CN118091644A - Unmanned aerial vehicle navigation deception method and device - Google Patents
Unmanned aerial vehicle navigation deception method and device Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/21—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service
- G01S19/215—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service issues related to spoofing
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/10—Internal combustion engine [ICE] based vehicles
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Abstract
The invention discloses a navigation spoofing method and a navigation spoofing device for an unmanned aerial vehicle, wherein the method comprises the following steps: acquiring position, speed and distance information of the unmanned aerial vehicle by utilizing radar detection, and acquiring a spoofing track of the unmanned aerial vehicle, false position information of the unmanned aerial vehicle and false area information of the unmanned aerial vehicle according to the radar detection information and the false area information of the unmanned aerial vehicle; processing the unmanned aerial vehicle deception track and the unmanned aerial vehicle false position information to obtain an unmanned aerial vehicle deception signal; the unmanned aerial vehicle deception signal is sent to the unmanned aerial vehicle by utilizing a signal simulator, the unmanned aerial vehicle deception signal controls the unmanned aerial vehicle to fly to a false area of the unmanned aerial vehicle. The method solves the problems that the conventional spoofing model has low spoofing precision and the actual spoofing position of the unmanned aerial vehicle deviates too much from the spoofing target point, bypasses the spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of pixhawk modules, ensures the concealment of navigation spoofing, and has simple and easy spoofing method and strong mobility.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle autonomous control, and particularly relates to an unmanned aerial vehicle spoofing method and device based on a Pixhawk module.
Background
With the development of society, unmanned aerial vehicles are playing an increasingly important role in many fields of daily life such as aerial photography, hydrologic monitoring, agricultural production and the like. However, the popularity of unmanned aerial vehicle applications, while bringing convenience to life, is also accompanied by risks and challenges. Due to the lack of effective supervision on unmanned aerial vehicles, the problem of 'black flight' of unmanned aerial vehicles, especially small unmanned aerial vehicles, constitutes a serious threat to normal production and living orders. To better regulate the use of unmanned aerial vehicles, unmanned aerial vehicle countering technical research in the form of low collateral damage is attracting more and more attention.
The cheating technology for the unmanned aerial vehicle flight control system is a very effective and easy-to-implement unmanned aerial vehicle countering technology with low collateral damage. On the current civil small unmanned aerial vehicle, the widely-equipped Pixhawk module is an important component part of a corresponding unmanned aerial vehicle flight control system, and can provide a complete autonomous flight control solution comprising sensor fusion, navigation planning, control algorithm and the like. However, the current unmanned spoofing technology for Pixhawk faces the following problems: 1. neglecting influence caused by target detection errors of the unmanned aerial vehicle and error generated by a spoofing signal source; 2. unmanned aerial vehicles equipped with a combined satellite navigation and inertial navigation system often employ normalized innovation square (Normalized Innovation Squared, NIS) detection to enhance anti-fraud capability. Therefore, the invention adopts an improved spoofing method aiming at the Pixhawk module of the small unmanned aerial vehicle, so that the unmanned aerial vehicle target makes an error decision, and further the unmanned aerial vehicle target is induced to be spoofed to an appointed safe area accurately so as to facilitate subsequent treatment.
Disclosure of Invention
The invention aims to solve the technical problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from a spoofing target point, and provides a method and a device for unmanned aerial vehicle navigation spoofing.
In order to solve the technical problems, a first aspect of the embodiment of the invention discloses an unmanned aerial vehicle navigation spoofing method, which comprises the following steps:
s1, acquiring first unmanned aerial vehicle position information, first unmanned aerial vehicle speed information, unmanned aerial vehicle deception target position information and unmanned aerial vehicle distance information;
S2, processing the first unmanned aerial vehicle position information, the first unmanned aerial vehicle speed information, the unmanned aerial vehicle deception target position information and the unmanned aerial vehicle distance information to obtain unmanned aerial vehicle deception tracks, unmanned aerial vehicle false position information and unmanned aerial vehicle false area information;
s3, processing the unmanned aerial vehicle deception track and the unmanned aerial vehicle false position information to obtain a first unmanned aerial vehicle deception signal; the first unmanned aerial vehicle deception signal comprises unmanned aerial vehicle false positions and unmanned aerial vehicle false speeds;
s4, processing the first unmanned aerial vehicle deception signal by using a signal simulator to obtain a second unmanned aerial vehicle deception signal, and sending the second unmanned aerial vehicle deception signal to the unmanned aerial vehicle;
s5, processing the second unmanned aerial vehicle deception signal to obtain second unmanned aerial vehicle position information and second unmanned aerial vehicle speed information;
S6, correcting the second unmanned aerial vehicle position information and the second unmanned aerial vehicle speed information to obtain third unmanned aerial vehicle position information and third unmanned aerial vehicle speed information;
S7, judging whether the false area information of the unmanned aerial vehicle is consistent with the position information of the third unmanned aerial vehicle, and obtaining a position consistency judging result;
when the position consistency judging result is negative, executing S8;
when the position consistency judging result is yes, ending the unmanned aerial vehicle deception task;
s8, judging the deception effectiveness of the third unmanned aerial vehicle position information by using a deception effect evaluation algorithm to obtain a deception effectiveness judgment result;
when the spoofing validity judgment result is yes, executing S9;
when the deception validity judgment result is negative, executing S1;
s9, processing the third unmanned aerial vehicle position information and the third unmanned aerial vehicle speed information by utilizing the unmanned aerial vehicle deception track to obtain fourth unmanned aerial vehicle position information and fourth unmanned aerial vehicle speed information, updating the first unmanned aerial vehicle position information by using the fourth unmanned aerial vehicle position information, updating the first unmanned aerial vehicle speed information by using the fourth unmanned aerial vehicle speed information, and executing S2.
