Communication link dynamic modeling method and device based on aircraft motion parameters in high dynamic scene

Abstract

The invention discloses a communication link dynamic modeling method and device based on aircraft motion parameters in a high dynamic scene, and the method comprises the steps: S1, collecting the motion parameters of an aircraft in real time, and carrying out the real-time collection of the time-varying characteristics of a communication signal between the aircraft and the ground; s2, the collected motion parameters are aligned and integrated according to a unified timestamp, and noise interference of communication signals is removed; s3, constructing a link performance evaluation model according to the aligned and integrated motion parameters and the communication signals without noise interference, wherein the link performance evaluation model is used for outputting the dynamic performance change of the communication link; and S4, comparing a link performance prediction result output by the model with data fed back in an actual communication link, and updating parameters in the model in real time. The whole communication system has higher adaptability and robustness when facing complex and changeable high dynamic scenes, and flight potential safety hazards caused by communication problems are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication link modeling, and in particular to a communication link dynamic modeling method and device based on aircraft motion parameters in a high dynamic scenario. BACKGROUND

[0002] In the scenarios of aviation communication and unmanned aerial vehicle cluster communication, the aircraft is often in a high-speed flight and frequent maneuvering state. Such high dynamic motion makes the communication link present complex and dramatic changes, for example, the signal will produce a dramatic Doppler shift. The existing communication link modeling methods are mostly based on the assumption of relatively stable communication environment, without fully considering various time-varying factors brought by high dynamic motion of the aircraft. This makes it difficult for the existing model to accurately reflect the real-time performance changes of the communication link in the actual high dynamic scenario, and further unable to provide accurate and effective basis for link optimization and resource allocation. In actual aviation communication and unmanned aerial vehicle cluster communication applications, due to inaccurate link performance, communication interruption, low transmission efficiency, unreasonable resource allocation and other problems are prone to occur, which seriously affects the flight safety and the effect of task execution.

[0003] Therefore, it is urgent to provide a communication link dynamic modeling method and device based on aircraft motion parameters in a high dynamic scenario. SUMMARY

[0004] In order to solve the above problems, the technical scheme of the present application provides a communication link dynamic modeling method and device based on aircraft motion parameters in a high dynamic scenario, which can reduce the flight safety hazards caused by communication problems.

[0005] According to the first aspect embodiment of the technical scheme of the present application, a communication link dynamic modeling method based on aircraft motion parameters in a high dynamic scenario is provided, comprising:

[0006] S1, real-time collection of motion parameters of the aircraft, real-time collection of time-varying characteristics of communication signals between the aircraft and the ground;

[0007] S2, aligning and integrating the collected motion parameters according to a unified timestamp, and removing noise interference of the communication signals;

[0008] S3, constructing a link performance evaluation model according to the aligned and integrated motion parameters and the communication signals with noise interference removed, the link performance evaluation model being used to output dynamic performance changes of the communication link;

[0009] S4, comparing the link performance prediction results output by the model with the data fed back in the actual communication link, and updating the parameters in the model in real time.

[0010] In the above scheme, in step S1, the motion parameters of the aircraft include: motion trajectory information, speed information and acceleration information.

[0011] In the above scheme, in step S1, the time-varying characteristics of the collected communication signals include: signal strength, frequency and phase.

[0012] In the above scheme, step S2 includes:

[0013] S21, align and integrate the collected motion parameters according to a unified timestamp, and construct a space-time data sequence of the motion state of the aircraft;

[0014] S22, filtering and normalizing the communication signals.

[0015] In the above scheme, step S3 includes:

[0016] S31, establishing a three-dimensional rectangular coordinate system with the communication receiving end as the origin, and calculating the velocity vector, position vector and relative distance of the aircraft according to the position coordinates of the aircraft at time t;

[0017] S32, based on the Doppler effect formula and the Shannon formula, establishing a link performance evaluation model between the signal parameters and the velocity vector, position vector and relative distance of the aircraft;

[0018] S33, unifying the velocity vector, position vector and signal parameters of the aircraft into a state vector, and constructing an observation equation through the state vector;

[0019] S34, updating the observation equation through Kalman filtering iteration to realize real-time calibration of the model parameters.

[0020] In the above scheme, the dynamic performance change of the communication link includes: the real-time bandwidth, error rate and signal fading curve of the communication link over time.

