Stable flight control methods for unmanned aerial vehicles in high electromagnetic field environments

Abstract

This invention discloses a method for stable flight control of unmanned aerial vehicles (UAVs) in high electromagnetic field environments, belonging to the field of UAV control technology. This method generates an interference-free comparison benchmark by fusing visual and inertial data, and uses this benchmark to diagnose the reliability of data from magnetic compasses and global navigation satellite systems online, generating a quantified reliability index. In a recursive state estimator, the fusion weights of each data source are adaptively adjusted based on this index to generate a robust state estimate that suppresses the effects of interference. The method assesses the inherent uncertainty of the state estimate and automatically switches between various flight modes, such as autonomous flight, assisted stabilization, and pure attitude control, based on the determined confidence level. This invention achieves dynamic matching between control strategies and real-time perception capabilities, significantly improving the flight safety and operational reliability of UAVs in environments with strong interference, such as near high-voltage power lines.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control, and in particular to a method for stable flight control of UAVs in high electromagnetic field environments. Background Technology

[0002] In the power industry, using drones for autonomous inspections of high-voltage transmission corridors has become a key technological means to replace traditional manual inspections and improve operational efficiency and safety. However, when performing such tasks, drones must operate close to power facilities such as high-voltage transmission lines and substations. These areas are characterized by extremely complex and severe electromagnetic interference environments, posing a fatal threat to drones that heavily rely on electronic sensors. Specifically, the strong magnetic field generated by the massive power frequency current in the transmission lines completely suppresses the geomagnetic field signal, causing the magnetic compass used to determine heading to output severely distorted information, which in turn causes the drone to lose control and hover uncontrollably. At the same time, the corona discharge generated on the surface of the high-voltage conductors radiates broadband radio frequency noise, which is sufficient to drown out weak global navigation satellite system signals, causing the drone to fail to locate and drift. Although existing technologies attempt to use techniques such as real-time dynamic differential (RTK) to replace the magnetic compass, RTK itself still relies on stable satellite signals and faces the same risk of losing lock-on under strong interference. Therefore, existing technologies generally lack an effective method to achieve intelligent fault diagnosis, adaptive state estimation, and reliable flight control when multiple core sensors, including RTK, may fail or their data may be contaminated. This greatly limits the operational safety and autonomy of UAVs in environments with strong electromagnetic interference. Summary of the Invention

[0003] To address the aforementioned problems in existing technologies, the present invention aims to provide a method for stable flight control of unmanned aerial vehicles (UAVs) in high electromagnetic field environments, comprising the following steps: Step S1: Real-time acquisition of navigation data output by a set of navigation sensors, wherein the navigation sensors include at least an inertial measurement unit, a magnetic compass, a global navigation satellite system receiver, and a visual sensor.

[0004] Step S2: Diagnose the reliability of the navigation data. Generate a set of motion information as a comparison benchmark using the data from the visual sensor. Compare the data from the magnetic compass and the global navigation satellite system receiver with the comparison benchmark to identify abnormal deviations caused by interference that exceed a preset inconsistency threshold. Based on the degree of the abnormal deviation, generate a numerical reliability index for each data source, where the larger the deviation, the lower the reliability index value.

[0005] Step S3, Adaptive fusion state estimation: In a recursive state estimator, the navigation data and the corresponding numerical reliability index are input; based on the numerical reliability index, the influence weight of the corresponding data source in the fusion update step is adjusted in real time to suppress the contribution of data sources with low reliability index, and a UAV final state estimate that suppresses the influence of abnormal deviation is generated.

[0006] Step S4: Switch the control mode based on confidence level. Quantitatively evaluate the posterior covariance of the final state estimate and map it to a state confidence level consisting of multiple discrete levels. Automatically switch between multiple preset flight control modes according to the current state confidence level.

[0007] Furthermore, in step S2, motion information is generated as a comparison benchmark, and the fusion process includes: Step S201: Integrate the data from the inertial measurement unit to generate a high-frequency motion state prediction value.

[0008] Step S202: By tracking visual feature points between consecutive image frames of the vision sensor, a visual pose measurement value for correcting the predicted value is calculated.

[0009] Step S203: The visual pose measurement value and the motion state prediction value are fused within a filtering framework to output a continuously smooth comparison benchmark that suppresses integral drift.

