A method and system for monitoring and early warning of the attitude stability of an unmanned aerial vehicle antenna

Abstract

The application discloses a kind of unmanned aerial vehicle airborne antenna attitude stability monitoring early warning method and system, belong to antenna pointing stability technical field.The application is formed by combining unmanned aerial vehicle real-time body attitude data and communication signal strength data time sequence alignment data pair, based on body attitude data and preset attitude signal strength mapping model, the expected signal strength and confidence interval corresponding to current attitude are obtained, and the expected signal strength is compared with communication signal strength data to generate the first early warning signal, based on body attitude data and unmanned aerial vehicle kinematics model prediction future attitude trajectory, the predicted antenna pointing deviation angle sequence is solved and compared with dynamic risk threshold to generate the second early warning signal, realize the real-time comprehensive monitoring of unmanned aerial vehicle airborne antenna attitude stability and communication signal quality, solve the problem that antenna pointing stability monitoring early warning is not accurate and timely in the prior art.

Description

Technical Field

[0001] This invention relates to the field of antenna pointing and stabilization technology, and in particular to a method and system for monitoring and early warning of attitude stability of an airborne antenna for unmanned aerial vehicles (UAVs). Background Technology

[0002] With the rapid development of drone technology, it has been widely used in many fields such as communication, surveying and mapping, and logistics. In these applications, the stability and reliability of the drone's onboard antenna are crucial. The stability of the antenna attitude directly affects the signal quality of the communication link, and thus affects the drone's flight safety and mission execution effectiveness.

[0003] Existing technologies, such as the control method and system for a phased array antenna module disclosed in Chinese Patent Publication No. CN113690617A, acquire the satellite signal strength received by the phased array antenna module. When the signal strength is lower than a preset threshold, the system estimates the current estimated attitude angle based on the antenna's historical attitude angles and adjusts the antenna module's attitude angle to maintain signal reception quality. Another example is the antenna attitude monitoring and early warning system based on the Internet of Things disclosed in Chinese Patent Publication No. CN116147572A, which identifies the antenna status by monitoring antenna attitude data during antenna operation and provides level-based early warnings for abnormal states.

[0004] In existing technologies, the former can only passively adjust the antenna attitude after signal strength anomalies, and cannot actively monitor attitude stability or provide early warnings of attitude deviations, thus failing to effectively prevent the risk of communication link interruption. While the latter can monitor antenna attitude data, it is insufficient to meet the high-precision attitude monitoring and early warning requirements of UAVs in complex flight environments. Neither technology can achieve real-time, accurate, and forward-looking comprehensive monitoring and early warning of the stability of UAV onboard antenna attitude and communication signal quality, resulting in insufficient precision and timeliness in monitoring and warning of antenna pointing stability. Summary of the Invention

[0005] This application provides a method and system for monitoring and warning the attitude stability of UAV airborne antennas, which solves the problem that the monitoring and warning of antenna pointing stability in the prior art is not accurate and timely enough, and realizes real-time comprehensive monitoring of the attitude stability of UAV airborne antennas and the quality of communication signals.

[0006] This application provides a method for monitoring and warning the attitude stability of an unmanned aerial vehicle (UAV) airborne antenna, including:

[0007] Acquire and synchronize real-time body attitude data of the UAV with communication signal strength data of the airborne antenna to form a time-aligned attitude signal data pair;

[0008] Based on the body attitude data and the preset attitude signal strength mapping model, the expected signal strength and confidence interval corresponding to the current attitude are obtained. The expected signal strength is compared with the communication signal strength data to generate the first warning signal.

[0009] Based on the aircraft attitude data and the UAV kinematic model, the predicted attitude trajectory within a certain time window is predicted. Based on the predicted attitude trajectory and the ground station position coordinates, the predicted antenna pointing deviation angle sequence is calculated. The predicted antenna pointing deviation angle sequence is compared with the dynamic risk threshold to generate a second early warning signal.

[0010] The attitude signal intensity mapping model is updated online based on the attitude signal data pairs.

[0011] The first and second early warning signals are combined to output the final early warning command.

[0012] Furthermore, the process of obtaining the expected signal strength and confidence interval corresponding to the current attitude based on the body attitude data and the preset attitude signal strength mapping model specifically includes:

[0013] Obtain the body attitude data at the current time t. ,in For roll angle, The pitch angle, Yaw angle;

[0014] The body attitude data is input into a preset attitude signal intensity mapping model to obtain the expected signal intensity and the variance of the predicted distribution.