As an optional implementation manner, in a first aspect of the embodiment of the present invention, the processing the first unmanned aerial vehicle location information, the first unmanned aerial vehicle speed information, the unmanned aerial vehicle spoofing target location information, and the unmanned aerial vehicle distance information to obtain an unmanned aerial vehicle spoofing track, unmanned aerial vehicle false location information, and unmanned aerial vehicle false area information includes:
s21, processing the first unmanned aerial vehicle position information by utilizing radar detection precision to obtain unmanned aerial vehicle range precision;
s22, carrying out fusion processing on the first unmanned aerial vehicle position information and the unmanned aerial vehicle range precision to obtain an unmanned aerial vehicle actual position range;
s23, utilizing the actual position range of the unmanned aerial vehicle and the target position information of the unmanned aerial vehicle spoofing, and presetting a spoofing area; the deception area is located in the actual position range of the unmanned aerial vehicle;
S24, the first unmanned aerial vehicle position information and the first unmanned aerial vehicle speed information are processed, and unmanned aerial vehicle deception tracks, unmanned aerial vehicle false position information and unmanned aerial vehicle false area information are obtained.
In an optional implementation manner, in a first aspect of the embodiment of the present invention, the processing, using radar detection accuracy, the first unmanned aerial vehicle position information to obtain unmanned aerial vehicle range accuracy includes:
S211, detecting the unmanned aerial vehicle by using a radar to obtain the distance L between the radar and the unmanned aerial vehicle;
s212, obtaining the unmanned aerial vehicle range accuracy by using an unmanned aerial vehicle range accuracy algorithm, the distance L between the radar and the unmanned aerial vehicle and the radar detection accuracy;
The unmanned aerial vehicle range accuracy algorithm expression is:
δL＝L×tanθ
Delta L is the range precision of the unmanned aerial vehicle, L is the distance between the radar and the unmanned aerial vehicle, and theta is the radar detection precision.
As an optional implementation manner, in a first aspect of the embodiment of the present invention, the processing the first unmanned aerial vehicle location information, the first unmanned aerial vehicle speed information, and the spoofing area to obtain a unmanned aerial vehicle spoofing track, unmanned aerial vehicle false location information, and unmanned aerial vehicle false area information includes:
s241, carrying out fusion processing on the first unmanned aerial vehicle position information and the first unmanned aerial vehicle speed information to obtain the unmanned aerial vehicle state variable;
The fusion processing expression is:
Xk＝[γx,k,γy,k,γz,k,vx,k,vy,k,vz,k]
Wherein (gamma x,k,γy,k,γz,k) is the first unmanned aerial vehicle position information, (v x,k,vy,k,vz,k) is the first unmanned aerial vehicle speed information, and X k is the unmanned aerial vehicle state variable;
S242, processing the unmanned aerial vehicle state variable by using an unmanned aerial vehicle motion model to obtain the unmanned aerial vehicle state value;
The unmanned aerial vehicle motion model is:
Xk＝Φk|k-1Xk-1+Γk-1Wk-1；
Wherein X k is the state quantity of the unmanned aerial vehicle at the moment k, phi k|k-1 is a state transition matrix of the unmanned aerial vehicle from the moment k-1 to the moment k, Γ k-1 is a noise matrix of the unmanned aerial vehicle at the moment k-1, and W k-1 is the system noise of the unmanned aerial vehicle at the moment k-1;
s243, carrying out observation calculation on the unmanned aerial vehicle state variable to obtain an unmanned aerial vehicle state observation value;
The observation calculation expression is:
Zk＝HkXk+Vk
Wherein Z k is the unmanned aerial vehicle state observation value at the moment k, X k is the unmanned aerial vehicle state quantity at the moment k, H k is the unmanned aerial vehicle measurement matrix at the moment k, and V k is the unmanned aerial vehicle measurement noise at the moment k;
S244, carrying out fusion processing on the unmanned aerial vehicle state value and the unmanned aerial vehicle state observation value to obtain an unmanned aerial vehicle state estimation value;
The fusion processing expression is:
Wherein Z k is the observation vector matrix of the unmanned aerial vehicle at the moment k, For the unmanned plane state vector matrix from time k-1 to time k,For the unmanned plane state estimation matrix at the time K, K k is the gain at the time K, P k|k-1 is the unmanned plane prediction mean square error from the time K-1 to the time K, P k is the unmanned plane estimation mean square error at the time K, Γ k-1 is the unmanned plane noise matrix, Q k-1 is the unmanned plane noise variance at the time K-1, and H k is the unmanned plane measurement matrix at the time K;
S245, processing the unmanned aerial vehicle state estimation value by using a false flight control model to obtain the unmanned aerial vehicle deception track;
The false flight control model is as follows:
Wherein K is the unmanned aerial vehicle control parameter matrix, Tracking a reference track for the unmanned aerial vehicle,Estimating a vector matrix for the unmanned aerial vehicle state;
S246, traversing the unmanned aerial vehicle deception track to acquire the unmanned aerial vehicle false position information and the unmanned aerial vehicle false area information.