[0010] Furthermore, in step S2, comparing the data with the comparison benchmark to identify abnormal deviations includes: Step S211: Within a sliding time window, obtain the heading and position sequence of the data source to be diagnosed, and the reference heading and position sequence of the corresponding time generated by the fusion process.

[0011] Step S212: Calculate the difference sequence between the two sequences, and calculate a Mahalanobis distance based on the statistical properties of the difference sequence. The calculation formula is as follows: ,in, Represents Mahalanobis distance, The difference vector represents the current moment, indicating the deviation between the measured value of the data source to be diagnosed and the comparison benchmark value; The mean vector of the difference sequence represents its average systematic deviation; This represents the inverse of the covariance matrix.

[0012] Step S213: Compare the Mahalanobis distance with a preset diagnostic threshold. If the Mahalanobis distance is greater than the threshold, it is determined that there is an abnormal deviation.

[0013] Furthermore, the calculation of the statistical characteristics of the difference sequence in step S212 is achieved through a recursive update process. The update value at each time step is obtained by weighting the difference data at the current time step with the statistical characteristic value calculated at the previous time step. The weighting combination adopts a preset weight factor. The weight factor gives the difference data at the current time step a larger influence weight, while giving the statistical characteristic value at the previous time step an influence weight that decays over time.

[0014] Furthermore, in step S3, the final state estimate of the UAV generated remains continuous in the time domain and is unaffected by any anomalous changes contained in the low-reliability data when either the magnetic compass or the global navigation satellite system receiver is diagnosed as having low reliability indicators. The generation process of the final state estimate smoothly switches from the low-reliability data source to a high-reliability comparison benchmark generated by the fusion of the visual sensor and the inertial measurement unit by using the key information required for its positioning and orientation.

[0015] Furthermore, step S3 involves adjusting the influence weight of the corresponding data source in the fusion update step in real time, including: Step S301: Pre-determine a nominal covariance matrix for each navigation data source to characterize its nominal measurement uncertainty.

[0016] In step S302, in each update iteration of the recursive state estimator, the reliability index of each data source is mapped to an adjustment factor through a preset functional relationship.

[0017] Step S303: The nominal covariance matrix is ​​numerically scaled using the adjustment factor to generate an adjusted covariance matrix, wherein the functional relationship ensures that the lower the reliability index, the larger the adjusted covariance matrix is ​​numerically.

[0018] Step S304: Using the adjusted covariance matrix, complete the update step for fusing measurement data in the recursive state estimator.

[0019] Furthermore, in step S4, the posterior covariance of the final state estimate is evaluated by quantification. Specifically, this means calculating the trace of the posterior state covariance matrix P of the recursive state estimator. The smaller the trace value, the lower the uncertainty.

[0020] Furthermore, the sub-step in step S4 that maps inherent uncertainty to state confidence specifically includes: Step S401: Obtain the trace value U of the posterior state covariance matrix P.

[0021] Step S402: Set at least two uncertainty thresholds T1 and T2, where T1 < T2.

[0022] Step S403: Determine the state confidence level according to the following mapping rules: If U < T1, the state confidence is at the "high" level; if T1 ≤ U < T2, the state confidence is at the "medium" level; if U ≥ T2, the state confidence is at the "low" level.

[0023] Furthermore, the corresponding relationship between the multiple preset flight control modes and the state confidence level is as follows: When the state confidence is at the "high" level, switch to the autonomous waypoint flight mode; when the state confidence is at the "medium" level, switch to the vision-assisted position holding mode; when the state confidence is at the "low" level, switch to the attitude stabilization mode that only stabilizes the attitude.

[0024] Furthermore, in step S4, the quantitative evaluation of the posterior covariance of the final state estimate decouples the posterior covariance matrix into two components respectively representing position uncertainty and attitude uncertainty, and according to the different requirements of the current flight task for position accuracy and attitude stability, the scalar values derived from the two components are weighted and combined to form a task-adaptive comprehensive uncertainty index.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows: Through its hierarchical control architecture, the present invention realizes the online evaluation and quantitative diagnosis of the state of the UAV sensor. This method generates a dynamic comparison benchmark that is not affected by electromagnetic interference by fusing vision and inertial data, and accurately identifies and quantifies data anomalies caused by external strong electromagnetic fields, thereby avoiding the flight control system from making wrong decisions due to adopting contaminated and incorrect data, and significantly improving the flight safety and reliability of the UAV in complex electromagnetic environments.