[0015] The attitude signal strength mapping model The construction process is as follows:

[0016] Historical attitude signal data pairs were acquired, and a nonlinear mapping model was established using Gaussian process regression. The nonlinear mapping model is as follows:

[0017] ;

[0018] In the formula, It is a mean function. The covariance function is used to characterize the correlation between the signal intensities corresponding to different attitude points P and P'.

[0019] arbitrary machine posture data Substituting into the nonlinear mapping model:

[0020] ;

[0021] The variance of the expected signal strength and the predicted distribution is obtained;

[0022] The confidence interval is obtained from the variance.

[0023] Furthermore, the step of comparing the desired signal strength with the communication signal strength data to generate a first warning signal specifically includes:

[0024] Obtain the measured communication signal strength data at time t. ;

[0025] Based on the desired signal strength and variance Calculate the standardized residuals of the signal strength deviation:

[0026] ;

[0027] Determine whether the standardized residuals meet the early warning conditions: ,in, The coefficients are the preset confidence interval coefficients;

[0028] If this condition is met, the first warning signal is generated.

[0029] Furthermore, the prediction of the attitude trajectory within a certain time window based on the aircraft attitude data and the UAV kinematic model specifically includes:

[0030] Get the attitude quaternion at the current time t and the body angular velocity vector ;

[0031] By numerically integrating the kinematic model of the UAV, the prediction is obtained. The attitude quaternion of time :

[0032] ;

[0033] in, To represent quaternion multiplication, It is a pure quaternion composed of the body's angular velocity vector. To predict the time step.

[0034] Furthermore, the step of calculating the predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates specifically includes:

[0035] The predicted attitude quaternion Convert to rotation matrix ;

[0036] This rotation matrix is ​​used to determine the nominal pointing vector of the antenna in the body coordinate system. Transform to the navigation coordinate system to obtain the predicted pointing vector. ;

[0037] Based on the current location coordinates of the drone Ground station location coordinates Calculate the line-of-sight vector in the navigation coordinate system:

[0038] ;

[0039] in, This is the position vector of the UAV to the ground station;

[0040] Calculate the predicted pointing vector and the line-of-sight vector pointing to the ground station in the navigation coordinate system. The angle between them is used as the predicted antenna pointing deviation angle. :

[0041] ;

[0042] in, It is the nominal pointing vector of the antenna fixed to the body. It is the vector pointing from the drone's current position to the ground station.

[0043] Furthermore, the comparison between the predicted antenna pointing deviation angle sequence and the dynamic risk threshold specifically includes:

[0044] The dynamic risk threshold at the current time t The calculation method is as follows:

[0045] ;

[0046] in, Based on the static risk threshold, for The angular acceleration vector at time t. Represents the L2 norm. This is the integration time window.

[0047] Furthermore, the generation of the second warning signal specifically includes:

[0048] If within the prediction time window Within, there exists at any time Predicted antenna pointing deviation angle satisfy Then the second warning signal will be generated.

[0049] Furthermore, the step of updating the attitude signal intensity mapping model online based on the attitude signal data pairs specifically includes:

[0050] The newly acquired attitude signal data pairs are used as new evidence. ;

[0051] The hyperparameter set of the attitude signal intensity mapping model is updated using Bayesian inference. ;

[0052] The posterior probability density function of the hyperparameters is: ;

[0053] The hyperparameters are updated by maximizing the posterior probability.

[0054] Furthermore, the fusion of the first and second warning signals to output the final warning command specifically includes:

[0055] Define the first warning signal Second warning signal , where 0 indicates no warning and 1 indicates a warning has been triggered;

[0056] Final warning level Determined through the following logic:

[0057] ;

[0058] when When this occurs, a Level 1 warning command is output to indicate abnormal signal quality;

[0059] when At that time, a level-two early warning command is output to indicate a risk in the attitude direction;

[0060] when At that time, a level three early warning command is output to indicate a serious risk to the communication link;

[0061] The warning instruction includes a warning level, a trigger type identifier, a timestamp of the trigger time, and a snapshot of the current attitude signal data. And the predicted antenna pointing deviation angle value.

[0062] This application provides an embodiment of an unmanned aerial vehicle (UAV) airborne antenna attitude stability monitoring and early warning system, used to implement a method for monitoring and early warning of UAV airborne antenna attitude stability, including:

[0063] The data acquisition module is used to acquire and synchronize the real-time body attitude data of the UAV with the communication signal strength data of the airborne antenna to form a time-aligned attitude signal data pair.