In a first aspect of the embodiment of the present invention, the processing the unmanned aerial vehicle spoofing track and the unmanned aerial vehicle false position information to obtain a first unmanned aerial vehicle spoofing signal includes:
S31, processing the unmanned aerial vehicle deception track by utilizing the unmanned aerial vehicle false position information to obtain unmanned aerial vehicle speed information;
s32, packaging the false position information of the unmanned aerial vehicle and the speed information of the unmanned aerial vehicle to obtain false motion data of the unmanned aerial vehicle;
And S33, modulating the false motion data of the unmanned aerial vehicle to obtain the first unmanned aerial vehicle deception signal.
In a first aspect of the embodiment of the present invention, the processing, by using a signal simulator, the first unmanned aerial vehicle spoofing signal to obtain a second unmanned aerial vehicle spoofing signal, and sending the second unmanned aerial vehicle spoofing signal to the unmanned aerial vehicle includes;
S41, acquiring a real GPS signal of the unmanned aerial vehicle;
S42, adjusting the power, the code phase and the Doppler frequency shift of the first unmanned aerial vehicle deception signal by using the signal simulator, so that the first unmanned aerial vehicle deception signal is synchronous with the real GPS signal, and the second unmanned aerial vehicle deception signal is obtained;
S43, acquiring a loop updating interval of the GPS receiver of the unmanned aerial vehicle to obtain a deception signal transmitting time slot;
and S44, sending the second unmanned aerial vehicle deception signal to the unmanned aerial vehicle in the deception signal transmission time slot by using the signal simulator.
In a first aspect of the embodiment of the present invention, the processing the second unmanned aerial vehicle spoofing signal to obtain second unmanned aerial vehicle location information and second unmanned aerial vehicle speed information includes:
S51, demodulating the second unmanned aerial vehicle deception signal by using the GPS receiver of the unmanned aerial vehicle to obtain a second deception signal information set;
And S52, analyzing the second deception signal information set to obtain the second unmanned aerial vehicle position information and the second unmanned aerial vehicle speed information.
In an optional implementation manner, in a first aspect of the embodiment of the present invention, the correcting the second unmanned aerial vehicle position information and the second unmanned aerial vehicle speed information to obtain third unmanned aerial vehicle position information and third unmanned aerial vehicle speed information includes:
S61, processing the position information of the second unmanned aerial vehicle by using a pseudo-range generation model to obtain a deceptive signal pseudo-range of the second unmanned aerial vehicle;
The pseudo-range generation model is as follows:
Wherein ρ is the pseudo range of the satellite to the GPS receiver of the unmanned aerial vehicle, (X k,Yk,Zk) is the second unmanned aerial vehicle position coordinate, (X j,Yj,Zj) is the coordinate of the satellite participating in position calculation, δ b is the total equivalent distance between the satellite and the GPS receiver which can be eliminated, orbit error and atmospheric delay error, δ k is the total equivalent distance between the random error which cannot be eliminated and the signal simulator performance error;
S62, processing the deception signal pseudo range of the second unmanned aerial vehicle to obtain a second deception signal pseudo range error dP;
s63, obtaining the deception signal positioning error of the second unmanned aerial vehicle for the relation between the pseudo-range error dP and the false positioning error dR by using a least square method;
the relation expression of the pseudo-range error dP and the false positioning error dR is as follows:
dP＝H·dR
Wherein, N is the number of satellites, (X k,Yk,Zk) is the coordinate position of the unmanned aerial vehicle, (X j,Yj,Zj) is the position of the satellites,A geometric distance between the satellite and the unmanned aerial vehicle;
the pseudo-range error expression of the satellite is:
Wherein N is the number of satellites;
The covariance matrix of the false positioning error dR is as follows:
