CN111740796A

CN111740796A - Unmanned aerial vehicle data link electromagnetic interference situation prediction method and device

Info

Publication number: CN111740796A
Application number: CN202010506804.4A
Authority: CN
Inventors: 陈亚洲; 张冬晓; 赵敏; 程二威; 王玉明; 马立云
Original assignee: Army Engineering University of PLA
Current assignee: Army Engineering University of PLA
Priority date: 2020-06-05
Filing date: 2020-06-05
Publication date: 2020-10-02
Anticipated expiration: 2040-06-05
Also published as: CN111740796B

Abstract

The invention is suitable for the technical field of unmanned aerial vehicle communication, and provides a method and a device for predicting the electromagnetic interference situation of an unmanned aerial vehicle data link, wherein the method comprises the following steps: acquiring electromagnetic parameters and environmental interference data of the unmanned aerial vehicle; inputting the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold; and determining the electromagnetic interference situation of the unmanned aerial vehicle data chain according to the environmental interference data and the electromagnetic interference effect threshold value. This application establishes the relevant of developments with the unmanned aerial vehicle electromagnetic parameter that obtains electromagnetic interference effect threshold value and detection and environmental interference data through gaussian process regression prediction model, and prediction unmanned aerial vehicle that can be dynamic electromagnetic interference effect threshold value under different operating condition improves unmanned aerial vehicle electromagnetic interference situation and judges the accuracy to improve unmanned aerial vehicle's initiative interference killing feature.

Description

Unmanned aerial vehicle data link electromagnetic interference situation prediction method and device

Technical Field

The invention belongs to the technical field of unmanned aerial vehicle communication, and particularly relates to a method and a device for predicting the electromagnetic interference situation of an unmanned aerial vehicle data link.

Background

The unmanned aerial vehicle is an unmanned aerial vehicle controlled by a radio remote control device or a preset program of the unmanned aerial vehicle, the unmanned aerial vehicle depends on an information link seriously, and the safety and reliability of unmanned aerial vehicle equipment in a complex electromagnetic interference environment are a big problem in the field of unmanned aerial vehicle communication. The anti electromagnetic interference ability of current unmanned aerial vehicle equipment is relatively weak, and outstanding embodiment receives external electromagnetic radiation interference easily at uplink, leads to the ground-to-air communication to break off, seriously threatens unmanned aerial vehicle's flight safety.

In the prior art, the current electromagnetic interference situation of the information link of the unmanned aerial vehicle is usually judged by adopting an electromagnetic interference effect threshold value obtained through experiments, however, in the actual flight process of the unmanned aerial vehicle, the state of a data link continuously and dynamically changes, the power of a working signal received by an airborne antenna continuously changes, and the frequency of an external interference signal randomly appears. The problem that the unmanned aerial vehicle has low anti-jamming capability under the condition of low judgment accuracy of the electromagnetic interference situation of the unmanned aerial vehicle is caused.

Disclosure of Invention

In view of this, the embodiment of the invention provides a method and a device for predicting an electromagnetic interference situation of an unmanned aerial vehicle data link, so as to solve the problem that the accuracy of judging the electromagnetic interference situation of the unmanned aerial vehicle is low in the prior art.

The first aspect of the embodiment of the invention provides a method for predicting the electromagnetic interference situation of an unmanned aerial vehicle data link, which comprises the following steps:

acquiring electromagnetic parameters and environmental interference data of the unmanned aerial vehicle;

inputting the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold;

and determining the electromagnetic interference situation of the unmanned aerial vehicle data chain according to the environmental interference data and the electromagnetic interference effect threshold value.

A second aspect of the embodiments of the present invention provides an apparatus for predicting an electromagnetic interference situation of an unmanned aerial vehicle data link, including:

the electromagnetic data acquisition module is used for acquiring electromagnetic parameters and environmental interference data of the unmanned aerial vehicle;

the threshold value obtaining module is used for inputting the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold value;

and the electromagnetic interference situation determination module is used for determining the electromagnetic interference situation of the unmanned aerial vehicle data chain according to the environmental interference data and the electromagnetic interference effect threshold.

A third aspect of the embodiments of the present invention provides an adaptive prediction terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for predicting the electromagnetic interference situation of the data link of the unmanned aerial vehicle as described above when executing the computer program.

A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the method for predicting an electromagnetic interference situation of a data link of an unmanned aerial vehicle as described above are implemented.

Compared with the prior art, the embodiment of the invention has the following beneficial effects: the method comprises the steps of firstly, acquiring electromagnetic parameters and environmental interference data of the unmanned aerial vehicle; then inputting the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold; and finally, determining the electromagnetic interference situation of the unmanned aerial vehicle data chain according to the environmental interference data and the electromagnetic interference effect threshold value. This application establishes the relevant of developments with the unmanned aerial vehicle electromagnetic parameter that obtains electromagnetic interference effect threshold value and detection and environmental interference data through gaussian process regression prediction model, and prediction unmanned aerial vehicle that can be dynamic electromagnetic interference effect threshold value under different operating condition improves unmanned aerial vehicle electromagnetic interference situation and judges the accuracy to improve unmanned aerial vehicle's initiative interference killing feature.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.

Fig. 1 is a schematic structural diagram of an unmanned aerial vehicle data link system provided in an embodiment of the present invention.