[0064] The first early warning module is used to obtain the expected signal strength and confidence interval corresponding to the current attitude based on the body attitude data and the preset attitude signal strength mapping model, compare the expected signal strength with the communication signal strength data, and generate the first early warning signal.

[0065] The second early warning module is used to predict the predicted attitude trajectory within a certain time window based on the body attitude data and the UAV kinematic model, and calculate the predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates. The predicted antenna pointing deviation angle sequence is compared with the dynamic risk threshold to generate a second early warning signal.

[0066] The online update module is used to update the attitude signal intensity mapping model online based on the attitude signal data pairs;

[0067] The early warning output module is used to merge the first early warning signal and the second early warning signal to output the final early warning command.

[0068] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0069] 1. By acquiring and synchronizing real-time UAV body attitude data and airborne antenna communication signal strength data, a time-aligned attitude signal data pair is formed, thereby establishing a dynamic correlation between attitude and signal strength. This enables real-time comprehensive monitoring of UAV airborne antenna attitude stability and communication signal quality, effectively solving the problem of insufficient accuracy and timeliness in existing technologies for monitoring and warning of antenna pointing stability.

[0070] 2. By obtaining the expected signal strength and confidence interval corresponding to the current attitude based on the body attitude data and the preset attitude signal strength mapping model, the expected signal strength is compared with the communication signal strength data to generate the first warning signal, thereby actively monitoring the abnormal fluctuation of the signal strength under the current attitude, and thus realizing early warning of the risk of communication link interruption.

[0071] 3. By predicting future attitude trajectories based on body attitude data and UAV kinematic models, the predicted antenna pointing deviation angle sequence is calculated and compared with a dynamic risk threshold to generate a second early warning signal, thereby proactively assessing the risk of antenna pointing deviation and thus achieving early prevention of potential communication link problems. Attached Figure Description

[0072] Figure 1 An architecture diagram of an unmanned aerial vehicle (UAV) airborne antenna attitude stability monitoring and early warning method provided in this application embodiment;

[0073] Figure 2 This is a structural diagram of an unmanned aerial vehicle (UAV) airborne antenna attitude stability monitoring and early warning system provided in an embodiment of this application. Detailed Implementation

[0074] This application provides a method and system for monitoring and warning the attitude stability of an unmanned aerial vehicle (UAV) onboard antenna. This solves the problem that the monitoring and warning of antenna pointing stability in the prior art is not accurate or timely enough. By acquiring and synchronizing the real-time body attitude data of the UAV and the communication signal strength data of the onboard antenna, a time-aligned attitude signal data pair is formed, thereby establishing a dynamic correlation between attitude and signal strength. This enables real-time comprehensive monitoring of the attitude stability and communication signal quality of the UAV onboard antenna.

[0075] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0076] like Figure 1 As shown, this application provides a method for monitoring and warning the attitude stability of an airborne antenna on a UAV. This method is applied to a system for monitoring and warning the attitude stability of an airborne antenna on a UAV, and includes:

[0077] Acquire and synchronize real-time body attitude data of the UAV with communication signal strength data of the airborne antenna to form a time-aligned attitude signal data pair;

[0078] Based on the body attitude data and the preset attitude signal strength mapping model, the expected signal strength and confidence interval corresponding to the current attitude are obtained. The expected signal strength is compared with the communication signal strength data to generate the first warning signal.

[0079] Based on the aircraft attitude data and the UAV kinematic model, the predicted attitude trajectory within a certain time window is predicted. Based on the predicted attitude trajectory and the ground station position coordinates, the predicted antenna pointing deviation angle sequence is calculated. The predicted antenna pointing deviation angle sequence is compared with the dynamic risk threshold to generate a second early warning signal.

[0080] The attitude signal intensity mapping model is updated online based on the attitude signal data pairs.

[0081] The first and second early warning signals are combined to output the final early warning command.

[0082] Furthermore, the process of obtaining the expected signal strength and confidence interval corresponding to the current attitude based on the body attitude data and the preset attitude signal strength mapping model specifically includes:

[0083] Obtain the body attitude data at the current time t. ,in For roll angle, The pitch angle, Yaw angle;

[0084] The body attitude data is input into a preset attitude signal intensity mapping model to obtain the expected signal intensity and the variance of the predicted distribution.