[0026] Through the adaptive state estimation and control switching mechanism, the present invention realizes the dynamic matching of the control strategy and the perception ability. This method adjusts the data fusion weight according to the reliability index of the online diagnosis, and smoothly transfers the information dependence to ensure the continuity and robustness of the final state estimate when the quality of some sensor data deteriorates. Furthermore, the system determines a system confidence level according to the inherent uncertainty of the state estimate, and automatically switches between multiple preset flight modes accordingly, ensuring that the UAV always executes the optimal control strategy within its ability boundary, and realizing intelligent fault-tolerant flight with interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is an exemplary step flowchart of the control method of the present invention.

[0028] Figure 2This is an exemplary flowchart of a step in the present invention for fusing and processing motion information of a comparison benchmark.

[0029] Figure 3 This is an exemplary flowchart of a step in the present invention for identifying abnormal deviations.

[0030] Figure 4 This is an exemplary flowchart of the fusion update step of the present invention.

[0031] Figure 5 This is an exemplary flowchart of the process of mapping uncertainty to state confidence in this invention. Detailed Implementation

[0032] The present invention will be further described below with reference to specific embodiments.

[0033] like Figure 1 The diagram illustrates an exemplary step in the UAV stable flight control method under high electromagnetic field conditions provided in this embodiment, including the following steps: Step S1: Acquire navigation data output by a set of navigation sensors in real time. The navigation sensors include at least an inertial measurement unit, a magnetic compass, a global navigation satellite system receiver, and a visual sensor.

[0034] In one embodiment, the navigation sensor suite onboard the drone acts as a physical device, outputting its measurement data in real time. An inertial measurement unit (IMU) typically includes a three-axis accelerometer and a three-axis gyroscope, providing the drone's angular velocity and linear acceleration information at high rates, forming the basis for attitude and motion calculations. A magnetic compass provides the drone's heading information in the Earth's magnetic field, but it is highly susceptible to interference in strong electromagnetic fields. Global navigation satellite system (GNSS) receivers, such as GPS and BeiDou modules, receive satellite signals to provide the drone's global position and velocity information, but are also vulnerable to electromagnetic interference or signal blockage. Visual sensors, such as monocular or binocular cameras, capture environmental image data; their advantage lies in their resistance to electromagnetic interference and the provision of rich local environmental information. This sensor data is transmitted in real time to an onboard processor, such as a high-performance ARM processor or FPGA, via a data bus and interface on the drone's flight control board, preparing for subsequent diagnostics and state estimation.

[0035] Step S2 involves diagnosing the reliability of the navigation data. A set of motion information is generated using data from the visual sensor as a comparison benchmark. Data from the magnetic compass and the Global Navigation Satellite System receiver are compared with this benchmark to identify abnormal deviations caused by interference that exceed a preset inconsistency threshold. Based on the degree of abnormal deviation, a numerical reliability index is generated for each data source, with a higher reliability index value for larger deviations. This step first generates a highly reliable comparison benchmark through meticulous data processing, and then compares data from other susceptible sensors with this benchmark to detect anomalies.

[0036] like Figure 2 The figure shows step S2 of this embodiment, which involves fusing and processing the motion information of the comparison benchmark, including: Step S201: Integrate the data from the inertial measurement unit to generate a high-frequency motion state prediction value; this is a rough pose estimate of the UAV obtained by inertial navigation calculation of acceleration and angular velocity data.

[0037] Step S202 involves calculating a visual pose measurement to correct the predicted value by tracking visual feature points between consecutive image frames from a visual sensor, for example using the FAST feature point detector and the KLT optical flow tracking algorithm. This process is typically implemented using visual odometry or visual SLAM algorithms, which can provide high-precision relative pose information in a local environment.

[0038] Step S203 involves fusing the visual pose measurement values ​​and motion state prediction values ​​within a filtering framework to output a smooth, continuously flowing comparison benchmark that suppresses integral drift. This benchmark incorporates high-frequency pose and position information and exhibits high robustness in local environments.

