CN117176288A - Unmanned aerial vehicle GNSS deception signal detection method and system based on relative entropy - Google Patents
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Abstract
The application discloses a relative entropy-based unmanned aerial vehicle GNSS deception signal detection method and a relative entropy-based unmanned aerial vehicle GNSS deception signal detection system.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicle communication, in particular to an unmanned aerial vehicle GNSS deception signal detection method and system based on relative entropy.
Background
With the great development of unmanned aerial vehicle technology, unmanned aerial vehicle's use in many applications such as remote sensing survey, electric power inspection, search rescue task and disaster management all greatly increased. In addition, unmanned aerial vehicles are also widely used in military warfare. Although unmanned aerial vehicle technology is mature at present, the capability of the civil unmanned aerial vehicle for resisting satellite navigation deception jamming is weak, and the deception jamming problem of the unmanned aerial vehicle is not fully solved. Under the background that the dependence of various industries on unmanned aerial vehicles is greater and greater, how to improve the anti-interference capability of unmanned aerial vehicles has become an important research direction.
Global positioning systems (Global Navigation Satellite System, GNSS) are one of the core technologies for unmanned aerial vehicle platform navigation, guidance and control. Because the navigation satellite is far away from the ground, the satellite signal is very weak when reaching the ground, and the navigation terminal of the unmanned aerial vehicle is extremely easy to be influenced by suppression interference and deception interference. The suppression interference transmits a suppression signal by a high-power suppression interference machine so that the navigation terminal of the unmanned aerial vehicle cannot work normally. The deception jamming can make the navigation terminal of the unmanned aerial vehicle obtain wrong time, position, speed and other information by generating deception signals similar to real satellite signals or forwarding the real satellite signals, so as to achieve the deception purpose. In contrast, the power required to suppress interference is greater and is also easier to find. However, the deception jamming does not need too strong power, so that the deception jamming is better in concealment and higher in hazard.
The existing spoofing detection technology has great technical defects. The multi-peak detection method based on the mutual ambiguity function is a spoofing detection technology which is more used at present and is simpler to realize, but multipath signals can generate interference peaks. Methods using additional sensors, such as multiple antennas, high precision inertial navigation combinations, etc., are also possible, but these precision sensors are very costly. The machine learning-based method requires a large amount of data for training, and has high calculation force requirements, so that the method is difficult to realize on a miniaturized platform of the unmanned aerial vehicle.
Disclosure of Invention
In order to solve the technical problems, the embodiment of the application provides a relative entropy-based unmanned aerial vehicle GNSS deception signal detection method and system, which are used for judging whether deception signals exist or not by calculating the relative entropy of parameters in the unmanned aerial vehicle flight process and judging, so that the deception signal detection efficiency is improved.
The first aspect of the embodiment of the application provides a relative entropy-based unmanned aerial vehicle GNSS spoofing signal detection method and system, wherein the method comprises the following steps:
acquiring a plurality of flight parameter data in a preset time period in the flight process of the unmanned aerial vehicle to be detected;
modeling probability distribution of each flight parameter data to obtain probability functions of each flight parameter data;
solving according to probability functions of each flight parameter data to obtain a solving result;
and calculating the total relative entropy of each flight parameter data in a preset time period according to the solving result, and if the total relative entropy of the flight parameter data with the preset number is larger than a preset value, obtaining a deception signal of the unmanned aerial vehicle to be detected, so that the unmanned aerial vehicle to be detected sends out early warning information.
According to the method, probability distribution modeling is conducted on all flight parameter data to obtain probability functions of all flight parameter data, solving is conducted according to the probability functions of all flight parameter data to obtain solving results, the total relative entropy of all flight parameter data in the preset time period is calculated according to the solving results, if the total relative entropy of all flight parameter data in the preset number is larger than a preset value, a deception signal of the unmanned aerial vehicle to be detected is obtained, and judgment is conducted through calculation of the relative entropy of the parameters in the flight process of the unmanned aerial vehicle, so that whether deception signals exist or not is judged, and detection efficiency of the deception signals is improved.
In a possible implementation manner of the first aspect, probability distribution modeling is performed on each flight parameter data to obtain a probability function of each flight parameter data, which specifically is:
carrying out probability distribution modeling on each flight parameter data by using poisson distribution to obtain probability functions of each flight parameter data, wherein the probability function expression is as follows:
wherein P represents a probability function of random variable distribution according to poisson distribution law, x i The parameters representing the unmanned aerial vehicle flight process, e representing the euler constant, λ representing the mathematical expectation, i.e. the average number of events occurring per unit time.