[0085] The attitude signal strength mapping model The construction process is as follows:

[0086] Historical attitude signal data pairs were acquired, and a nonlinear mapping model was established using Gaussian process regression. The nonlinear mapping model is as follows:

[0087] ;

[0088] In the formula, It is a mean function. The covariance function is used to characterize the correlation between the signal intensities corresponding to different attitude points P and P'.

[0089] arbitrary machine posture data Substituting into the nonlinear mapping model:

[0090] ;

[0091] Obtain the desired signal strength and the variance of the predicted distribution ;

[0092] Calculation based on variance Confidence interval:

[0093] ;

[0094] in is the quantile of the standard normal distribution.

[0095] Furthermore, the step of comparing the desired signal strength with the communication signal strength data to generate a first warning signal specifically includes:

[0096] Obtain the measured communication signal strength data at time t. ;

[0097] Based on the desired signal strength and variance Calculate the standardized residuals of the signal strength deviation:

[0098] ;

[0099] Determine whether the standardized residuals meet the early warning conditions: ,in, The preset confidence interval coefficient characterizes the sensitivity of the early warning.

[0100] If this condition is met, the first warning signal is generated;

[0101] The The value ranges from 2.0 to 3.0, corresponding to a confidence level of 95% to 99.7%.

[0102] Furthermore, the prediction of the attitude trajectory within a certain time window based on the aircraft attitude data and the UAV kinematic model specifically includes:

[0103] Get the attitude quaternion at the current time t and the body angular velocity vector ;

[0104] By numerically integrating the kinematic model of the UAV, the prediction is obtained. The attitude quaternion of time :

[0105] ;

[0106] in, To represent quaternion multiplication, It is a pure quaternion composed of the body's angular velocity vector. To predict the time step.

[0107] Furthermore, the step of calculating the predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates specifically includes:

[0108] The predicted attitude quaternion Convert to rotation matrix ;

[0109] This rotation matrix is ​​used to determine the nominal pointing vector of the antenna in the body coordinate system. Transform to the navigation coordinate system to obtain the predicted pointing vector. ;

[0110] Based on the current location coordinates of the drone Ground station location coordinates Calculate the line-of-sight vector in the navigation coordinate system:

[0111] ;

[0112] in, This is the position vector of the UAV to the ground station;

[0113] Calculate the predicted pointing vector and the line-of-sight vector pointing to the ground station in the navigation coordinate system. The angle between them is used as the predicted antenna pointing deviation angle. :

[0114] ;

[0115] in, It is the nominal pointing vector of the antenna fixed to the body. It is the vector pointing from the drone's current position to the ground station, which can be calculated from GPS data.

[0116] Furthermore, the comparison between the predicted antenna pointing deviation angle sequence and the dynamic risk threshold specifically includes:

[0117] The dynamic risk threshold at the current time t The calculation method is as follows:

[0118] ;

[0119] in, Based on the static risk threshold, for The angular acceleration vector at time t is obtained by differentiating the angular velocity. Represents the L2 norm. The integral time window is used to quantify the intensity of the drone's maneuvering by integrating the recent angular acceleration. The more intense the maneuvering, the higher the risk threshold.

[0120] Furthermore, the generation of the second warning signal specifically includes:

[0121] If within the prediction time window Within, there exists at any time Predicted antenna pointing deviation angle satisfy Then the second warning signal will be generated.

[0122] Furthermore, the step of updating the attitude signal intensity mapping model online based on the attitude signal data pairs specifically includes:

[0123] The newly acquired attitude signal data pairs are used as new evidence. ;

[0124] The hyperparameter set of the attitude signal intensity mapping model is updated using Bayesian inference. ;

[0125] The posterior probability density function of the hyperparameters is: ;

[0126] Update the hyperparameters by maximizing the posterior probability;

[0127] Its posterior probability Proportional to the likelihood function Prior probabilities of the previous hyperparameters The product of these factors enables the model to learn and adapt online.

[0128] in, Represents historical data. Represents newly collected data. This is the set of model hyperparameters, including the length scale of the covariance function, signal variance, etc.

[0129] Furthermore, the fusion of the first and second warning signals to output the final warning command specifically includes:

[0130] Define the first warning signal Second warning signal , where 0 indicates no warning and 1 indicates a warning has been triggered;

[0131] Final warning level Determined through the following logic:

[0132] ;

[0133] when When this occurs, a Level 1 warning command is output to indicate abnormal signal quality;

[0134] when At that time, a level-two early warning command is output to indicate a risk in the attitude direction;

[0135] when At that time, a level three early warning command is output to indicate a serious risk to the communication link;

[0136] The warning instruction includes a warning level, a trigger type identifier, a timestamp of the trigger time, and a snapshot of the current attitude signal data. And the predicted antenna pointing deviation angle value.