Wherein, For the location coordinate variance of the second unmanned aerial vehicle spoofing signal, (σ XY,σXZ,σYX,σYZ,σZX,σZY) for the location coordinate covariance of the second unmanned aerial vehicle spoofing signal;
s64, using false positioning error estimation algorithm to estimate false positioning error caused by the pseudo range error Solving to obtain the estimated value/>, of the positioning error of the spoofing signal of the second unmanned aerial vehicle
S65, acquiring a positioning error sigma M of the GPS receiver of the unmanned aerial vehicle;
S66, processing the variance of the positioning coordinates of the second unmanned aerial vehicle spoofing signal by using a positioning error precision factor to obtain the second unmanned aerial vehicle spoofing signal positioning error;
the positioning error precision factor expression is:
Wherein, Positioning coordinate variance of the spoofing signal for the second unmanned aerial vehicle;
s67, using pseudo-range measurement error and false positioning error estimation value Processing the second unmanned aerial vehicle spoofing signal positioning error sigma R and the positioning error sigma M of the unmanned aerial vehicle GPS receiver to obtain a combined navigation system spoofing positioning error radius delta F;
The integrated navigation system spoofing positioning error radius δ F expression is:
Wherein σ R is the positioning error of the second unmanned aerial vehicle spoofing signal, and σ M is the positioning error of the unmanned aerial vehicle GPS receiver;
and S68, carrying out fusion processing on the second unmanned aerial vehicle position information, the second unmanned aerial vehicle speed information and the positioning error to obtain the third unmanned aerial vehicle position information and the third unmanned aerial vehicle speed information.
In an optional implementation manner, in a first aspect of the embodiment of the present invention, the determining, by using a spoofing effect evaluation algorithm, the spoofing validity of the third location information of the unmanned aerial vehicle to obtain a spoofing validity determination result includes:
S81, processing the third unmanned aerial vehicle position information by using the deception effect evaluation algorithm to obtain a deception effect detection value;
The deception effect evaluation algorithm expression is:
Wherein S k＝HkPk|k-1Hk T+Rk,εk is a deception effect detection value, Z k is the third unmanned plane position information, H k is a k-moment system measurement matrix, S k is an information covariance matrix and R k is a measurement noise variance matrix;
s82, acquiring an alarm threshold epsilon * of a pixhawk module of the unmanned aerial vehicle;
S83, judging whether the deception effect detection value is smaller than or equal to the alarm threshold, and obtaining the deception effectiveness judgment result.
The second aspect of the invention discloses an unmanned aerial vehicle spoofing device, comprising:
A memory storing executable program code;
A processor coupled to the memory;
the processor invokes the executable program code stored in the memory to execute some or all of the steps in the unmanned aerial vehicle navigation spoofing method disclosed in the first aspect of the embodiment of the invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
(1) In the embodiment of the invention, a mode of correcting radar detection errors and deception signal errors is adopted to solve the problems that the deception precision of the traditional deception model is not high and the deviation of the actual deception position of the unmanned plane from deception target points is too large;
(2) In the embodiment of the invention, the alarm threshold value of pixhawk module is utilized to bypass the deception detection system of the unmanned aerial vehicle, so that the concealment of navigation deception is ensured, the deception method is simple and easy, and the mobility is strong.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a unmanned aerial vehicle navigation spoofing method disclosed in an embodiment of the invention;
Fig. 2 is a physical diagram of a Pixhawk module used in the unmanned aerial vehicle spoofing device according to the embodiment of the present invention;
fig. 3 is a schematic structural diagram of another unmanned aerial vehicle spoofing device according to an embodiment of the present invention.