Fig. 2 is a schematic flowchart of a method for predicting an electromagnetic interference situation of an unmanned aerial vehicle data link according to an embodiment of the present invention;

fig. 3 is a schematic flow chart of another implementation of the method for predicting the electromagnetic interference situation of the data link of the unmanned aerial vehicle according to the embodiment of the present invention;

fig. 4 is a schematic flow chart of S102 in fig. 2 according to an embodiment of the present invention;

fig. 5 is a schematic diagram of a relationship curve between input data and output data when an error occurs in a data chain of an unmanned aerial vehicle according to an embodiment of the present invention;

fig. 6 is a schematic diagram of a relationship curve between input data and output data when an unmanned aerial vehicle data link is unlocked according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of a Gaussian process regression training error of an EMI effect threshold when an error occurs in a data chain according to an embodiment of the present invention;

FIG. 8 is a schematic diagram of a Gaussian process regression training error for an EMI effect threshold when a data link is unlocked according to an embodiment of the present invention;

fig. 9 is a schematic structural diagram of an apparatus for predicting an electromagnetic interference situation of an unmanned aerial vehicle data link according to an embodiment of the present invention.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

In order to explain the technical means of the present invention, the following description will be given by way of specific examples.

As shown in fig. 1, fig. 1 shows a structure of the data link system of the unmanned aerial vehicle provided by the present embodiment, including a ground subsystem and an airborne subsystem 92; the ground subsystem comprises a ground terminal 911, a first duplexer 912 and a first antenna 913, and the airborne subsystem 92 comprises a second duplexer 921, an airborne data link 922, electromagnetic interference environment monitoring hardware 923, an adaptive prediction terminal 924 and a second antenna 925.

In this embodiment, the ground subsystem is used for drone ground remote control, drone electromagnetic environment monitoring display, and telemetry display, and the ground subsystem communicates with the onboard subsystem 92 through the first duplexer 912 and the second duplexer 921.

Specifically, the airborne data link 922 is an existing airborne data link, and includes an airborne transmitting end, an airborne receiving end, and a data terminal; the unmanned aerial vehicle data link electromagnetic interference situation prediction method provided by the embodiment is applied to the adaptive prediction terminal 924. Self-adaptation prediction terminal 924 is connected with unmanned aerial vehicle flight control system for send the anti-interference response measure that self generated to unmanned aerial vehicle flight control system.

Further, the electromagnetic interference environment monitoring hardware 923 is configured to obtain electromagnetic parameters and environmental interference data of the drone. Electromagnetic interference environment monitoring hardware 923 includes the merit and divides ware, compensating circuit, monitoring platform and memory, and wherein the merit divides the ware to be connected with compensating circuit, and compensating circuit is connected with monitoring platform, and monitoring platform is connected with the memory, and the signal that the duplexer sent is received to the merit to send the airborne receiving terminal to monitoring platform and airborne data link 922 through compensating circuit, the signal storage that monitoring platform will acquire to the memory.

As shown in fig. 2, fig. 2 shows a flow of the unmanned aerial vehicle data link electromagnetic interference situation prediction method provided by the embodiment of the present invention, and the process thereof is detailed as follows:

s101: and acquiring electromagnetic parameters and environmental interference data of the unmanned aerial vehicle.

The main flow body of this embodiment is adaptive prediction terminal 924 of airborne data link, and adaptive prediction terminal 924 acquires the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle that electromagnetic interference environment monitoring hardware sent at first. The electromagnetic parameters of the drone include the working signal power, the working signal frequency, and the current AGC voltage. The environmental interference data includes electromagnetic interference power and electromagnetic interference frequency.

Specifically, the AGC voltage is a conducted signal attenuation amplitude controlled by an electrically-tuned attenuator inside an automatic gain control circuit in an intermediate frequency amplification unit of the receiver, the circuit structure can control the power of an output intermediate frequency signal to be stable at a fixed value, and the voltage value reflects the strength of a received signal, i.e., the stronger the received signal, the larger the required electrically-tuned attenuation is, and the higher the AGC voltage is.

S102: and inputting the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold value.

In this embodiment, unmanned aerial vehicle flight in-process, the data link state lasts dynamic change, and the working signal power that airborne antenna received constantly changes, and in addition, external interference signal frequency appears at random, so can't obtain the electromagnetic interference effect threshold value that arbitrary potential interference frequency signal caused under all operating condition of data link through the experiment. Therefore, a correlation between the electromagnetic interference effect threshold and the operating signal power and the interfering signal frequency must be found.

In the embodiment, a Gaussian process regression method is adopted for modeling, the relevance between the electromagnetic interference effect threshold value and the working signal power and the interference signal frequency is established, and the method has the characteristics of easiness in implementation, probability significance of nonparametric inference, hyperparametric self-adaptive acquisition and prediction output and the like.

S103: and determining the electromagnetic interference situation of the unmanned aerial vehicle data chain according to the environmental interference data and the electromagnetic interference effect threshold value.

According to the embodiment, the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle are firstly acquired; then inputting the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold; and finally, determining the electromagnetic interference situation of the unmanned aerial vehicle data chain according to the environmental interference data and the electromagnetic interference effect threshold value. This application establishes the relevant of developments with the unmanned aerial vehicle electromagnetic parameter that obtains electromagnetic interference effect threshold value and detection and environmental interference data through gaussian process regression prediction model, and prediction unmanned aerial vehicle that can be dynamic electromagnetic interference effect threshold value under different operating condition improves unmanned aerial vehicle electromagnetic interference situation and judges the accuracy to improve unmanned aerial vehicle's initiative interference killing feature.

In an embodiment of the present invention, as shown in fig. 3, fig. 3 shows another implementation flow of the unmanned aerial vehicle data link electromagnetic interference situation prediction method, and the process thereof is detailed as follows:

s201: acquiring a training sample, wherein the training sample comprises observation data and input data; the observed data includes an interference-to-signal ratio and an AGC voltage variation, and the input data includes electromagnetic parameters and environmental interference data.