[0039] Next, the system identifies anomalous deviations by comparing data from the magnetic compass and the Global Navigation Satellite System receiver with this high-precision comparison benchmark.

[0040] like Figure 3 The following describes step S2 of this embodiment, where data is compared with a benchmark to identify abnormal deviations, including: Step S211: Within a sliding time window, acquire the heading and position sequence of the data source to be diagnosed, and the corresponding time reference heading and position sequence generated by the fusion processing; this time window is usually between several hundred milliseconds and several seconds.

[0041] Step S212: Calculate the difference sequence between the two sequences, and calculate a Mahalanobis distance based on the statistical properties of the difference sequence; the calculation formula is as follows: ,in, Represents Mahalanobis distance, The difference vector represents the current moment, indicating the deviation between the measured value of the data source to be diagnosed and the comparison benchmark value; The mean vector of the difference sequence represents its average systematic deviation; This represents the inverse of the covariance matrix.

[0042] Mahalanobis distance effectively measures the statistical deviation of multidimensional data points when considering covariance. The calculation of the statistical properties of the difference sequence is achieved through a recursive update process. The updated value at each time step is obtained by weighting the difference data at the current time step with the statistical property value calculated at the previous time step. The weighting combination uses a preset weighting factor, which assigns a larger influence weight to the difference data at the current time step, while assigning a time-decreasing influence weight to the statistical property value at the previous time step, in order to reflect the timeliness of the data.

[0043] Step S213: Compare the Mahalanobis distance with a preset diagnostic threshold. If the Mahalanobis distance is greater than the threshold, an abnormal deviation is determined. Based on the degree of abnormal deviation, the system generates a numerical reliability index for each data source. The larger the deviation, the lower the reliability index value. This index will serve as an important weighting factor for subsequent state estimation.

[0044] Step S3, adaptive fusion state estimation: In a recursive state estimator, navigation data and corresponding numerical reliability indices are input. Based on the numerical reliability indices, the influence weights of the corresponding data sources in the fusion update step are adjusted in real time to suppress the contribution of data sources with low reliability indices, generating a final UAV state estimate that suppresses the impact of abnormal deviations. In one embodiment, a recursive state estimator, such as a Kalman filter or its extended version, such as an extended Kalman filter or an unscented Kalman filter, serves as the core algorithm module and runs on the UAV's main control chip or dedicated navigation processor. This estimator takes the original navigation data obtained in S1 and the numerical reliability indices generated in S2 as inputs. To effectively handle abnormal deviations caused by low reliability data, the estimator adjusts the influence weights of the corresponding data sources in the fusion update step in real time.

[0045] like Figure 4 As shown, step S3 of this embodiment involves real-time adjustment of the influence weight of the corresponding data source in the fusion update step, including: Step S301: Pre-determine a nominal covariance matrix for each navigation data source to characterize its nominal measurement uncertainty; this matrix reflects the measurement noise level of the sensor under ideal conditions.

[0046] In step S302, during each update iteration of the recursive state estimator, a preset functional relationship is used to map the reliability index of each data source to an adjustment factor; similarly, a preset functional relationship is used to map the reliability index of each data source calculated in S2 to an adjustment factor. This functional relationship ensures that the lower the reliability index, the more significantly the weight of the data source in the fusion update is reduced by the corresponding adjustment factor.

[0047] Step S303: The nominal covariance matrix is ​​numerically scaled using an adjustment factor to generate an adjusted covariance matrix. The functional relationship ensures that the lower the reliability index, the larger the adjusted covariance matrix is ​​numerically, thereby effectively reducing the contribution of the data source in the fusion update.

[0048] Step S304 uses the adjusted covariance matrix to complete the update step for fusing measurement data in the recursive state estimator. Through this adaptive weight adjustment mechanism, when either the magnetic compass or the GNSS receiver is diagnosed as having low reliability indicators, the final state estimate remains continuous in the time domain and effectively suppresses the impact of anomalous mutations contained in the low-reliability data. The final state estimate generation process smoothly switches the key information required for positioning and orientation from the low-reliability data source to a high-reliability comparison benchmark generated by the fusion of visual sensors and the inertial measurement unit, ensuring the accuracy and stability of the estimate, and serves as input to step S4.