[0021] In the above scheme, in step S4, the error correction algorithm is used to update the parameters in the model in real time.

[0022] According to the second aspect of the technical scheme, a communication link dynamic modeling device based on aircraft motion parameters in a high dynamic scene is provided, which includes the high dynamic scene communication link dynamic modeling method based on aircraft motion parameters in any of the above schemes, and further includes:

[0023] The acquisition module is used for real-time acquisition of the motion parameters of the aircraft, and real-time acquisition of the time-varying characteristics of the communication signals between the aircraft and the ground;

[0024] The integration module is used for aligning and integrating the collected motion parameters according to a unified timestamp, and removing the noise interference of the communication signals.

[0025] a construction module, configured to construct a link performance evaluation model according to the aligned integrated motion parameters and the communication signals with noise interference removed, the link performance evaluation model being configured to output dynamic performance changes of the communication link;

[0026] an updating module, configured to compare the link performance prediction result output by the model with the data fed back in the actual communication link, and update parameters in the model in real time.

[0027] According to a third aspect of the technical scheme of the present application, a UAV is provided, which is applied to a UAV cluster communication scenario, and is configured to implement the method according to any one of the above solutions.

[0028] According to a fourth aspect of the technical scheme of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program is configured to implement the method according to any one of the above solutions when executed by a processor.

[0029] The present application has the following beneficial effects:

[0030] The communication link dynamic modeling method and device based on aircraft motion parameters in a high dynamic scene disclosed in the present application can more accurately evaluate the real-time performance of the link by precisely introducing multi-dimensional dynamic parameters such as the trajectory, speed and acceleration of the aircraft, and combining the time-varying characteristics of the communication signals, and can provide a reliable basis for subsequent optimization decisions, compared with the existing modeling methods. The model constructed based on the present application can reflect the dynamic performance changes of the communication link in real time, and can be used in the management and control system of aviation communication or UAV cluster communication, can dynamically optimize the parameter configuration (such as the adjustment of modulation mode and coding mode) of the communication link according to the real-time changes of the link state, reasonably allocate communication resources (such as the allocation of frequency bands and power), effectively improve the stability and transmission efficiency of communication, avoid resource waste, and ensure the smooth progress of the flight mission. The model has a real-time updating and calibration mechanism, can adapt to the complex high dynamic changes of the aircraft in different flight stages and different environments, and can be corrected in time according to the actual communication feedback, so that the entire communication system has stronger adaptability and robustness when facing complex and variable high dynamic scenes, and reduces the flight safety hazards caused by communication problems. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description only represent some of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from the structures shown in the drawings without creative labor.

[0032] Figure 1 The flow chart of the communication link dynamic modeling method based on aircraft motion parameters in a high dynamic scene provided by the present application;

[0033] Figure 2 The architecture diagram of the communication link dynamic modeling method based on aircraft motion parameters in a high dynamic scene provided by the present application.

[0034] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0035] The exemplary embodiments will be described in detail herein with reference to the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present disclosure. Instead, they are merely examples of apparatuses and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0036] The terms "first", "second", and the like in the description and claims of the present disclosure are used for distinguishing between similar objects and do not necessarily have a particular order or sequence. It should be understood that data used in this way can be interchanged, where appropriate, so that the embodiments of the present disclosure described herein can be carried out in other than the order shown or described herein.

[0037] In addition, the terms "comprising" and "having" and any variations thereof are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or apparatus that includes a list of steps or units not necessarily limited to those clearly listed, but can include other steps or units not clearly listed or inherent to such processes, methods, products or apparatuses.

[0038] Multiple, including two or more.

[0039] And / or, it should be understood that the term "and / or" used in the present disclosure is only a description of the association relationship of the associated objects, which means that there can be three relationships. For example, A and / or B can represent three cases: A exists alone, A and B exist together, and B exists alone.

[0040] As Figure 1 and Figure 2 shown, one embodiment of the technical scheme of the present application provides a communication link dynamic modeling method based on aircraft motion parameters in a high dynamic scene, comprising:

[0041] S1, real-time collection of motion parameters of the aircraft, real-time collection of time-varying characteristics of the communication signal between the aircraft and the ground;

[0042] Among them, the motion parameters of the aircraft include: motion trajectory information, speed information and acceleration information. The collected communication signal time-varying characteristics include: signal strength, frequency and phase.