In one possible implementation manner of the first aspect, the solving is performed according to a probability function of each flight parameter data to obtain a solving result, which is specifically:
substituting each flight parameter data of N epochs in a preset time period into each corresponding probability function and solving to obtain a solving result, wherein the solving result is the final expected value of each probability function.
In a possible implementation manner of the first aspect, the calculating, according to the solving result, a total relative entropy of each flight parameter data in a preset time period specifically includes:
calculating the relative entropy of each flight parameter data of the ith epoch and the flight parameter data of the ith-1 th epoch corresponding to each flight parameter data according to the solving result to obtain N relative entropies of each flight parameter data, wherein i is an integer greater than 1 and less than N;
obtaining the total relative entropy of each flight parameter data according to the N relative entropies of each flight parameter data, wherein the total relative entropy formula is as follows:
wherein D is KL Representing the KL divergence value, i.e. the total relative entropy of n epochs, p (x) T k Probability function, p (x) T, representing flight parameter data at the ith epoch k-1 And (3) representing a probability function of flight parameter data at the i-1 th epoch, wherein ln is a natural logarithm.
A second aspect of an embodiment of the present application provides an unmanned aerial vehicle GNSS spoofing signal detection system based on relative entropy, the system including:
the acquisition module is used for acquiring a plurality of flight parameter data in a preset time period in the flight process of the unmanned aerial vehicle to be detected;
the modeling module is used for modeling probability distribution of each flight parameter data to obtain probability functions of each flight parameter data;
the solving module is used for solving according to the probability function of each flight parameter data to obtain a solving result;
the judging module is used for calculating the total relative entropy of each flight parameter data in a preset time period according to the solving result, and if the total relative entropy of the flight parameter data with the preset number is larger than a preset value, obtaining that the unmanned aerial vehicle to be detected has a deception signal so as to enable the unmanned aerial vehicle to be detected to send out early warning information.
In a possible implementation manner of the second aspect, probability distribution modeling is performed on each flight parameter data to obtain a probability function of each flight parameter data, which specifically is:
carrying out probability distribution modeling on each flight parameter data by using poisson distribution to obtain probability functions of each flight parameter data, wherein the probability function expression is as follows:
wherein P represents a probability function of random variable distribution according to poisson distribution law, x i The parameters representing the unmanned aerial vehicle flight process, e representing the euler constant, λ representing the mathematical expectation, i.e. the average number of events occurring per unit time.
In a possible implementation manner of the second aspect, the solving is performed according to a probability function of each flight parameter data, so as to obtain a solving result, which is specifically:
substituting each flight parameter data of N epochs in a preset time period into each corresponding probability function and solving to obtain a solving result, wherein the solving result is the final expected value of each probability function.
In a possible implementation manner of the second aspect, the calculating, according to the solving result, total relative entropy of each flight parameter data in the preset time period specifically includes:
calculating the relative entropy of each flight parameter data of the ith epoch and the flight parameter data of the ith-1 epoch corresponding to each flight parameter data according to the solving result to obtain N relative entropies of each flight parameter data, wherein i is an integer greater than 1 and less than N;
obtaining total relative entropy of each flight parameter data according to N relative entropies of each flight parameter data, wherein the total relative entropy formula is as follows:
wherein D is KL Representing the KL divergence value, i.e. the total relative entropy of n epochs, p (x) T k Representation ofProbability function, p (x) T, of flight parameter data at the ith epoch k-1 And (3) representing a probability function of flight parameter data at the i-1 th epoch, wherein ln is a natural logarithm.
A third aspect of an embodiment of the present application provides a terminal device, including: the system comprises a processor and a memory, wherein the memory stores a computer program, the computer program is configured to be executed by the processor, and the processor realizes the unmanned aerial vehicle GNSS deception signal detection method based on relative entropy when executing the computer program.
A fourth aspect of the embodiments of the present application provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement a method for detecting GNSS spoofing signals of an unmanned aerial vehicle based on relative entropy according to the embodiments of the present application.