[0137] like Figure 2 As shown, this application provides an unmanned aerial vehicle (UAV) airborne antenna attitude stability monitoring and early warning system to implement the UAV airborne antenna attitude stability monitoring and early warning method, including: a data pair acquisition module, a first early warning module, a second early warning module, an online update module, and an early warning output module;

[0138] The data acquisition module is used to acquire and synchronize the real-time body attitude data of the UAV and the communication signal strength data of the airborne antenna to form a time-aligned attitude signal data pair.

[0139] The first early warning module is used to obtain the expected signal strength and confidence interval corresponding to the current posture based on the body posture data and the preset posture signal strength mapping model, compare the expected signal strength with the communication signal strength data, and generate a first early warning signal.

[0140] The second early warning module is used to predict the predicted attitude trajectory within a certain time window based on the body attitude data and the UAV kinematic model, and calculate the predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates. The predicted antenna pointing deviation angle sequence is compared with the dynamic risk threshold to generate a second early warning signal.

[0141] The online update module is used to update the attitude signal intensity mapping model online based on the attitude signal data pairs;

[0142] The warning output module is used to merge the first warning signal and the second warning signal to output the final warning command.

[0143] In summary, this application embodiment combines real-time UAV body attitude data and communication signal strength data to form time-aligned data pairs, and combines the attitude signal strength mapping model with the UAV kinematic model to achieve active monitoring of current signal strength, prediction and early warning of antenna pointing deviation under future attitude trajectory, and online updating of the model. This effectively prevents the risk of communication link interruption, meets the UAV's need for high-precision attitude monitoring and early warning in complex flight environments, and improves the monitoring accuracy and timeliness of early warning for antenna attitude stability and communication signal quality.

[0144] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0145] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0147] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0148] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0149] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0150] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for monitoring and early warning of attitude stability of an unmanned aerial vehicle (UAV) on-board antenna, characterized in that, Includes the following steps: Acquire and synchronize real-time body attitude data of the UAV with communication signal strength data of the airborne antenna to form a time-aligned attitude signal data pair; Based on the body attitude data and the preset attitude signal strength mapping model, the expected signal strength and confidence interval corresponding to the current attitude are obtained. The expected signal strength is compared with the communication signal strength data to generate the first warning signal. Based on the aircraft attitude data and the UAV kinematic model, the predicted attitude trajectory within a certain time window is predicted. Based on the predicted attitude trajectory and the ground station position coordinates, the predicted antenna pointing deviation angle sequence is calculated. The predicted antenna pointing deviation angle sequence is compared with the dynamic risk threshold to generate a second early warning signal. The attitude signal intensity mapping model is updated online based on the attitude signal data pairs. The first and second early warning signals are combined to output the final early warning command.

2. The unmanned aerial vehicle airborne antenna attitude stability monitoring and early warning method of claim 1, wherein, The method based on the body posture data and the preset posture signal intensity mapping model obtains the expected signal intensity and confidence interval corresponding to the current posture, specifically including: acquiring body attitude data at a current time t wherein is a roll angle, is a pitch angle, is a yaw angle; The body attitude data is input into a preset attitude signal intensity mapping model to obtain the expected signal intensity and the variance of the predicted distribution. The attitude signal strength mapping model The construction process is as follows: Historical attitude signal data pairs were acquired, and a nonlinear mapping model was established using Gaussian process regression. The nonlinear mapping model is as follows: ； In the formula, It is a mean function. The covariance function is used to characterize the correlation between the signal intensities corresponding to different attitude points P and P'. arbitrary machine posture data Substituting into the nonlinear mapping model: ； The variance of the expected signal strength and the predicted distribution is obtained; The confidence interval is obtained from the variance.

3. The method for monitoring and early warning the attitude stability of an unmanned aerial vehicle (UAV) airborne antenna as described in claim 2, characterized in that, The step of comparing the desired signal strength with the communication signal strength data to generate a first warning signal specifically includes: Obtain the measured communication signal strength data at time t. ; Based on the desired signal strength and variance Calculate the standardized residuals of the signal strength deviation: ； Determine whether the standardized residuals meet the early warning conditions: ,in, The coefficients are the preset confidence interval coefficients; If this condition is met, the first warning signal is generated.