[0043] The specific collection method of the motion parameters of the aircraft includes: acquiring the coordinate change sequence with time through the positioning system to collect the motion trajectory information of the aircraft, acquiring the speed data at different times through the pitot tube, inertial measurement unit, etc., and relying on the inertial measurement unit to acquire the acceleration data.

[0044] S2, align and integrate the collected motion parameters according to a unified timestamp, and remove the noise interference of the communication signal;

[0045] Specifically, step S2 includes:

[0046] S21, align and integrate the collected motion parameters according to a unified timestamp, and construct a space-time data sequence of the motion state of the aircraft;

[0047] S22, filtering and normalization processing of the communication signal.

[0048] In step S22, the specific processing is: for the time-varying characteristic data of the communication signal, filtering, normalization preprocessing operation is performed to remove noise interference and improve data quality, so that it can be better associated with the aircraft motion parameters for correlation analysis.

[0049] S3, constructing a link performance evaluation model according to the aligned and integrated motion parameters and the communication signal with noise interference removed, the link performance evaluation model being used to output the dynamic performance change of the communication link;

[0050] Step S3 includes:

[0051] S31, establishing a three-dimensional rectangular coordinate system with the communication receiving end as the origin, calculating the speed vector, position vector and relative distance of the aircraft according to the position coordinates of the aircraft at time t;

[0052] S32, establishing a link performance evaluation model between the signal parameters and the speed vector, position vector and relative distance of the aircraft based on the Doppler effect formula and the Shannon formula;

[0053] S33, unify the speed vector, position vector and signal parameter of the aircraft into a state vector, and construct an observation equation through the state vector;

[0054] S34, update the observation equation through Kalman filter iteration to realize real-time calibration of the model parameter.

[0055] Based on the signal propagation theory, Doppler effect principle and related basic theoretical knowledge of communication link, mathematical relationship expressions between the dynamic performance of communication link and the motion parameters of aircraft, time-varying characteristics of communication signal are established. Then, the dynamic change of relative radial velocity is determined combined with the real-time speed change and direction change of aircraft trajectory, and the characteristics of communication signal frequency, phase and other time-varying characteristics are integrated to construct a comprehensive and real-time updated link performance evaluation model.

[0056] The specific construction process is as follows:

[0057] Core parameter association and coordinate system definition:

[0058] A three-dimensional rectangular coordinate system is established with the communication receiving end as the origin O, wherein: the X-axis points to the east direction, the Y-axis points to the north direction, and the Z-axis is perpendicular to the ground and upward. The position coordinates of the aircraft at time t are (x(t), y(t), z(t)), and the receiving end position is fixed as (0, 0, 0).

[0059] The radial velocity v rad (t) of the aircraft relative to the receiving end is the projection of its speed vector in the direction of the line connecting the two, and the formula is:

[0060]

[0061] Wherein:

[0062] v(t)=(v x (t),v y (t),v z (t)) is the speed vector of the aircraft at time t (obtained by inertial measurement unit);

[0063] r(t)=(x(t),y(t),z(t)) is the position vector of the aircraft, is the relative distance.

[0064] According to the Doppler effect formula, combined with the real-time change of radial velocity, the frequency shift f d (t) can be expressed as:

[0065]

[0066] Wherein: f0 is the original frequency of the communication signal, c is the speed of light (constant, taking 3×108 m / s).

[0067] When the aircraft is accelerating (e.g. diving or climbing), v rad (t) varies with acceleration a(t), causing f(t) to exhibit non-linear fluctuations.

[0068] Mathematical description of the time-varying characteristics of the communication signal:

[0069] The change of signal phase φ(t) with time reflects the cumulative effects of path delay and Doppler shift, expressed as:

[0070] φ(t) = φ0+ 2π∫0(f0+ f d (τ))dτ + φ noise (t)

[0071] where: φ0is the initial phase, φ noise (t) is random phase noise (caused by multipath effects, etc., suppressed by Kalman filtering).

[0072] The instantaneous frequency f(t) is the derivative of the phase with respect to time, i.e.:

[0073]

[0074] In actual modeling, the changes in f(t) are tracked in real time by a phase-locked loop (PLL), extracting a stable carrier frequency component.