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Fig. 1: the flow diagram of one embodiment of the unmanned aerial vehicle GNSS deception signal detection method based on the relative entropy is provided by the application;
fig. 2: the flow structure schematic diagram of one embodiment of the unmanned aerial vehicle GNSS deception signal detection method based on the relative entropy is provided by the application;
fig. 3: the application provides a system structure schematic diagram of another embodiment of the relative entropy-based unmanned aerial vehicle GNSS deception signal detection method.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
Referring to fig. 1, a flow chart of an embodiment of a method for detecting GNSS spoofing signals of an unmanned aerial vehicle based on relative entropy according to an embodiment of the present application includes steps S11 to S14, where the steps are specifically as follows:
s11, acquiring a plurality of flight parameter data in a preset time period in the flight process of the unmanned aerial vehicle to be detected.
In this embodiment, first, several parameters in the flight process of the unmanned aerial vehicle are selected, where the parameters include, but are not limited to, the flight altitude H, the flight speed, the number of visible satellites N received by the unmanned aerial vehicle, the flight elevation angle of the unmanned aerial vehicle, and the longitude Lon and latitude Lat of the location where the unmanned aerial vehicle is located.
S12, modeling probability distribution of each flight parameter data to obtain probability functions of each flight parameter data.
In a preferred embodiment, probability distribution modeling is performed on each flight parameter data to obtain probability functions of each flight parameter data, specifically:
carrying out probability distribution modeling on each flight parameter data by using poisson distribution to obtain probability functions of each flight parameter data, wherein the probability function expression is as follows:
wherein P represents a probability function of random variable distribution according to poisson distribution law, x i The parameters representing the unmanned aerial vehicle flight process, e representing the euler constant, λ representing the mathematical expectation, i.e. the average number of events occurring per unit time.
In this embodiment, probability distribution modeling is performed on each flight parameter data by using poisson distribution, so as to obtain probability functions of each flight parameter data, where the probability function expression is:
wherein P represents a probability function of random variable distribution according to poisson distribution law, x i The parameters representing the unmanned aerial vehicle flight process, e representing the euler constant, λ representing the mathematical expectation, i.e. the average number of events occurring per unit time.
It should be noted that, in order to ensure accuracy of detecting the spoofing signal, all the parameters need to be selected, and if only one parameter is selected individually, it is also feasible to calculate the relative entropy, but in practice, there is a problem that the rate of missing report is very high, so that multiple parameters need to participate in the detection together. The parameters mentioned in this patent are all parameters that are relatively easy to obtain during the actual unmanned aerial vehicle flight, but may also be used if other parameters, such as signal power, etc., can be obtained by some method. However, if these additional parameters are used, a re-modeling is required based on the law of variation of these parameters, as the law of variation of other parameters does not necessarily conform to the poisson distribution model used herein.
And S13, solving according to probability functions of the flight parameter data to obtain a solving result.
In this embodiment, after obtaining probability functions of each flight parameter data, the poisson probability distribution function is solved, the flight parameter data obtained by the unmanned aerial vehicle sensor in the last n epochs is selected, and the data is used as an independent variable to fit λ, so as to obtain a final λ value of the poisson probability density function when the probability P is maximum.
Taking the flying height H as an example, the flying height data obtained by the unmanned aerial vehicle sensor in the last n epochs is selected, and the data is used as an independent variable to fit lambda, so that lambda when the probability P is maximum is obtained as a final lambda value of the poisson probability density function.
And S14, calculating the total relative entropy of each flight parameter data in a preset time period according to the solving result, and if the total relative entropy of the flight parameter data with the preset number is larger than a preset value, obtaining that the unmanned aerial vehicle to be detected has a deception signal so as to enable the unmanned aerial vehicle to be detected to send out early warning information.
In a preferred embodiment, calculating the relative entropy of each flight parameter data of the ith epoch and the flight parameter data of the ith-1 th epoch corresponding to each flight parameter data according to the solving result to obtain N relative entropies of each flight parameter data, wherein i is an integer greater than 1 and less than N;
obtaining total relative entropy of each flight parameter data according to N relative entropies of each flight parameter data, wherein the total relative entropy formula is as follows:
wherein D is KL Representing the KL divergence value, i.e. the total relative entropy of n epochs, p (x) T k Probability function, p (x) T, representing flight parameter data at the ith epoch k-1 And (3) representing a probability function of flight parameter data at the i-1 th epoch, wherein ln is a natural logarithm.