[0049] Step S4: Switch the control mode based on the confidence level. Quantitatively evaluate the posterior covariance of the final state estimate and map it to a state confidence level consisting of multiple discrete levels. Automatically switch between multiple preset flight control modes according to the current state confidence level.

[0050] In step S4, the posterior covariance of the final state estimate is evaluated by quantification. Specifically, this means calculating the trace of the posterior state covariance matrix P of the recursive state estimator. The smaller the trace value, the lower the uncertainty.

[0051] like Figure 5 The diagram shows the step S4 in this embodiment, which maps inherent uncertainty to state confidence. Specifically, it includes: Step S401 first obtains the trace of the posterior state covariance matrix. To map inherent uncertainty to understandable state confidence, step S402 sets at least two uncertainty thresholds T1 and T2, where T1 is less than T2. ​​Next, step S403 determines the state confidence level according to a preset mapping rule: if the trace is less than T1, the state confidence is "high"; if the trace is between T1 and T2, the state confidence is "medium"; and if the trace is greater than or equal to T2, the state confidence is "low". For a more refined evaluation, the quantitative assessment of the posterior covariance of the final state estimate can further decouple the posterior state covariance matrix into two components representing position uncertainty and attitude uncertainty, respectively. Based on the different requirements of the current flight mission for position accuracy and attitude stability, the scalar values ​​derived from the two components are weighted and combined to form a mission-adaptive comprehensive uncertainty index.

[0052] The correspondence between multiple preset flight control modes and state confidence levels is as follows: when the state confidence level is "high", switch to autonomous waypoint flight mode; when the state confidence level is "medium", switch to visual-assisted position holding mode; when the state confidence level is "low", switch to attitude stabilization mode that only stabilizes the attitude.

[0053] In step S4, the quantification evaluation of the posterior covariance of the final state estimate decouples the posterior covariance matrix into two components that represent position uncertainty and attitude uncertainty, respectively. Based on the different requirements of the current flight mission for position accuracy and attitude stability, the scalar values ​​derived from the two components are weighted and combined to form a mission-adaptive comprehensive uncertainty index.

[0054] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the scope defined by the invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for stable flight control of unmanned aerial vehicles (UAVs) under high electromagnetic field conditions, characterized by: Includes the following steps, Step S1: Real-time acquisition of navigation data output by a set of navigation sensors, wherein the navigation sensors include at least an inertial measurement unit, a magnetic compass, a global navigation satellite system receiver, and a visual sensor; Step S2: Diagnose the reliability of the navigation data by generating a set of motion information as a comparison benchmark using the data from the visual sensor; compare the data from the magnetic compass and the global navigation satellite system receiver with the comparison benchmark to identify abnormal deviations caused by interference that exceed a preset inconsistency threshold. Based on the degree of the abnormal deviation, a numerical reliability index is generated for each data source, where the greater the deviation, the lower the reliability index value. Step S3, Adaptive fusion state estimation: In a recursive state estimator, the navigation data and the corresponding numerical reliability index are input; based on the numerical reliability index, the influence weight of the corresponding data source in the fusion update step is adjusted in real time to suppress the contribution of data sources with low reliability index, and a UAV final state estimate that suppresses the influence of abnormal deviation is generated. Step S4: Switch the control mode based on confidence level. Quantitatively evaluate the posterior covariance of the final state estimate and map it to a state confidence level consisting of multiple discrete levels. Automatically switch between multiple preset flight control modes according to the current state confidence level.

2. The method for stable flight control of unmanned aerial vehicles in a high electromagnetic field environment according to claim 1, characterized in that: In step S2, motion information is generated as a comparison benchmark, and the fusion process includes: Step S201: Integrate the data from the inertial measurement unit to generate a high-frequency motion state prediction value; Step S202: By tracking visual feature points between consecutive image frames of the visual sensor, a visual pose measurement value for correcting the predicted value is calculated. Step S203: The visual pose measurement value and the motion state prediction value are fused within a filtering framework to output a continuously smooth comparison benchmark that suppresses integral drift.