In the present embodiment, the interference-to-signal ratio is the ratio of the interference signal power to the operating signal power. The standard AGC voltage is the AGC voltage monitored by the unmanned aerial vehicle under the normal and non-interference condition. The AGC voltage variation comprises AGC voltage variation corresponding to an unlocking effect and AGC voltage variation corresponding to an error code effect, and the AGC voltage variation corresponding to the unlocking effect is a difference value obtained by subtracting a standard AGC voltage from the AGC voltage when the unmanned aerial vehicle data chain is subjected to the unlocking effect; and the AGC voltage variation corresponding to the error code effect is the difference value of the AGC voltage minus the standard AGC voltage when the error code effect occurs to the data chain of the unmanned aerial vehicle.

S202: standardizing the observation data and the input data in the training sample to obtain a standardized training sample;

s203: establishing an initial prediction model based on a Gaussian process regression method;

s204: and inputting the normalized training samples into the initial prediction model, and training the initial prediction model to obtain the Gaussian process regression prediction model.

In this embodiment, two data link states of a working signal, namely a high power-45 dBm state and a low power-80 dBm state, are taken as examples, and are obtained through analysis of an unmanned aerial vehicle dynamic data link electromagnetic interference injection effect test: the operating signal power and the interference frequency affect the data chain electromagnetic interference effect threshold.

Specifically, the application takes 1 frequency channel as an example, typical working signal power { -100, -95, -90, …, -45, -40, -35} dBm and interference frequency offset { -5, -4, -3, -2, -1,0,1,2,3,4,5} MHz are selected, and standard continuous wave interference effect thresholds under different data chain working states, including an interference-to-signal ratio and an AGC voltage variation when the data chain starts to have two effects of error code and lock loss, are obtained by carrying out injection tests. Because the effect of electromagnetic interference on the data link of the unmanned aerial vehicle cannot be intuitively reflected by the interference signal power and the AGC voltage, the interference signal power and the AGC voltage are respectively converted into an interference-signal ratio and an AGC voltage variation. The relationship between the input data (interfering signal frequency, operating signal power) and the output data (interference-to-signal ratio and AGC voltage variation) is determined as shown in fig. 5 and fig. 6.

Fig. 5 shows the relationship between input data (interfering signal frequency, operating signal power) and output data (interference-to-signal ratio and AGC voltage variation) when errors occur in the data chain of the drone. Fig. 5a shows a relationship curve between interference-signal ratios and interference frequency offsets corresponding to different operating signal powers under the error code effect, fig. 5b shows a relationship curve between interference-signal ratios and interference signal powers corresponding to different interference frequency offsets under the error code effect, fig. 5c shows a relationship curve between AGC voltage variation and interference frequency offsets corresponding to different operating signal powers under the error code effect, and fig. 5d shows a relationship curve between AGC voltage variation and interference signal powers corresponding to different interference frequency offsets under the error code effect.

Specifically, the interference frequency offset represents a difference obtained by subtracting the operating signal frequency from the interference signal frequency.

Fig. 6 shows the relationship between input data (interference signal frequency, working signal power) and output data (interference-to-signal ratio and AGC voltage variation) when the data chain of the drone is out-of-lock. Fig. 6a shows a relationship curve between interference-signal ratios and interference frequency offsets corresponding to different operating signal powers under the lock-out effect, fig. 6b shows a relationship curve between interference-signal ratios and interference signal powers corresponding to different interference frequency offsets under the lock-out effect, fig. 6c shows a relationship curve between AGC voltage variation and interference frequency offsets corresponding to different operating signal powers under the lock-out effect, and fig. 6d shows a relationship curve between AGC voltage variation and interference signal powers corresponding to different interference frequency offsets under the lock-out effect.

As can be seen from fig. 5a and 6a, the interference-to-signal ratio of the data chain when the error code and the lock loss effect occur in different working states is in a nonlinear relationship with the interference frequency offset, the interference-to-signal ratio corresponding to the same frequency interference is relatively small, that is, the data chain has relatively weak capability of resisting the same frequency interference, and the interference-to-signal ratio is relatively increased as the interference frequency deviates from the working frequency farther. In fig. 5b and 6b, for different interference frequencies, the interference-to-signal ratio and the working signal power both have a non-linear variation trend when the error code and the lock loss effect occur in the data chain, and the stronger the working signal is, the interference-to-signal ratio is relatively reduced, which is a blocking effect phenomenon caused by the fact that the internal active circuit of the receiver of the unmanned aerial vehicle gradually approaches a saturation state. Similarly, as can be seen from fig. 5c, 5d and 6c, 6d, the AGC voltage variation is non-linearly related to the interference frequency offset and the working signal power when the data chain has bit error and lock loss effects. Therefore, the effect prediction of the nonlinear system electromagnetic environment is difficult to develop from the perspective of a system principle or an effect mechanism by using the traditional deterministic modeling method, but uncertainty modeling can be developed from the perspective of mathematical statistics based on the test data, and a Gaussian process regression prediction model is created.

Specifically, as can be seen from the data chain electromagnetic interference injection effect test, the working signal power P_sAnd interference frequency f_jInterference signal power affecting when bit error and lock loss effect begin to appear in data chain P_j1,P_j2And AGC voltage V_j1,V_j2}. This embodiment compares the interference-to-signal ratio (ISR)₁,ISR₂And amount of change in AGC voltage { V }₁,V₂As the observed value of the training sample, the disturbed variation of the state of the data chain can be observed and measured convenientlyThe degree of conversion. Wherein, ISR₁Interference-to-signal ratio, ISR, in the presence of bit errors₂Indicating the interference-to-signal ratio when the lock is lost. V₁Representing the AGC voltage variation, V, before and after applying interference signal corresponding to error code effect₂Indicating the AGC voltage variation before and after applying the interference signal corresponding to the out-of-lock effect.