In this embodiment, as shown in fig. 2, after obtaining a final expected value, n epochs are selected, and the relative entropy between each epoch and the preceding epoch of each flight parameter is calculated, and then the total relative entropy of n epochs is calculated, so as to obtain the total relative entropy of each flight parameter, which is specifically implemented as follows:
wherein D is KL Representing the KL divergence value, i.e. the total relative entropy of n epochs, p (x) T k Probability function, p (x) T, representing flight parameter data at the ith epoch k-1 And (3) representing a probability function of flight parameter data at the i-1 th epoch, wherein ln is a natural logarithm.
Then, analyzing the total relative entropy, judging that a deception signal exists when the relative entropy is larger than a set threshold value, giving an alarm to an operator by the unmanned aerial vehicle at the moment, and judging that the deception signal does not exist when the relative entropy is smaller than the set threshold value, and continuing to normally work by the unmanned aerial vehicle.
It should be noted that, the threshold value can be set according to actual conditions, in the unmanned aerial vehicle flight positioning process, parameters of the unmanned aerial vehicle are always stably output under the most ideal condition, the probability distribution of the parameters is unchanged before and after the time, and the total relative entropy is 0. However, even if no spoofing signal is available, some parameters may have small abnormal results, and the abnormalities may be caused by errors caused by the positioning algorithm, or changes of external environments, such as strong crosswind suddenly occurring in the flight process of the unmanned aerial vehicle, and at this time, some abnormal mutations may occur in parameters such as speed, elevation angle, etc. of the unmanned aerial vehicle. According to the advantages and disadvantages of the positioning algorithm and the inconsistency of the measured environmental change, the abnormal data are more or less, the inconsistency of probability distribution caused by the abnormal data is also more or less, but the fluctuation is in a certain range under the normal condition without fraud. The fluctuation range under the normal condition without fraud can be used as the threshold value, the probability distribution change under the condition of fraud is definitely far greater than that under the normal condition, the corresponding relative entropy value change is obvious, the value limit is obvious through accumulation among a plurality of epochs, and the threshold value can be set slightly more. Therefore, the size of the threshold is not fixed, and a longer data period is selected randomly from the data output by the actual test in specific application, and the same modeling-calculating-analyzing process is carried out on the data period to obtain the threshold.
As an example of this embodiment, assume that each epoch contains three sampling points, where epoch P data is 0.1,0.2,0.3 and the corresponding distribution probability is 0.2,0.4,0.4; the data of the next epoch Q is 0.4,0.3,0.6, and the corresponding distribution probability is 0.4,0.3,0.3; the data of the next epoch R is 0.7,0.5,0.3, and the corresponding distribution probability is 0.3,0.4,0.3.
Then the relative entropy calculation between the two epochs of PQ is:
the relative entropy calculation between these two epochs of QR is:
then the total relative entropy is the sum of the two relative entropies.
Taking the flying height H as an example, selecting n epochs, and calculating a total relative entropy formula as follows:
wherein D is KL Representing KL divergence value, i.e. the total relative entropy of n epochs, p (H) T k Poisson distribution, p (H) T representing fly height H at ith epoch k-1 The poisson distribution of the flight height H at the i-1 th epoch is represented, and l n is natural logarithm.
Acquiring a plurality of flight parameter data in a preset time period in the flight process of the unmanned aerial vehicle to be detected, carrying out probability distribution modeling on each flight parameter data to obtain probability functions of each flight parameter data, solving according to the probability functions of each flight parameter data to obtain a solving result, calculating the total relative entropy of each flight parameter data in the preset time period according to the solving result, and if the total relative entropy of the flight parameter data with the preset number is larger than a preset value, obtaining that the unmanned aerial vehicle to be detected has a deception signal.
Example two
Accordingly, referring to fig. 3, fig. 3 is a relative entropy-based unmanned aerial vehicle GNSS spoofing signal detecting system provided by the present application, as shown in the figure, the relative entropy-based unmanned aerial vehicle GNSS spoofing signal detecting system includes:
the acquiring module 301 is configured to acquire a plurality of flight parameter data in a preset time period in a flight process of the unmanned aerial vehicle to be detected;
the modeling module 302 is configured to perform probability distribution modeling on each flight parameter data to obtain a probability function of each flight parameter data;
the solving module 303 is configured to solve according to probability functions of each flight parameter data, so as to obtain a solving result;
the judging module 304 is configured to calculate a total relative entropy of each flight parameter data in a preset time period according to the solving result, and if the total relative entropy of the flight parameter data with the preset number is greater than a preset value, obtain that a spoofing signal exists in the unmanned aerial vehicle to be detected, so that the unmanned aerial vehicle to be detected sends out early warning information.