[0075] Comprehensive modeling of link performance indicators:

[0076] Based on the Shannon formula, considering the time-varying signal-to-noise ratio (SNR), the formula for the bandwidth B(t) is:

[0077]

[0078] where: W is the channel bandwidth (a fixed value), P recv (t) is the received signal power, inversely proportional to the square of the distance: is the transmitted power, G t ·G r is the transmit and receive antenna gain, λ is the signal wavelength, and N0is the noise power spectral density.

[0079] For binary phase shift keying (BPSK) modulation, the bit error rate formula is:

[0080]

[0081] where: E b (t) is the bit energy, (Rb is the data rate), and Q(·) is the Gaussian tail probability function.

[0082] E b (t) directly, and the aircraft maneuver will affect the bit error rate through both distance and speed factors.

[0083] Real-time updating mechanism of dynamic model:

[0084] Unify the aircraft motion parameters and signal parameters into a state vector:

[0085] x(t) = [x(t), y(t), z(t), v x (t), v y (t), v z (t), f(t), φ(t)] T

[0086] The state transition equation is:

[0087] x(t+1) = F·x(t) + w(t)

[0088] Where: F is the state transition matrix (contains the update of acceleration to speed), w(t) is the process noise (unmodeled dynamics such as air flow disturbance).

[0089] Through the observation vector z(t) = [P recv (t), f meas (t), φ meas (t)] T (measured signal power, frequency, phase), the observation equation is constructed:

[0090] z(t) = H·x(t) + v(t)

[0091] Where H is the observation matrix, v(t) is the observation noise. Through the Kalman filter iteration to update the state estimation (t), realize the real-time calibration of model parameters.

[0092] S4, compare the link performance prediction results output by the model with the feedback data in the actual communication link, and update the parameters in the model in real time.

[0093] Because the motion state of the aircraft and the communication environment are changing all the time, a feedback correction link is set. By comparing the link performance prediction results output by the model with the monitoring data (such as the actual received signal quality indicators) fed back from the actual communication link, and using error correction algorithms (such as the commonly used error correction algorithms such as least squares method) to adjust and update the parameters in the model in real time, it is ensured that the model can continuously and accurately reflect the dynamic changes of the communication link.

[0094] Specifically as follows:

[0095] Let the model predicted link performance indicator be (like bandwidth, error rate), and the actual monitored value be y(t), then the real-time error is defined as:

[0096]

[0097] The cumulative error can be represented by the mean square error (MSE) within a sliding window:

[0098]

[0099] where N is the window length (e.g., take N = 100 corresponding to 10 seconds of monitoring data).

[0100] Express the link model in a parameterized form:

[0101]

[0102] where: θ = [θ1, θ2, …, θ m ] T are the parameters to be optimized (such as Doppler shift coefficient, signal attenuation factor), u(t) = [x(t), v(t), a(t), f0(t)] T are the input motion parameters and signal parameters.

[0103] Based on the least squares method, define the cost function as the sum of the squares of the cumulative error:

[0104]

[0105] The goal is to find θ * that minimizes J(θ).

[0106] Directly calculate the optimal solution by matrix inversion:

[0107] θ * = (U T U) -1 U T y

[0108] where U is the input data matrix and y is the observation vector.

[0109] Use the recursive least squares method (RLS) to avoid repeated matrix operations:

[0110] K(t) = P(t-1)u(t)(u(t) T P(t-1)u(t) + λ)-1

[0111] θ(t) = θ(t-1) + K(t)(y(t) - u(t) T θ(t-1))

[0112]

[0113] wherein:

[0114] K(t) is the gain matrix, P(t) is the covariance matrix, λ ∈ (0, 1] is the forgetting factor used to suppress the influence of old data, adapt to the time-varying environment.

[0115] When the model has a state space describable dynamic characteristic (such as the aircraft motion equation), the extended Kalman filter (EKF) is used to estimate the state and parameters simultaneously:

[0116] 1. State vector: x(t) = [position, velocity, acceleration, parameter θ] T ;

[0117] 2. State transition equation: fusion of aircraft dynamics model (such as uniform acceleration motion) and parameter evolution model (such as random walk);

[0118] 3. Observation equation: associate the link performance index y(t) with the state vector, update x(t) through EKF iteration.

