information output to aircraft, operators and low-altitude traffic control centers.

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