In a preferred embodiment, probability distribution modeling is performed on each flight parameter data to obtain probability functions of each flight parameter data, specifically:
carrying out probability distribution modeling on each flight parameter data by using poisson distribution to obtain probability functions of each flight parameter data, wherein the probability function expression is as follows:
wherein P represents a probability function of random variable distribution according to poisson distribution law, x i The parameters representing the unmanned aerial vehicle flight process, e representing the euler constant, λ representing the mathematical expectation, i.e. the average number of events occurring per unit time.
In a preferred embodiment, the solution is performed according to a probability function of each flight parameter data, so as to obtain a solution result, which specifically is:
substituting each flight parameter data of N epochs in a preset time period into each corresponding probability function and solving to obtain a solving result, wherein the solving result is the final expected value of each probability function.
In a preferred embodiment, the total relative entropy of each flight parameter data in the preset time period is calculated according to the solving result, specifically:
calculating the relative entropy of each flight parameter data of the ith epoch and the flight parameter data of the ith-1 epoch corresponding to each flight parameter data according to the solving result to obtain N relative entropies of each flight parameter data, wherein i is an integer greater than 1 and less than N;
obtaining total relative entropy of each flight parameter data according to N relative entropies of each flight parameter data, wherein the total relative entropy formula is as follows:
wherein D is KL Representing the KL divergence value, i.e. the total relative entropy of n epochs, p (x) T k Probability function, p (x) T, representing flight parameter data at the ith epoch k-1 And (3) representing a probability function of flight parameter data at the i-1 th epoch, wherein ln is a natural logarithm.
The more detailed working principle and the step flow of this embodiment can be, but not limited to, those described in the related embodiment one.
Example III
Correspondingly, the unmanned aerial vehicle GNSS deception signal detection system based on the relative entropy comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor is used for realizing the unmanned aerial vehicle GNSS deception signal detection method based on the relative entropy when executing the computer program.
Example IV
Accordingly, the present application provides a computer readable storage medium, on which a computer executable program is stored, which when executed by a processor implements the method for detecting GNSS spoofing signals of an unmanned aerial vehicle based on relative entropy as shown in the present application.
In summary, the embodiment of the application has the following beneficial effects:
acquiring a plurality of flight parameter data in a preset time period in the flight process of the unmanned aerial vehicle to be detected, carrying out probability distribution modeling on each flight parameter data to obtain probability functions of each flight parameter data, solving according to the probability functions of each flight parameter data to obtain a solving result, calculating the total relative entropy of each flight parameter data in the preset time period according to the solving result, and if the total relative entropy of the flight parameter data with the preset number is larger than a preset value, obtaining that the unmanned aerial vehicle to be detected has a deception signal.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present application, and are not to be construed as limiting the scope of the application. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present application are intended to be included in the scope of the present application.
Claims (10)
1. The unmanned aerial vehicle GNSS deception signal detection method based on the relative entropy is characterized by comprising the following steps of:
acquiring a plurality of flight parameter data in a preset time period in the flight process of the unmanned aerial vehicle to be detected;
modeling probability distribution of each flight parameter data to obtain probability functions of each flight parameter data;
solving according to the probability function of each flight parameter data to obtain a solving result;
and calculating the total relative entropy of each flight parameter data in a preset time period according to the solving result, and if the total relative entropy of the flight parameter data with the preset number is larger than a preset value, obtaining a deception signal of the unmanned aerial vehicle to be detected, so that the unmanned aerial vehicle to be detected sends out early warning information.
2. The method for detecting the GNSS spoofing signal of the unmanned aerial vehicle based on the relative entropy according to claim 1, wherein the probability distribution modeling is performed on each flight parameter data to obtain a probability function of each flight parameter data, specifically:
and modeling probability distribution of each flight parameter data by using poisson distribution to obtain probability functions of each flight parameter data, wherein the probability function expression is as follows:
wherein P represents a probability function of random variable distribution according to poisson distribution law, x i The parameters representing the unmanned aerial vehicle flight process, e representing the euler constant, λ representing the mathematical expectation, i.e. the average number of events occurring per unit time.