Detailed Description
In order to make the present invention better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses a navigation spoofing method and device for an unmanned aerial vehicle, which solve the problems of low spoofing precision of a traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from a spoofing target point, ensure the concealment of spoofing, and have the advantages of simplicity, easiness, practicability and strong mobility. The following will describe in detail.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for unmanned aerial vehicle navigation spoofing according to an embodiment of the invention. The unmanned aerial vehicle navigation spoofing method described in fig. 1 is applied to an unmanned aerial vehicle spoofing attack system, such as a local server or cloud server for unmanned aerial vehicle spoofing attack management, and the embodiment of the invention is not limited. As shown in fig. 1, the unmanned aerial vehicle navigation spoofing method may include the following operations:
s1, acquiring first unmanned aerial vehicle position information, first unmanned aerial vehicle speed information, unmanned aerial vehicle deception target position information and unmanned aerial vehicle distance information;
S2, processing the first unmanned aerial vehicle position information, the first unmanned aerial vehicle speed information, the unmanned aerial vehicle deception target position information and the unmanned aerial vehicle distance information to obtain unmanned aerial vehicle deception tracks, unmanned aerial vehicle false position information and unmanned aerial vehicle false area information;
S3, processing the unmanned aerial vehicle deception track and the unmanned aerial vehicle false position information to obtain a first unmanned aerial vehicle deception signal;
The first unmanned aerial vehicle deception signal comprises unmanned aerial vehicle false positions and unmanned aerial vehicle false speeds;
s4, processing the first unmanned aerial vehicle deception signal by using a signal simulator to obtain a second unmanned aerial vehicle deception signal, and sending the second unmanned aerial vehicle deception signal to the unmanned aerial vehicle;
s5, processing the second unmanned aerial vehicle deception signal to obtain second unmanned aerial vehicle position information and second unmanned aerial vehicle speed information;
S6, correcting the second unmanned aerial vehicle position information and the second unmanned aerial vehicle speed information to obtain third unmanned aerial vehicle position information and third unmanned aerial vehicle speed information;
S7, judging whether the false area information of the unmanned aerial vehicle is consistent with the position information of the third unmanned aerial vehicle, and obtaining a position consistency judging result;
when the position consistency judging result is negative, executing S8;
when the position consistency judging result is yes, ending the unmanned aerial vehicle deception task;
s8, judging the deception effectiveness of the third unmanned aerial vehicle position information by using a deception effect evaluation algorithm to obtain a deception effectiveness judgment result;
when the spoofing validity judgment result is yes, executing S9;
when the deception validity judgment result is negative, executing S1;
s9, processing the third unmanned aerial vehicle position information and the third unmanned aerial vehicle speed information by utilizing the unmanned aerial vehicle deception track to obtain fourth unmanned aerial vehicle position information and fourth unmanned aerial vehicle speed information, updating the first unmanned aerial vehicle position information by using the fourth unmanned aerial vehicle position information, updating the first unmanned aerial vehicle speed information by using the fourth unmanned aerial vehicle speed information, and executing S2.
Therefore, the unmanned aerial vehicle navigation spoofing method described by the embodiment of the invention solves the problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from the spoofing target point, bypasses a spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of the pixhawk module, ensures the concealment of navigation spoofing, and has the advantages of simplicity, practicability and strong mobility.
In an optional embodiment, in the step S2, the processing the first unmanned aerial vehicle location information, the first unmanned aerial vehicle speed information, the unmanned aerial vehicle spoofing target location information, and the unmanned aerial vehicle distance information to obtain a unmanned aerial vehicle spoofing track, unmanned aerial vehicle false location information, and unmanned aerial vehicle false area information includes:
s21, processing the first unmanned aerial vehicle position information by utilizing radar detection precision to obtain unmanned aerial vehicle range precision;
s22, carrying out fusion processing on the first unmanned aerial vehicle position information and the unmanned aerial vehicle range precision to obtain an unmanned aerial vehicle actual position range;
s23, utilizing the actual position range of the unmanned aerial vehicle and the target position information of the unmanned aerial vehicle spoofing, and presetting a spoofing area; the deception area is located in the actual position range of the unmanned aerial vehicle;
S24, the first unmanned aerial vehicle position information and the first unmanned aerial vehicle speed information are processed, and unmanned aerial vehicle deception tracks, unmanned aerial vehicle false position information and unmanned aerial vehicle false area information are obtained.
Therefore, the unmanned aerial vehicle navigation spoofing method described by the embodiment of the invention solves the problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from the spoofing target point, bypasses a spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of the pixhawk module, ensures the concealment of navigation spoofing, and has the advantages of simplicity, practicability and strong mobility.
In another optional embodiment, in the step S21, the processing the first unmanned aerial vehicle location information to obtain unmanned aerial vehicle range accuracy by using radar detection accuracy includes:
S211, detecting the unmanned aerial vehicle by using a radar to obtain the distance L between the radar and the unmanned aerial vehicle;
s212, obtaining the unmanned aerial vehicle range accuracy by using an unmanned aerial vehicle range accuracy algorithm, the distance L between the radar and the unmanned aerial vehicle and the radar detection accuracy;
The unmanned aerial vehicle range accuracy algorithm expression is:
δL＝L×tanθ
Delta L is the range precision of the unmanned aerial vehicle, L is the distance between the radar and the unmanned aerial vehicle, and theta is the radar detection precision.
Therefore, the unmanned aerial vehicle navigation spoofing method described by the embodiment of the invention solves the problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from the spoofing target point, bypasses a spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of the pixhawk module, ensures the concealment of navigation spoofing, and has the advantages of simplicity, practicability and strong mobility.