In addition, at the frequency f of electromagnetic interference_jAnd operating signal power P_sAnd performing Gaussian process regression modeling as input data of the training sample.

In this embodiment, the dynamic data link electromagnetic interference effect threshold prediction method based on gaussian process regression can be summarized as follows:

(1) sample data is determined. Determining a training sample according to a dynamic data chain electromagnetic interference injection effect test result;

(2) and setting a super-parameter initial value. And determining a prior distribution function by setting a super-parameter initial value by taking a double-exponential covariance function as a kernel function.

Specifically, let y ═ f' (x) ═ f (x) ++, where it is gaussian noise and follows a gaussian distribution

The covariance function of the observed value is

I denotes an identity matrix. Further, n is_*A sample X_*As a test set, its output f_*Also obeying the Gaussian distribution N (mu (x)_*),K(x_*,x_*) And the observation value and the predicted value of the test sample obey a joint Gaussian prior distribution function:

in the formula (1), the covariance K (X, X) is n × n-dimensional matrix, K (X, X)_*) Is n × n_*Dimension matrix, K (X)_*X) is n_*× n-dimensional matrix, K (X)_*,X_*) Is n_*×n_*Dimension matrix

(3) And (5) training the model. Inputting training sample data, converting the prior distribution function into a posterior distribution function, and optimizing the super parameter value of the kernel function; and obtaining a Gaussian process regression prediction model.

In this embodiment, the output f can be obtained by the bayesian principle and the prior distribution function of the joint gaussian distribution_*The conditional posterior distribution function of (2):

the embodiment can obtain the predicted output value of the test sample according to the mean value and the covariance of the posterior distribution.

Further, the optimization process of the hyper-parameter specifically includes:

since the zero mean value is usually adopted in the calculation process, the prediction error is generally greatly influenced by the covariance, so that the optimal hyper-parameter of the covariance function must be determined to reduce the prediction error. And selecting a square exponential function as a covariance function of the Gaussian kernel function, wherein the function is expressed as formula (3).

In the formula (3), l represents a feature length scale and represents a sample standard deviation, and θ ═ l, } ═ θ₁,θ₂,…,θ_nThe posterior distribution function of the hyper-parameters can be obtained by knowing P (a | B, C) P (B | C) ═ P (B | a, C) P (a | C) ═ P (a, B | C) through bayesian theory:

in equation (4), p (θ | X) represents a hyper-parametric prior, and since the likelihood function value thereof is independent of the sample X, p (θ | X) ═ p (θ), this value is set to be constant; p (y | X) represents a likelihood function of the sample observed value, the function value of which is independent of θ, and this value may be set to a constant; p (y | θ, X) represents an edge likelihood function, y obeys Gaussian scoreCloth

Sample variance

The sum covariance K (X, X) depends on the hyper-parameter set θ, hence

Finally, calculating the optimal hyper-parameter and converting the optimal hyper-parameter into the maximum value of the solving formula (5), and solving the logarithm of the formula (5) for convenient solving to obtain

In equation (6), the log-likelihood function contains three terms: the first term is a complex penalty term for preventing the training model from overfitting; the second item is a data fitting item and is used for representing the fitting degree of the hyper-parameters to the training samples; the third term is a constant term. In the process of solving the maximum value of the log-likelihood function, a conjugate gradient method is generally adopted, namely, the gradient negative direction is obtained by calculating the derivative of each parameter for several times, and then the optimal hyper-parameter is obtained by carrying out iterative calculation for many times until convergence.

In an embodiment of the present invention, for the observation data and the input data in this embodiment, since the gaussian process regression method generally adopts the zero-mean assumption, when the non-stationary training sample data is predicted, the predicted value rapidly approaches zero, which results in a large prediction error of the model. To eliminate the physical dimension effect, input sample data { P } must be input_s，f_jAnd observation sample data { ISR₁,ISR₂,V₁,V₂Standardizing, and then carrying out model training and prediction. For any array Z ═ Z₁,z₂,…,z_nAnd (6) the standardization processing formula is shown as the formula (7).

In the formula (7), Z' represents the array after the normalization process,

represents the average of the raw data, stdZ represents the standard deviation of the raw data, i.e., equation (8):

after the raw data is normalized, the mean of the training samples becomes 0 and the variance is 1. On the contrary, the predicted value obtained by using the gaussian process regression prediction model is also the normalized data, and the inverse solution according to the formula (7) is needed to restore the predicted value dimension.

In the present embodiment, input sample data { P after normalization processing is used_s，f_jAnd observation sample data { ISR₁,ISR₂,V₁,V₂And (5) carrying out Gaussian process regression model training, setting a hyperparameter initial value theta of the square exponential kernel function to be {1,1}, and setting the standard deviation of Gaussian noise likelihood estimation to be 0.37. Experiments show that the change trend of actually measured data can be fitted by the interference-signal ratio and AGC voltage change quantity of the prediction output of the training sample, and in addition, the probabilistic uncertainty of the prediction output can be given by the Gaussian process regression method. The smaller the 95% confidence interval range is, the higher the corresponding prediction output value credibility is; conversely, the larger the interval, the lower the confidence in the corresponding predicted output value.