3. The method for detecting the GNSS spoofing signal of the unmanned aerial vehicle based on the relative entropy according to claim 1, wherein the solving is performed according to a probability function of each flight parameter data to obtain a solving result, specifically:
substituting each flight parameter data of N epochs in a preset time period into each corresponding probability function and solving to obtain a solving result, wherein the solving result is a final expected value of each probability function.
4. The method for detecting the GNSS spoofing signal of the unmanned aerial vehicle based on the relative entropy according to claim 1, wherein the calculating the total relative entropy of each flight parameter data in a preset time period according to the solving result is specifically as follows:
calculating the relative entropy of each flight parameter data of the ith epoch and the flight parameter data of the ith-1 th epoch corresponding to each flight parameter data according to the solving result to obtain N relative entropies of each flight parameter data, wherein i is an integer greater than 1 and less than N;
obtaining the total relative entropy of each flight parameter data according to the N relative entropies of each flight parameter data, wherein the total relative entropy formula is as follows:
wherein D is KL Representing the KL divergence value, i.e. the total relative entropy of n epochs, p (x) T k Probability function, p (x) T, representing flight parameter data at the ith epoch k-1 Represents the ith-probability function of flight parameter data at 1 epoch, ln is natural logarithm.
5. Unmanned aerial vehicle GNSS spoofing signal detecting system based on relative entropy, characterized by comprising:
the acquisition module is used for acquiring a plurality of flight parameter data in a preset time period in the flight process of the unmanned aerial vehicle to be detected;
the modeling module is used for modeling probability distribution of each flight parameter data to obtain probability functions of each flight parameter data;
the solving module is used for solving according to the probability function of each flight parameter data to obtain a solving result;
and the judging module is used for calculating the total relative entropy of each flight parameter data in a preset time period according to the solving result, and if the total relative entropy of the flight parameter data with the preset number is larger than a preset value, obtaining that the unmanned aerial vehicle to be detected has deceptive signals so that the unmanned aerial vehicle to be detected sends out early warning information.
6. The unmanned aerial vehicle GNSS spoofing signal detection system of claim 5 wherein the modeling of the probability distribution for each of the flight parameter data yields a probability function for each of the flight parameter data, in particular:
and modeling probability distribution of each flight parameter data by using poisson distribution to obtain probability functions of each flight parameter data, wherein the probability function expression is as follows:
wherein P represents a probability function of random variable distribution according to poisson distribution law, x i The parameters representing the unmanned aerial vehicle flight process, e representing the euler constant, λ representing the mathematical expectation, i.e. the average number of events occurring per unit time.
7. The unmanned aerial vehicle GNSS spoofing signal detecting system based on relative entropy according to claim 5, wherein the solving is performed according to the probability function of each flight parameter data to obtain a solving result, specifically:
substituting each flight parameter data of N epochs in a preset time period into each corresponding probability function and solving to obtain a solving result, wherein the solving result is a final expected value of each probability function.
8. The unmanned aerial vehicle GNSS spoofing signal detecting system of claim 5, wherein the calculating the total relative entropy of each of the flight parameter data in the predetermined time period according to the solving result is specifically as follows:
calculating the relative entropy of each flight parameter data of the ith epoch and the flight parameter data of the ith-1 th epoch corresponding to each flight parameter data according to the solving result to obtain N relative entropies of each flight parameter data, wherein i is an integer greater than 1 and less than N;
obtaining the total relative entropy of each flight parameter data according to the N relative entropies of each flight parameter data, wherein the total relative entropy formula is as follows:
wherein D is KL Representing the KL divergence value, i.e. the total relative entropy of n epochs, p (x) T k Probability function, p (x) T, representing flight parameter data at the ith epoch k-1 And (3) representing a probability function of flight parameter data at the i-1 th epoch, wherein ln is a natural logarithm.
9. A terminal device, comprising: a processor and a memory are provided for the processor,
the memory has stored therein a computer program, and the computer program is configured to be executed by the processor, which when executing the computer program, implements the unmanned aerial vehicle GNSS spoofing signal detection method based on relative entropy as defined in any of claims 1 to 4.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program is executed by a processor to implement the method of unmanned aerial vehicle GNSS spoofing signal detection based on relative entropy as defined in any of claims 1 to 4.
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| CN (1) | CN117176288A (en) |
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2023
- 2023-09-01 CN CN202311127157.6A patent/CN117176288A/en active Pending
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