[0119] Assign dynamic weights to different error sources:

[0120]

[0121] where the weight w(τ) is dynamically adjusted according to the data reliability:

[0122] When the sensor is abnormal (such as GPS signal loss), reduce the weight of the data at the corresponding time;

[0123] When the environment changes suddenly (such as thunderstorm weather), increase the weight of recent data (forgetting factor λ decreases).

[0124] Through the above refinement, the error correction mechanism can realize dynamic tracking of model parameters, ensuring that the model maintains high precision in the scenarios of aircraft maneuvering, environmental changes, etc., providing reliable support for real-time optimization of communication links.

[0125] The present application has the following advantages:

[0126] 1. Improve the accuracy of link performance evaluation

[0127] By accurately introducing multi-dimensional dynamic parameters such as aircraft motion trajectory, velocity, acceleration, etc., and combining with the time-varying characteristics of communication signals, the model constructed can better fit the changes of communication links in actual high dynamic scenarios, compared with existing modeling methods, it can more accurately evaluate the real-time performance of the link, providing a reliable foundation for subsequent optimization decisions.

[0128] 2. Optimize link and resource allocation

[0129] Based on the model constructed according to the application, which reflects the dynamic performance change of the communication link in real time, the management and control system of the aviation communication or the unmanned aerial vehicle cluster communication can dynamically optimize the parameter configuration (such as the adjustment of the modulation mode and the encoding mode) of the communication link according to the real-time change of the link state, reasonably allocate the communication resources (such as the allocation of the frequency band and the power), effectively improve the stability and the transmission efficiency of the communication, avoid the waste of resources, and ensure the smooth progress of the flight task.

[0130] 3. Enhance the adaptability and robustness of the system

[0131] The model has a real-time updating and calibration mechanism, can adapt to the complex high dynamic change of the aircraft in different flight stages and different environments, and can be corrected in time according to the actual communication feedback, so that the whole communication system has stronger adaptability and robustness when facing complex and variable high dynamic scenes, and reduces the flight safety hidden danger caused by the communication problem.

[0132] According to the second aspect of the technical scheme of the application, a communication link dynamic modeling device based on the motion parameters of the aircraft in a high dynamic scene is provided, which comprises the communication link dynamic modeling method based on the motion parameters of the aircraft in a high dynamic scene in any of the above schemes, and further comprises:

[0133] The acquisition module is configured to acquire the motion parameters of the aircraft in real time, and acquire the time-varying characteristics of the communication signals between the aircraft and the ground in real time.

[0134] The integration module is configured to align and integrate the acquired motion parameters according to a unified timestamp, and remove the noise interference of the communication signals.

[0135] The construction module is configured to construct a link performance evaluation model according to the aligned and integrated motion parameters and the communication signals from which the noise interference is removed, and the link performance evaluation model is configured to output the dynamic performance change of the communication link.

[0136] The updating module is configured to compare the link performance prediction result output by the model with the data fed back in the actual communication link, and update the parameters in the model in real time.

[0137] According to the third aspect of the technical scheme of the application, an unmanned aerial vehicle is provided, which is applied to an unmanned aerial vehicle cluster communication scene, and is configured to implement the method in any of the above schemes.

[0138] According to the fourth aspect of the technical scheme of the application, a computer readable storage medium is provided, which stores a computer program, and the computer program is configured to implement the method in any of the above schemes when executed by a processor.

[0139] Embodiment 1

[0140] Taking the aviation communication scenario as an example, in the scenario of a civil aviation passenger plane flying across regions, the passenger plane is equipped with a perfect sensor system and a communication monitoring device.

[0141] 1. Data acquisition stage

[0142] The position coordinates of the plane are recorded every certain time interval (such as 1 second) by using the satellite positioning system of the plane, so as to determine the flight trajectory; the flight speed and acceleration information of the plane are obtained in real time through the air speed tube and the inertial measurement unit. At the same time, the communication monitoring device monitors the signals of the communication between the plane and the ground base station in real time, and collects time-varying parameters such as frequency and intensity of the signals.

[0143] 2. Parameter integration and preprocessing

[0144] The data of different sources obtained above are aligned and integrated according to the time stamp, for example, the position, speed and acceleration of the plane at the same time are arranged into a data record together with the corresponding communication signal parameters. The parameters of the communication signal are filtered to remove high-frequency noise interference, and then normalized to be in a suitable numerical range for subsequent calculation and analysis.