4. The method for monitoring and early warning of attitude stability of an airborne antenna for unmanned aerial vehicles as described in claim 1, characterized in that, The prediction of the attitude trajectory within a certain time window based on the aircraft attitude data and the UAV kinematic model specifically includes: Get the attitude quaternion at the current time t and the body angular velocity vector ; By numerically integrating the kinematic model of the UAV, the prediction is obtained. The attitude quaternion of time : ； in, To represent quaternion multiplication, It is a pure quaternion composed of the body's angular velocity vector. To predict the time step.

5. The method for monitoring and early warning of attitude stability of an airborne antenna for a UAV as described in claim 4, characterized in that, The process of calculating the predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates specifically includes: The predicted attitude quaternion Convert to rotation matrix ; This rotation matrix is ​​used to determine the nominal pointing vector of the antenna in the body coordinate system. Transform to the navigation coordinate system to obtain the predicted pointing vector. ; Based on the current location coordinates of the drone Ground station location coordinates Calculate the line-of-sight vector in the navigation coordinate system: ； in, This is the position vector of the UAV to the ground station; Calculate the predicted pointing vector and the line-of-sight vector pointing to the ground station in the navigation coordinate system. The angle between them is used as the predicted antenna pointing deviation angle. : ； in, It is the nominal pointing vector of the antenna fixed to the body. It is the vector pointing from the drone's current position to the ground station.

6. The method for monitoring and early warning of attitude stability of an unmanned aerial vehicle (UAV) airborne antenna as described in claim 1, characterized in that, The comparison between the predicted antenna pointing deviation angle sequence and the dynamic risk threshold specifically includes: The dynamic risk threshold at the current time t The calculation method is as follows: ； in, Based on the static risk threshold, for The angular acceleration vector at time t. Represents the L2 norm. This is the integration time window.

7. The method for monitoring and early warning of attitude stability of an unmanned aerial vehicle (UAV) airborne antenna as described in claim 6, characterized in that, The generation of the second early warning signal specifically involves: If within the prediction time window Within, there exists at any time Predicted antenna pointing deviation angle satisfy Then the second warning signal will be generated.

8. The method for monitoring and early warning of attitude stability of an airborne antenna for a UAV as described in claim 1, characterized in that, The step of updating the attitude signal intensity mapping model online based on attitude signal data pairs specifically includes: The newly acquired attitude signal data pairs are used as new evidence. ; The hyperparameter set of the attitude signal intensity mapping model is updated using Bayesian inference. ; The posterior probability density function of the hyperparameters is: ; The hyperparameters are updated by maximizing the posterior probability.

9. The method for monitoring and early warning of attitude stability of an unmanned aerial vehicle (UAV) airborne antenna as described in claim 1, characterized in that, The process of fusing the first and second early warning signals to output the final early warning command specifically includes: Define the first warning signal Second warning signal , where 0 indicates no warning and 1 indicates a warning has been triggered; Final warning level Determined through the following logic: ； when When this occurs, a Level 1 warning command is output to indicate abnormal signal quality; when At that time, a level-two early warning command is output to indicate a risk in the attitude direction; when At that time, a level three early warning command is output to indicate a serious risk to the communication link; The warning instruction includes a warning level, a trigger type identifier, a timestamp of the trigger time, and a snapshot of the current attitude signal data. And the predicted antenna pointing deviation angle value.

10. A UAV airborne antenna attitude stability monitoring and early warning system, used to implement the UAV airborne antenna attitude stability monitoring and early warning method according to any one of claims 1-9, characterized in that, include: The data acquisition module is used to acquire and synchronize the real-time body attitude data of the UAV with the communication signal strength data of the airborne antenna to form a time-aligned attitude signal data pair. The first early warning module is used to obtain the expected signal strength and confidence interval corresponding to the current attitude based on the body attitude data and the preset attitude signal strength mapping model, compare the expected signal strength with the communication signal strength data, and generate the first early warning signal. The second early warning module is used to predict the predicted attitude trajectory within a certain time window based on the body attitude data and the UAV kinematic model, and calculate the predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates. The predicted antenna pointing deviation angle sequence is compared with the dynamic risk threshold to generate a second early warning signal. The online update module is used to update the attitude signal intensity mapping model online based on the attitude signal data pairs; The early warning output module is used to merge the first early warning signal and the second early warning signal to output the final early warning command.

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