In this embodiment, fig. 7 shows the gaussian process regression training error of the threshold of the electromagnetic interference effect when the data chain is in error, where fig. 7a shows the interference-to-signal ratio prediction error when the data chain is in error, and fig. 7b shows the AGC voltage variation prediction error when the data chain is in error. Fig. 8 shows the gaussian process regression training error of the emi threshold when the data link is unlocked, where fig. 8a shows the interference-to-signal ratio prediction error when the data link is unlocked, and fig. 8b shows the AGC voltage variation prediction error when the data link is unlocked.

As shown in fig. 7 and 8, compared with the observed sample value obtained by actual test, the deviation of the predicted output value of the co-channel interference effect threshold is relatively large, which is caused by inaccurate measurement opportunity due to unstable state of the data chain under the co-channel interference condition; the larger the interference frequency offset is, the more stable the state of the data link when the electromagnetic interference effect occurs is, the relatively accurate the measurement opportunity is, the better the data fitting effect is, and the total prediction error is less than 1.5 dB. And obtaining a Gaussian process regression prediction model through the sample learning and training.

In one embodiment of the invention, the electromagnetic parameters of the drone include an operating signal power, the environmental interference data includes an electromagnetic interference frequency; the electromagnetic interference effect threshold comprises an interference signal power threshold and an AGC voltage threshold; as shown in fig. 4, fig. 4 shows a specific duration flow of S102 in fig. 2, and the process thereof is detailed as follows:

s301: inputting the working signal power and the electromagnetic interference frequency into the Gaussian process regression prediction model, and outputting corresponding interference-signal ratio and AGC voltage variation when different effects occur in the unmanned aerial vehicle data chain;

s302: determining interference signal power thresholds of the unmanned aerial vehicle data chain in different working states according to corresponding interference-signal ratios and the working signal power when different effects occur in the unmanned aerial vehicle data chain;

s303: and determining AGC voltage threshold values of the unmanned aerial vehicle data chain in different working states according to the AGC voltage variation and the standard AGC voltage corresponding to the unmanned aerial vehicle data chain with different effects.

In this embodiment, the power of the interference signal in the current environment of the unmanned aerial vehicle is compared with the power threshold of the interference signal in different working states, and the electromagnetic interference situation where the data link of the unmanned aerial vehicle is located is determined.

In this embodiment, the current AGC voltage of the unmanned aerial vehicle may also be compared with AGC voltage thresholds in different operating states, so as to determine an electromagnetic interference situation where the data link of the unmanned aerial vehicle is located.

And further, determining the electromagnetic interference situation of the data chain of the unmanned aerial vehicle by comprehensive judgment of the interference signal power and the current AGC voltage.

In one embodiment of the present invention, the data link effect includes out-of-lock and bit error, and the interference signal power threshold includes a first interference signal power threshold, a second interference signal power threshold, a third interference signal power threshold, and a fourth interference signal power threshold; the specific implementation flow of S302 in fig. 4 further includes:

calculating the product of the corresponding interference-signal ratio and the working signal power when the unmanned aerial vehicle data link has the out-of-lock effect to obtain a first interference signal power threshold; the first interference signal power threshold is an interference signal power threshold when the unmanned aerial vehicle data link enters an out-of-lock state;

calculating the product of the corresponding interference-signal ratio and the working signal power when the error code effect occurs to the data chain of the unmanned aerial vehicle to obtain a second interference signal power threshold value, wherein the second interference signal power threshold value is the interference signal power threshold value when the data chain of the unmanned aerial vehicle enters a critical out-of-lock state from an unstable state;

subtracting a first preset buffer value from the second interference signal power threshold value to obtain a third interference signal power threshold value, wherein the third interference signal power threshold value is an interference signal power threshold value when the unmanned aerial vehicle data chain enters an unstable state from a relatively stable state;

and subtracting a second preset buffer value from the third interference signal power threshold value to obtain a fourth interference signal power threshold value, wherein the fourth interference signal power threshold value is an interference signal power threshold value corresponding to the fact that the unmanned aerial vehicle data chain enters a relatively stable state from a stable state.

In this embodiment, the working states of the data chain are defined as out-of-lock, critical out-of-lock, unstable, relatively stable, and stable. Wherein the stable and relatively stable states are states that are not affected by external electromagnetic interference; instability indicates that the stability of the link is poor after the data link is affected by external electromagnetic interference, and the state parameters of the airplane have fluctuation risks; the critical unlocking indicates that the data link is unstable after being influenced by external electromagnetic interference, and the fluctuation of the state parameters of the airplane is large; losing the lock and showing that the data link receives external electromagnetic interference influence back communication interruption, unmanned aerial vehicle loses control.

In the present embodiment, both the first preset buffer value and the second preset buffer value may be set to 6 dB.

In this embodiment, if the current interference signal power of the unmanned aerial vehicle is greater than the first interference signal power threshold, it is predicted that the unmanned aerial vehicle is in an out-of-lock state; if the current interference signal power of the unmanned aerial vehicle is larger than the second interference signal power threshold and smaller than or equal to the first interference signal power threshold, predicting that the unmanned aerial vehicle is in a critical out-of-lock state; if the current interference signal power of the unmanned aerial vehicle is greater than the third interference signal power threshold and less than or equal to the second interference signal power threshold, predicting that the unmanned aerial vehicle is in an unstable state; if the current interference signal power of the unmanned aerial vehicle is greater than the fourth interference signal power threshold and less than or equal to the third interference signal power threshold, predicting that the unmanned aerial vehicle is in a relatively stable state; and if the current interference signal power of the unmanned aerial vehicle is smaller than or equal to the fourth interference signal power threshold, predicting that the unmanned aerial vehicle is in a stable state.