In yet another optional embodiment, in step S24, the processing the first unmanned aerial vehicle location information, the first unmanned aerial vehicle speed information, and the spoofing area to obtain a unmanned aerial vehicle spoofing track, unmanned aerial vehicle false location information, and unmanned aerial vehicle false area information includes:
s241, carrying out fusion processing on the first unmanned aerial vehicle position information and the first unmanned aerial vehicle speed information to obtain the unmanned aerial vehicle state variable;
The fusion processing expression is:
Xk＝[γx,k,γy,k,γz,k,vx,k,vy,k,vz,k]
Wherein (gamma x,k,γy,k,γz,k) is the first unmanned aerial vehicle position information, (v x,k,vy,k,vz,k) is the first unmanned aerial vehicle speed information, and X k is the unmanned aerial vehicle state variable;
S242, processing the unmanned aerial vehicle state variable by using an unmanned aerial vehicle motion model to obtain the unmanned aerial vehicle state value;
The unmanned aerial vehicle motion model is:
Xk＝Φk|k-1Xk-1+Γk-1Wk-1；
Wherein X k is the state quantity of the unmanned aerial vehicle at the moment k, phi k|k-1 is a state transition matrix of the unmanned aerial vehicle from the moment k-1 to the moment k, Γ k-1 is a noise matrix of the unmanned aerial vehicle at the moment k-1, and W k-1 is the system noise of the unmanned aerial vehicle at the moment k-1;
s243, carrying out observation calculation on the unmanned aerial vehicle state variable to obtain an unmanned aerial vehicle state observation value;
The observation calculation expression is:
Zk＝HkXk+Vk
Wherein Z k is the unmanned aerial vehicle state observation value at the moment k, X k is the unmanned aerial vehicle state quantity at the moment k, H k is the unmanned aerial vehicle measurement matrix at the moment k, and V k is the unmanned aerial vehicle measurement noise at the moment k;
S244, carrying out fusion processing on the unmanned aerial vehicle state value and the unmanned aerial vehicle state observation value to obtain an unmanned aerial vehicle state estimation value;
The fusion processing expression is:
Wherein Z k is the observation vector matrix of the unmanned aerial vehicle at the moment k, For the unmanned plane state vector matrix from time k-1 to time k,For the unmanned plane state estimation matrix at the time K, K k is the gain at the time K, P k|k-1 is the unmanned plane prediction mean square error from the time K-1 to the time K, P k is the unmanned plane estimation mean square error at the time K, Γ k-1 is the unmanned plane noise matrix, Q k-1 is the unmanned plane noise variance at the time K-1, and H k is the unmanned plane measurement matrix at the time K;
S245, processing the unmanned aerial vehicle state estimation value by using a false flight control model to obtain the unmanned aerial vehicle deception track;
The false flight control model is as follows:
Wherein K is the unmanned aerial vehicle control parameter matrix, Tracking a reference track for the unmanned aerial vehicle,Estimating a vector matrix for the unmanned aerial vehicle state;
S246, traversing the unmanned aerial vehicle deception track to acquire the unmanned aerial vehicle false position information and the unmanned aerial vehicle false area information.
Therefore, the unmanned aerial vehicle navigation spoofing method described by the embodiment of the invention solves the problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from the spoofing target point, bypasses a spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of the pixhawk module, ensures the concealment of navigation spoofing, and has the advantages of simplicity, practicability and strong mobility.
In yet another optional embodiment, in the step S3, the processing the unmanned aerial vehicle spoofing track and the unmanned aerial vehicle false location information to obtain a first unmanned aerial vehicle spoofing signal includes:
S31, processing the unmanned aerial vehicle deception track by utilizing the unmanned aerial vehicle false position information to obtain unmanned aerial vehicle speed information;
s32, packaging the false position information of the unmanned aerial vehicle and the speed information of the unmanned aerial vehicle to obtain false motion data of the unmanned aerial vehicle;
And S33, modulating the false motion data of the unmanned aerial vehicle to obtain the first unmanned aerial vehicle deception signal.
Therefore, the unmanned aerial vehicle navigation spoofing method described by the embodiment of the invention solves the problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from the spoofing target point, bypasses a spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of the pixhawk module, ensures the concealment of navigation spoofing, and has the advantages of simplicity, practicability and strong mobility.
In an optional embodiment, in the step S4, the processing, by using a signal simulator, the first unmanned aerial vehicle spoofing signal to obtain a second unmanned aerial vehicle spoofing signal, and sending the second unmanned aerial vehicle spoofing signal to the unmanned aerial vehicle includes;
S41, acquiring a real GPS signal of the unmanned aerial vehicle;
S42, adjusting the power, the code phase and the Doppler frequency shift of the first unmanned aerial vehicle deception signal by using the signal simulator, so that the first unmanned aerial vehicle deception signal is synchronous with the real GPS signal, and the second unmanned aerial vehicle deception signal is obtained;
S43, acquiring a loop updating interval of the GPS receiver of the unmanned aerial vehicle to obtain a deception signal transmitting time slot;
and S44, sending the second unmanned aerial vehicle deception signal to the unmanned aerial vehicle in the deception signal transmission time slot by using the signal simulator.
Therefore, the unmanned aerial vehicle navigation spoofing method described by the embodiment of the invention solves the problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from the spoofing target point, bypasses a spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of the pixhawk module, ensures the concealment of navigation spoofing, and has the advantages of simplicity, practicability and strong mobility.