[0145] 3. Dynamic model construction and application

[0146] According to the model construction method of the application, the communication link dynamic performance model in the flight process of the plane is constructed by combining the flight trajectory, speed, acceleration of the plane and the real-time characteristics of the communication signal. In the flight process, the aviation communication management and control center on the ground can output the performance indicators such as link bandwidth and bit error rate in real time according to the model, and timely adjust the parameters such as transmission power and modulation and coding mode of the ground base station, so as to guarantee the stability and efficiency of the communication between the plane and the ground. For example, when the model predicts that the link bandwidth B(t) decreases due to the maneuvering of the plane, the control center timely switches to a more efficient coding mode to maintain the communication quality:

[0147] When B(t) ∈ [15, 20] Mbps (high SNR): use uncoded or low redundancy coding (such as convolution code 1 / 2) to maximize transmission efficiency;

[0148] When B(t) ∈ [10, 15] Mbps (medium SNR): switch to Turbo code 1 / 3 to balance bandwidth and error correction;

[0149] When B(t) < 10 Mbps (low SNR): enable LDPC code 1 / 4 to sacrifice code rate for reliability.

[0150] If the bandwidth is still insufficient after switching to LDPC code, the transmission power of the base station is simultaneously increased (such as by 3 dB);

[0151] Modulation adjustment: reduce the modulation order (e.g. from 16QAM to QPSK), reduce the number of bits per symbol, in exchange for higher demodulation reliability.

[0152] 4. Model updating and calibration

[0153] The ground base station monitors the actual quality of the received aircraft signals in real time, compares it with the model-predicted link performance indicators, and if a deviation is found, uses error correction algorithms such as least squares to fine-tune and update the relevant parameters in the model (such as the speed weight in Doppler shift calculation and other parameters), ensuring that the model accurately reflects the actual dynamic changes of the communication link.

[0154] Embodiment 2

[0155] Taking the UAV cluster communication scenario as an example, in a scenario where multiple UAVs perform formation inspection tasks, each UAV is equipped with a miniaturized inertial measurement unit, GPS positioning module, and communication signal acquisition module.

[0156] 1. Data collection

[0157] Each UAV obtains its position coordinates in real time through the GPS positioning module to determine its flight trajectory in the cluster, the inertial measurement unit provides speed and acceleration data, and the communication signal acquisition module collects the communication signals between the UAVs and between the UAVs and the ground control station, records the time-varying characteristic parameters such as phase and frequency change of the signals, and the collection frequency can be set to once every 0.5 seconds.

[0158] 2. Parameter integration and preprocessing

[0159] The data collected by each UAV is integrated and aligned in time sequence in its own on-board processing unit to construct the corresponding motion parameter and communication signal parameter data sequence of each UAV, and the communication signal parameters are preprocessed to remove noise caused by environmental electromagnetic interference and other factors, ensuring data quality.

[0160] 3. Dynamic model construction and application

[0161] With the model construction algorithm of the application, each unmanned aerial vehicle constructs a corresponding communication link dynamic model according to the motion trajectory, speed, acceleration and other information of itself and surrounding unmanned aerial vehicles, in combination with the time-varying characteristics of communication signals, and the ground control station can also construct a comprehensive communication link model of the entire unmanned aerial vehicle cluster according to the information fed back by each unmanned aerial vehicle in the cluster. During the task execution process, the ground control station can dynamically allocate communication frequency bands to different unmanned aerial vehicles according to the link performance change output by the model, avoid communication interference between unmanned aerial vehicles, and optimize the overall efficiency of cluster communication. For example, when it is found that part of the unmanned aerial vehicles have poor link state due to maneuvering, idle frequency band resources are preferentially allocated to them to ensure the stability of formation communication.

[0162] 4. Model updating and calibration

[0163] The actual monitoring data of the real-time interactive communication link between each unmanned aerial vehicle and between the unmanned aerial vehicle and the ground control station are compared and analyzed with the model prediction results, and an error correction algorithm is respectively run in the on-board processing unit of the unmanned aerial vehicle and the server of the ground control station to update the model parameters in real time, so that the model can accurately track the dynamic changes of the communication link of the unmanned aerial vehicle cluster in the complex maneuvering flight process.