3. The method for stable flight control of unmanned aerial vehicles in a high electromagnetic field environment according to claim 1, characterized in that: Step S2, which compares the data with the comparison benchmark to identify abnormal deviations, includes: Step S211: Within a sliding time window, obtain the heading and position sequence of the data source to be diagnosed, and the reference heading and position sequence of the corresponding time generated by the fusion process; Step S212: Calculate the difference sequence between the two sequences, and calculate a Mahalanobis distance based on the statistical properties of the difference sequence; the calculation formula is as follows: ,in, Represents Mahalanobis distance, The difference vector represents the current moment, indicating the deviation between the measured value of the data source to be diagnosed and the comparison benchmark value; The mean vector of the difference sequence represents its average systematic deviation; The inverse matrix of the covariance matrix; Step S213: Compare the Mahalanobis distance with a preset diagnostic threshold. If the Mahalanobis distance is greater than the threshold, it is determined that there is an abnormal deviation.

4. The method for stable flight control of unmanned aerial vehicles in a high electromagnetic field environment according to claim 3, characterized in that: The calculation of the statistical characteristics of the difference sequence in step S212 is achieved through a recursive update process. The update value at each time step is obtained by weighting the difference data at the current time step with the statistical characteristic value calculated at the previous time step. The weighting combination adopts a preset weight factor. The weight factor gives the difference data at the current time step a larger influence weight, while giving the statistical characteristic value at the previous time step an influence weight that decays over time.

5. The method for stable flight control of unmanned aerial vehicles in a high electromagnetic field environment according to claim 1, characterized in that: The final state estimation of the drone generated in step S3 is such that when any data source in the magnetic compass and the global navigation satellite system receiver is diagnosed with a low reliability index, the value of the final state estimation remains continuous in the time domain and is not affected by the abnormal mutations contained in the low reliability data; the generation process of the final state estimation smoothly switches the main information required for its positioning and orientation from the low reliability data source to the high reliability comparison benchmark generated by the fusion of the visual sensor and the inertial measurement unit.

6. The method for stable flight control of unmanned aerial vehicles in a high electromagnetic field environment according to claim 1, characterized in that: The real-time adjustment of the influence weight of the corresponding data source in the fusion update step in step S3 includes: Step S301, preset a nominal covariance matrix for each navigation data source to represent its nominal measurement uncertainty; Step S302, in each update iteration of the recursive state estimator, map the reliability index of each data source to an adjustment factor through a preset functional relationship; Step S303, use the adjustment factor to numerically scale the nominal covariance matrix to generate an adjusted covariance matrix, where the functional relationship ensures that the lower the reliability index, the larger the adjusted covariance matrix numerically; Step S304, use the adjusted covariance matrix to complete the update step for fusing measurement data in the recursive state estimator.

7. The method for stable flight control of unmanned aerial vehicles in a high electromagnetic field environment according to claim 1, characterized in that: The quantification and evaluation of the posterior covariance of the final state estimation in step S4 specifically refers to calculating the trace of the posterior state covariance matrix P of the recursive state estimator. The smaller the trace value, the lower the uncertainty.

8. The method for stable flight control of unmanned aerial vehicles in a high electromagnetic field environment according to claim 1, characterized in that: The sub-step of mapping the intrinsic uncertainty to the state confidence level in step S4 specifically includes: Step S401, obtain the trace value U of the posterior state covariance matrix P; Step S402, set at least two uncertainty thresholds T1 and T2, where T1 < T2; Step S403, determine the state confidence level according to the following mapping rules: if U < T1, the state confidence level is "high"; if T1 ≤ U < T2, the state confidence level is "medium"; if U ≥ T2, the state confidence level is "low".

9. The method for stable flight control of a UAV in a high electromagnetic field environment according to claim 1, characterized in that: The corresponding relationship between the multiple preset flight control modes and the state confidence level is: when the state confidence level is "high", switch to the autonomous waypoint flight mode; when the state confidence level is "medium", switch to the visual-aided position-holding mode; when the state confidence level is "low", switch to the attitude stabilization mode that only stabilizes the attitude.

10. The method for stable flight control of a UAV in a high electromagnetic field environment according to claim 1, characterized in that: The quantification and evaluation of the posterior covariance of the final state estimation in step S4 decouples the posterior covariance matrix into two components respectively representing position uncertainty and attitude uncertainty, and according to the different requirements of the current flight mission for position accuracy and attitude stability, weights and combines the scalar values derived from the two components to form a task-adaptive comprehensive uncertainty index.

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