In another optional embodiment, in the step S5, the processing the second unmanned aerial vehicle spoofing signal to obtain second unmanned aerial vehicle location information and second unmanned aerial vehicle speed information includes:
S51, demodulating the second unmanned aerial vehicle deception signal by using the GPS receiver of the unmanned aerial vehicle to obtain a second deception signal information set;
And S52, analyzing the second deception signal information set to obtain the second unmanned aerial vehicle position information and the second unmanned aerial vehicle speed information.
Therefore, the unmanned aerial vehicle navigation spoofing method described by the embodiment of the invention solves the problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from the spoofing target point, bypasses a spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of the pixhawk module, ensures the concealment of navigation spoofing, and has the advantages of simplicity, practicability and strong mobility.
In another optional embodiment, in step S6, the correcting the second unmanned aerial vehicle position information and the second unmanned aerial vehicle speed information to obtain third unmanned aerial vehicle position information and third unmanned aerial vehicle speed information includes:
S61, processing the position information of the second unmanned aerial vehicle by using a pseudo-range generation model to obtain a deceptive signal pseudo-range of the second unmanned aerial vehicle;
The pseudo-range generation model is as follows:
Wherein ρ is the pseudo range of the satellite to the GPS receiver of the unmanned aerial vehicle, (X k,Yk,Zk) is the second unmanned aerial vehicle position coordinate, (X j,Yj,Zj) is the coordinate of the satellite participating in position calculation, δ b is the total equivalent distance between the satellite and the GPS receiver which can be eliminated, orbit error and atmospheric delay error, δ k is the total equivalent distance between the random error which cannot be eliminated and the signal simulator performance error;
S62, processing the deception signal pseudo range of the second unmanned aerial vehicle to obtain a second deception signal pseudo range error dP;
s63, obtaining the deception signal positioning error of the second unmanned aerial vehicle for the relation between the pseudo-range error dP and the false positioning error dR by using a least square method;
the relation expression of the pseudo-range error dP and the false positioning error dR is as follows:
dP＝H·dR
Wherein, N is the number of satellites, (X k,Yk,Zk) is the coordinate position of the unmanned aerial vehicle, (X j,Yj,Zj) is the position of the satellites,A geometric distance between the satellite and the unmanned aerial vehicle;
the pseudo-range error expression of the satellite is:
Wherein N is the number of satellites;
The covariance matrix of the false positioning error dR is as follows:
Wherein, For the location coordinate variance of the second unmanned aerial vehicle spoofing signal, (σ XY,σXZ,σYX,σYZ,σZX,σZY) for the location coordinate covariance of the second unmanned aerial vehicle spoofing signal;
s64, using false positioning error estimation algorithm to estimate false positioning error caused by the pseudo range error Solving to obtain the estimated value/>, of the positioning error of the spoofing signal of the second unmanned aerial vehicle
S65, acquiring a positioning error sigma M of the GPS receiver of the unmanned aerial vehicle;
S66, processing the variance of the positioning coordinates of the second unmanned aerial vehicle spoofing signal by using a positioning error precision factor to obtain the second unmanned aerial vehicle spoofing signal positioning error;
the positioning error precision factor expression is:
Wherein, Positioning coordinate variance of the spoofing signal for the second unmanned aerial vehicle;
s67, using pseudo-range measurement error and false positioning error estimation value Processing the second unmanned aerial vehicle spoofing signal positioning error sigma R and the positioning error sigma M of the unmanned aerial vehicle GPS receiver to obtain a combined navigation system spoofing positioning error radius delta F;
The integrated navigation system spoofing positioning error radius δ F expression is:
Wherein σ R is the positioning error of the second unmanned aerial vehicle spoofing signal, and σ M is the positioning error of the unmanned aerial vehicle GPS receiver;
and S68, carrying out fusion processing on the second unmanned aerial vehicle position information, the second unmanned aerial vehicle speed information and the positioning error to obtain the third unmanned aerial vehicle position information and the third unmanned aerial vehicle speed information.
Therefore, the unmanned aerial vehicle navigation spoofing method described by the embodiment of the invention solves the problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from the spoofing target point, bypasses a spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of the pixhawk module, ensures the concealment of navigation spoofing, and has the advantages of simplicity, practicability and strong mobility.
In another optional embodiment, in step S8, the determining, by using a fraud effect evaluation algorithm, the fraud availability of the third unmanned aerial vehicle location information, to obtain a fraud availability determination result, includes:
S81, processing the third unmanned aerial vehicle position information by using the deception effect evaluation algorithm to obtain a deception effect detection value;
The deception effect evaluation algorithm expression is:
Wherein S k＝HkPk|k-1Hk T+Rk,εk is a deception effect detection value, Z k is the third unmanned plane position information, H k is a k-moment system measurement matrix, S k is an information covariance matrix and R k is a measurement noise variance matrix;
s82, acquiring an alarm threshold epsilon * of a pixhawk module of the unmanned aerial vehicle;
It should be noted that, fig. 2 shows a pixhawk module of the unmanned aerial vehicle;
S83, judging whether the deception effect detection value is smaller than or equal to the alarm threshold, and obtaining the deception effectiveness judgment result.