[0164] It should be noted that in this document, the terms "comprise", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or other elements inherent to such a process, method, article or device. Without more limitations, the element defined by the statement "comprises a" does not exclude the presence of another identical element in the process, method, article or device that includes the element.

[0165] The above-mentioned embodiment numbers of the application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0166] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned implementation methods can be realized by means of software and necessary general hardware platforms, of course, they can also be realized by hardware, but in many cases the former is a better implementation method. Based on such understanding, the technical solutions of the application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a plurality of instructions for making a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) execute the methods described in each embodiment of the application.

[0167] The embodiments of the present application are described above with reference to the accompanying drawings, but the present application is not limited to the above-described specific embodiments, and the above-described specific embodiments are merely illustrative, but not restrictive, and a person of ordinary skill in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, and these all belong to the protection of the present application.

Claims

1. A method for dynamic modeling of communication links based on aircraft motion parameters in highly dynamic scenarios, characterized in that: include: S1. Real-time acquisition of aircraft motion parameters and time-varying characteristics of communication signals between the aircraft and the ground; S2. Align and integrate the collected motion parameters according to a unified timestamp to remove noise interference from the communication signal; S3. Constructing a link performance evaluation model based on the aligned and integrated motion parameters and the communication signal with noise interference removed. The link performance evaluation model is used to output dynamic performance changes of the communication link; S4. Compare the link performance prediction results output by the model with the data fed back from the actual communication link, and update the parameters in the model in real time.

2. The method for dynamic modeling of communication links based on aircraft motion parameters in high dynamic scenarios according to claim 1, characterized in that: In step S1 , the motion parameters of the aircraft include: motion trajectory information, speed information, and acceleration information.

3. The method for dynamic modeling of communication links based on aircraft motion parameters in high dynamic scenarios according to claim 1, characterized in that: In step S1, the collected time-varying characteristics of the communication signal include: signal strength, frequency and phase.

4. The method for dynamic modeling of communication links based on aircraft motion parameters in high dynamic scenarios according to claim 1, characterized in that: Step S2 includes: S21. Align and integrate the collected motion parameters according to a unified timestamp to construct a spatiotemporal data sequence of the aircraft's motion state; S22: Filter and normalize the communication signal.

5. The method for dynamic modeling of communication links based on aircraft motion parameters in high dynamic scenarios according to claim 1, characterized in that: Step S3 includes: S31. Establish a three-dimensional rectangular coordinate system with the communication receiving end as the origin, and calculate the velocity vector, position vector, and relative distance of the aircraft based on the position coordinates of the aircraft at time t; S32. Based on the Doppler effect formula and the Shannon formula, a link performance evaluation model is established between the signal parameters and the velocity vector, position vector, and relative distance of the aircraft; S33, unifying the velocity vector, position vector, and signal parameters of the aircraft into a state vector, and constructing an observation equation through the state vector; S34. Iteratively update the observation equation through Kalman filtering to achieve real-time calibration of model parameters.

6. The method for dynamic modeling of communication links based on aircraft motion parameters in high dynamic scenarios according to claim 1, characterized in that: The dynamic performance changes of the communication link include: the real-time bandwidth, bit error rate and signal attenuation of the communication link over time.

7. The method for dynamic modeling of a communication link based on aircraft motion parameters in a high dynamic scenario according to claim 1, characterized in that: In step S4, the parameters in the model are adjusted and updated in real time using an error correction algorithm.

8. A communication link dynamic modeling device based on aircraft motion parameters in high dynamic scenarios, characterized by: The method for dynamic modeling of a communication link based on aircraft motion parameters in a high-dynamic scenario according to any one of claims 1 to 7 further includes: The acquisition module is used to collect the motion parameters of the aircraft in real time and the time-varying characteristics of the communication signal between the aircraft and the ground in real time; The integration module is used to align and integrate the collected motion parameters according to a unified timestamp to remove noise interference from the communication signal; A construction module is used to construct a link performance evaluation model based on the aligned and integrated motion parameters and the communication signal with noise interference removed, and the link performance evaluation model is used to output dynamic performance changes of the communication link; The update module is used to compare the link performance prediction results output by the model with the data fed back from the actual communication link and update the parameters in the model in real time.

9. A drone, characterized in that: The drone is applied to a drone cluster communication scenario, and is used to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The device stores a computer program, which implements the method according to any one of claims 1 to 7 when executed by a processor.

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