In this embodiment, the first interference signal power threshold is greater than the second interference signal power threshold, the second interference signal power threshold is greater than the third interference signal power threshold, and the third interference signal power threshold is greater than the fourth interference signal power threshold.

In one embodiment of the present invention, the data link effects include loss of lock and bit error, and the AGC voltage thresholds include a first AGC voltage threshold, a second AGC voltage threshold, a third AGC voltage threshold, and a fourth AGC voltage threshold; the specific implementation flow of S303 in fig. 4 includes:

adding the AGC voltage variation corresponding to the unmanned aerial vehicle data link under the unlocking effect to a standard AGC voltage to obtain a first AGC voltage threshold value, wherein the first AGC voltage threshold value is the AGC voltage threshold value when the unmanned aerial vehicle data link enters the unlocking state;

adding the AGC voltage variation corresponding to the data chain of the unmanned aerial vehicle under the error code effect to a standard AGC voltage to obtain a second AGC voltage threshold value, wherein the second AGC voltage threshold value is the AGC voltage threshold value when the data chain of the unmanned aerial vehicle enters a critical unlocking state from an unstable state;

subtracting a third preset buffer value from the second AGC voltage threshold value to obtain a third AGC voltage threshold value, wherein the third AGC voltage threshold value is an AGC voltage threshold value when the unmanned aerial vehicle data chain enters an unstable state from a relatively stable state;

and subtracting a fourth preset buffer value from the third AGC voltage threshold value to obtain a fourth AGC voltage threshold value, wherein the fourth AGC voltage threshold value is the AGC voltage threshold value when the unmanned aerial vehicle data chain enters a relatively stable state from the stable state.

In the present embodiment, both the third preset buffer value and the fourth preset buffer value may be set to 5V. The first AGC voltage threshold is greater than the second AGC voltage threshold, the second AGC voltage threshold is greater than the third AGC voltage threshold, and the third AGC voltage threshold is greater than the third AGC voltage threshold.

In this embodiment, if the current AGC voltage of the unmanned aerial vehicle is greater than the first AGC voltage threshold, it is predicted that the data link of the unmanned aerial vehicle is in an out-of-lock state; if the current AGC voltage of the unmanned aerial vehicle is greater than a second AGC voltage threshold and less than or equal to a first AGC voltage threshold, the unmanned aerial vehicle data link is predicted to be in a critical unlocking state, if the current AGC voltage of the unmanned aerial vehicle is greater than a third AGC voltage threshold and less than or equal to a second AGC voltage threshold, the unmanned aerial vehicle data link is predicted to be in an unstable state, if the current AGC voltage of the unmanned aerial vehicle is greater than a fourth AGC voltage threshold and less than or equal to a third AGC voltage threshold, the unmanned aerial vehicle data link is predicted to be in a relatively stable state, and if the current AGC voltage of the unmanned aerial vehicle is less than or equal to a fourth AGC voltage threshold, the unmanned aerial vehicle data link is predicted to be in a stable state.

In an embodiment of the present invention, the method for predicting the electromagnetic interference situation of the data link of the unmanned aerial vehicle provided in this embodiment further includes:

and determining corresponding anti-interference response measures according to the electromagnetic interference situation of the unmanned aerial vehicle data chain.

In an embodiment of the present invention, the electromagnetic interference situation includes an out-of-lock state, a critical out-of-lock state, an unstable state, a relatively stable state, and a stable state, and the specific process of determining the anti-interference response measure includes:

if the unmanned aerial vehicle data chain is in the relatively stable state, generating an abnormal alarm signal of the unmanned aerial vehicle data chain;

if the data link of the unmanned aerial vehicle is in the unstable state and the critical unlocking state, controlling the unmanned aerial vehicle to execute an electromagnetic interference adaptive behavior; the electromagnetic interference adaptive behavior includes, but is not limited to, changing flight path, adjusting airborne antenna direction, switching operating channels, and controlling ground transmit power.

In this embodiment, the stable state does not require any operation on the unmanned aerial vehicle, and the relative stable state is determined as a primary warning interval according to electromagnetic interference level division, and at this time, an alarm signal is generated and used for marking the data chain of the unmanned aerial vehicle. And determining unstable and critical unlocking states as an early warning interval, and executing electromagnetic interference adaptive behaviors when the data link of the unmanned aerial vehicle is in the early warning interval, wherein the electromagnetic interference adaptive behaviors comprise changing a flight line, adjusting the direction of an antenna, switching a working channel and controlling ground transmitting power. The method comprises the steps of changing a flight route to be far away from an interference source, adjusting the direction of an antenna and switching a working channel to reduce coupling efficiency, and controlling ground transmitting power to improve the anti-interference capability of equipment.

In this embodiment, if the drone is in an unstable state, the anti-interference mode of switching the working channel may be selected preferentially. If unmanned aerial vehicle is in the critical state of losing lock, the space spectrum purity is poor, and the channel switching can not satisfy anti-interference demand, in order to alleviate interference signal to unmanned aerial vehicle data link's influence, reduce the transmission loss of ground control station, suitably increase working signal and can make the data link reach anti-jamming purpose.

Specifically, since the electromagnetic interference signal already poses a serious threat to the data link, the electromagnetic interference margin a must be added to the data link, so that the electromagnetic interference threat degree of the data link of the unmanned aerial vehicle is reduced to an unstable state, that is, the power threshold value S of the second interference signal_j＝P_j+a。P_jRepresenting the interference signal power currently monitored by the drone. Using gaussInverse solution of S in process regression prediction model_jOperating signal power under conditions P_s。

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

In an embodiment, as shown in fig. 9, fig. 9 shows a schematic structural diagram of the unmanned aerial vehicle data link electromagnetic interference situation prediction apparatus 100 provided in this embodiment, which includes:

an electromagnetic data acquisition module 110, configured to acquire electromagnetic parameters and environmental interference data of the unmanned aerial vehicle;

a threshold obtaining module 120, configured to input the electromagnetic parameters of the unmanned aerial vehicle and the environmental interference data into a gaussian process regression prediction model to obtain an electromagnetic interference effect threshold;

an electromagnetic interference situation determination module 130, configured to determine, according to the environmental interference data and the electromagnetic interference effect threshold, an electromagnetic interference situation where the data chain of the unmanned aerial vehicle is located.

In an embodiment, the apparatus 100 for predicting the electromagnetic interference situation of the data link of the unmanned aerial vehicle provided by this embodiment further includes:

the training sample acquisition module is used for acquiring a training sample, and the training sample comprises observation data and input data; the observation data comprises an interference-signal ratio and AGC voltage variation, and the input data comprises electromagnetic parameters and environmental interference data;

the sample data standardization module is used for carrying out standardization processing on the observation data and the input data in the training sample to obtain a standardized training sample;

the initial model creating module is used for creating an initial prediction model based on a Gaussian process regression method;

and the model training module is used for inputting the standardized training samples into the initial prediction model and training the initial prediction model to obtain the Gaussian process regression prediction model.

In one embodiment, the electromagnetic parameters of the drone include an operating signal power, the environmental interference data includes an electromagnetic interference frequency; the electromagnetic interference effect threshold comprises an interference signal power threshold and an AGC voltage threshold;

the threshold acquisition module 120 includes:

the output value acquisition unit is used for inputting the working signal power and the electromagnetic interference frequency into the Gaussian process regression prediction model and outputting corresponding interference-signal ratio and AGC voltage variation when different effects appear in the unmanned aerial vehicle data chain;

the interference signal power threshold acquisition unit is used for determining interference signal power thresholds of the unmanned aerial vehicle data chain in different working states according to corresponding interference-signal ratios and the working signal powers when different effects occur in the unmanned aerial vehicle data chain;

and the voltage threshold acquisition unit is used for determining the AGC voltage threshold of the unmanned aerial vehicle data chain in different working states according to the AGC voltage variation and the standard AGC voltage corresponding to the unmanned aerial vehicle data chain with different effects.

In one embodiment, the data link effects include loss of lock and bit errors, and the interference signal power thresholds include a first interference signal power threshold, a second interference signal power threshold, a third interference signal power threshold, and a fourth interference signal power threshold;

the interference signal power threshold acquisition unit includes:

the first interference signal power threshold value calculation operator unit is used for calculating the product of the corresponding interference-signal ratio and the working signal power when the unmanned aerial vehicle data link has the out-of-lock effect, so as to obtain a first interference signal power threshold value; the first interference signal power threshold is an interference signal power threshold when the unmanned aerial vehicle data link enters an out-of-lock state;

a second interference signal power threshold calculation subunit, configured to calculate a product of an interference-to-signal ratio corresponding to an error code effect occurring in the data chain of the unmanned aerial vehicle and the power of the working signal, so as to obtain a second interference signal power threshold, where the second interference signal power threshold is an interference signal power threshold at which the data chain of the unmanned aerial vehicle enters a critical out-of-lock state from an unstable state;

the third interference signal power threshold value operator unit is used for subtracting a first preset buffer value from the second interference signal power threshold value to obtain a third interference signal power threshold value, and the third interference signal power threshold value is an interference signal power threshold value when the unmanned aerial vehicle data chain enters an unstable state from a relatively stable state;

and the fourth interference signal power threshold value operator unit is used for subtracting a second preset buffer value from the third interference signal power threshold value to obtain a fourth interference signal power threshold value, and the fourth interference signal power threshold value is an interference signal power threshold value corresponding to the fact that the unmanned aerial vehicle data chain enters a relatively stable state from the stable state.

In one embodiment, the data link effects include out-of-lock and bit errors, and the AGC voltage thresholds include a first AGC voltage threshold, a second AGC voltage threshold, a third AGC voltage threshold, and a fourth AGC voltage threshold; the voltage threshold acquisition unit includes:

the first AGC voltage threshold value obtaining subunit is used for adding the AGC voltage variation corresponding to the unmanned aerial vehicle data link under the unlocking effect with a standard AGC voltage to obtain a first AGC voltage threshold value, wherein the first AGC voltage threshold value is the AGC voltage threshold value when the unmanned aerial vehicle data link enters the unlocking state;

a second AGC voltage threshold obtaining subunit, configured to add an AGC voltage variation corresponding to the data chain of the unmanned aerial vehicle under an error code effect to a standard AGC voltage to obtain a second AGC voltage threshold, where the second AGC voltage threshold is an AGC voltage threshold at which the data chain of the unmanned aerial vehicle enters a critical out-of-lock state from an unstable state;

a third AGC voltage threshold obtaining subunit, configured to subtract a third preset buffer value from the second AGC voltage threshold to obtain a third AGC voltage threshold, where the third AGC voltage threshold is an AGC voltage threshold at which the data link of the unmanned aerial vehicle enters an unstable state from a relatively stable state;

and the fourth AGC voltage threshold acquisition subunit is used for subtracting a fourth preset buffer value from the third AGC voltage threshold to obtain a fourth AGC voltage threshold, and the fourth AGC voltage threshold is the AGC voltage threshold of the unmanned aerial vehicle data chain which enters a relatively stable state from the stable state.

and the interference measure determining module is used for determining a corresponding anti-interference response measure according to the electromagnetic interference situation of the unmanned aerial vehicle data chain.

In one embodiment, the jamming measure determination module includes:

the abnormal alarm unit is used for generating an abnormal alarm signal of the unmanned aerial vehicle data chain if the unmanned aerial vehicle data chain is in the relatively stable state;

the interference adaptive behavior execution unit is used for controlling the unmanned aerial vehicle to execute an electromagnetic interference adaptive behavior if the unmanned aerial vehicle data link is in the unstable state and the critical unlocking state; the electromagnetic interference adaptive behavior includes, but is not limited to, changing flight path, adjusting airborne antenna direction, switching operating channels, and controlling ground transmit power.

An embodiment of the present invention provides an adaptive prediction terminal 924, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor, when executing the computer program, implements the steps in each of the above-described embodiments of the method for predicting an electromagnetic interference situation of a data link of an unmanned aerial vehicle, for example, steps 101 to 103 shown in fig. 2. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 110 to 130 shown in fig. 9.

The computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the adaptive predictive terminal 924.

The adaptive prediction terminal 924 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing device. The drone may include, but is not limited to, a processor, a memory. Those skilled in the art will appreciate that the components of adaptive predictive terminal 924 given above do not constitute a limitation on adaptive predictive terminal 924, and may include more or less components than those described above, or some components in combination, or different components, e.g., the drone may also include input-output devices, network access devices, buses, etc.

The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory may be an internal storage unit of the adaptive predictive terminal 924, such as a hard disk or a memory of the adaptive predictive terminal 924. The memory may also be an external storage device of the adaptive predictive terminal 924, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the adaptive predictive terminal 924. Further, the memory may also include both an internal storage unit and an external storage device of the adaptive predictive terminal 924. The memory is used for storing the computer program and other programs and data required by the drone. The memory may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/drone and method may be implemented in other ways. For example, the above-described device/drone embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.

Claims

1. The method for predicting the electromagnetic interference situation of the data link of the unmanned aerial vehicle is characterized by comprising the following steps of:

2. The drone data link electromagnetic interference situation prediction method of claim 1, wherein prior to the obtaining the drone's electromagnetic parameters and environmental interference data, the method further comprises:

acquiring a training sample, wherein the training sample comprises observation data and input data; the observation data comprises an interference-signal ratio and AGC voltage variation, and the input data comprises electromagnetic parameters and environmental interference data;

standardizing the observation data and the input data in the training sample to obtain a standardized training sample;

establishing an initial prediction model based on a Gaussian process regression method;

and inputting the normalized training samples into the initial prediction model, and training the initial prediction model to obtain the Gaussian process regression prediction model.

3. The method of predicting drone data link electromagnetic interference posture of claim 1, wherein the electromagnetic parameters of the drone include operating signal power, the environmental interference data includes electromagnetic interference frequency; the electromagnetic interference effect threshold comprises an interference signal power threshold and an AGC voltage threshold;

inputting the electromagnetic parameters and the environmental interference data of the unmanned aerial vehicle into a Gaussian process regression prediction model to obtain an electromagnetic interference effect threshold, wherein the electromagnetic interference effect threshold comprises the following steps:

inputting the working signal power and the electromagnetic interference frequency into the Gaussian process regression prediction model, and outputting corresponding interference-signal ratio and AGC voltage variation when different effects occur in the unmanned aerial vehicle data chain;

determining interference signal power thresholds of the unmanned aerial vehicle data chain in different working states according to corresponding interference-signal ratios and the working signal power when different effects occur in the unmanned aerial vehicle data chain;

and determining AGC voltage threshold values of the unmanned aerial vehicle data chain in different working states according to the AGC voltage variation and the standard AGC voltage corresponding to the unmanned aerial vehicle data chain with different effects.

4. The method of claim 3, wherein the datalink effects include loss of lock and bit errors, and wherein the jamming signal power thresholds include a first jamming signal power threshold, a second jamming signal power threshold, a third jamming signal power threshold, and a fourth jamming signal power threshold;

the determining the interference signal power threshold of the data link of the unmanned aerial vehicle in different working states according to the corresponding interference-to-signal ratio and the working signal power when different effects occur in the data link of the unmanned aerial vehicle comprises:

5. The method of claim 3, wherein the datalink effects include loss of lock and bit errors, and wherein the AGC voltage thresholds include a first AGC voltage threshold, a second AGC voltage threshold, a third AGC voltage threshold, and a fourth AGC voltage threshold;

according to AGC voltage variation and standard AGC voltage that correspond when different effects appear in the unmanned aerial vehicle data link, confirm the AGC voltage threshold value of unmanned aerial vehicle data link under different operating condition, include:

6. The unmanned aerial vehicle data link electromagnetic interference situation prediction method of any one of claims 1 to 5, the method further comprising:

7. The unmanned aerial vehicle data link electromagnetic interference situation prediction method of claim 6, wherein the electromagnetic interference situation includes an out-of-lock state, a critical out-of-lock state, an unstable state, a relatively stable state, and a stable state;

the determining of the corresponding anti-interference response measures according to the electromagnetic interference situation where the unmanned aerial vehicle data chain is located includes:

8. The utility model provides an unmanned aerial vehicle data link electromagnetic interference situation prediction device which characterized in that includes:

9. An adaptive predictive terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 7 are implemented when the computer program is executed by the processor.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.