Therefore, the unmanned aerial vehicle navigation spoofing method described by the embodiment of the invention solves the problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from the spoofing target point, bypasses a spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of the pixhawk module, ensures the concealment of navigation spoofing, and has the advantages of simplicity, practicability and strong mobility.
In another optional embodiment, in step S9, the processing the third unmanned aerial vehicle position information and the third unmanned aerial vehicle speed information by using the unmanned aerial vehicle spoofing track to obtain fourth unmanned aerial vehicle position information and fourth unmanned aerial vehicle speed information includes:
s91, acquiring the third unmanned aerial vehicle position information and the third unmanned aerial vehicle speed information;
S92, processing the unmanned aerial vehicle deception track by utilizing a track acceleration model to obtain predicted unmanned aerial vehicle speed information;
The track acceleration model expression is as follows:
Tall＝T1+T2+T3
Wherein J is jerk, T is jerk duration, J Max is maximum jerk, a Max is maximum acceleration, v Max is maximum speed, a 0 is initial acceleration, a T1 is acceleration at the end of the T 1 phase, a T2 is acceleration at the end of the T 2 phase, T all is total synchronization time, T 1 phase is jerk phase, T 2 phase is uniform acceleration phase, T 3 phase is deceleration phase, v 0 is initial speed, vT 1 is speed at the end of the T 1 phase, v T2 is speed at the end of the T 2 phase, and v T3 is speed at the end of the T 3 phase;
S92, processing the speed information of the predicted unmanned aerial vehicle by using a track position model to obtain the position information of the predicted unmanned aerial vehicle;
the track position model expression is:
Wherein J is jerk, J Max is maximum jerk, x 0 is the initial position of the unmanned aerial vehicle, x T1 is the position of the unmanned aerial vehicle at the end of the T 1 stage, x T2 is the position of the unmanned aerial vehicle at the end of the T 2 stage, x T3 is the position of the unmanned aerial vehicle at the end of the T 3 stage, a Max is maximum acceleration, a 0 is the initial acceleration of the unmanned aerial vehicle, a T1 is the acceleration of the unmanned aerial vehicle at the end of the T 1 stage, a T2 is acceleration of the unmanned aerial vehicle at the end of a T 2 stage, T all is total synchronization time of the unmanned aerial vehicle, T 1 is a jerk stage of the unmanned aerial vehicle, T 2 is a uniform acceleration stage of the unmanned aerial vehicle, T 3 is a deceleration stage of the unmanned aerial vehicle, v 0 is initial speed of the unmanned aerial vehicle, v T1 is speed of the unmanned aerial vehicle at the end of a T 1 stage, and v T2 is speed of the unmanned aerial vehicle at the end of a T 2 stage;
And S93, correcting the predicted unmanned aerial vehicle speed information and the predicted unmanned aerial vehicle position information to obtain fourth unmanned aerial vehicle position information and fourth unmanned aerial vehicle speed information.
Therefore, the unmanned aerial vehicle navigation spoofing method described by the embodiment of the invention solves the problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from the spoofing target point, bypasses a spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of the pixhawk module, ensures the concealment of navigation spoofing, and has the advantages of simplicity, practicability and strong mobility.
In another optional embodiment, in step S93, the correcting the predicted unmanned aerial vehicle speed information and the predicted unmanned aerial vehicle position information to obtain the fourth unmanned aerial vehicle position information and the fourth unmanned aerial vehicle speed information includes:
S931, obtaining a model by using the state variable to obtain an estimated state variable of the unmanned aerial vehicle;
the state variable acquisition model expression is:
Xt -＝FtXt--1+Btat
Wherein, A t is acceleration of the unmanned aerial vehicle at the time t, P t is pseudo-range error of the unmanned aerial vehicle at the time t, V t is unmanned aerial vehicle speed of the unmanned aerial vehicle at the time t, and Deltat is time difference from t to t-1;
S932, obtaining a model by utilizing the error covariance to obtain a first error covariance matrix;
the error covariance acquisition model expression is:
Wherein, P t - is the first error covariance matrix, P t is the pseudo-range error of the unmanned aerial vehicle at the time t, Q is the system noise of the unmanned aerial vehicle, and Deltat is the time difference from t to t-1;
s933, processing the first error covariance matrix by using a Kalman gain acquisition model to obtain Kalman gain;
The Kalman gain acquisition model expression is:
Kt＝PtHT(HPt -HT+R)-1
Wherein, h= [ 10 ], K t is kalman gain at time t, P t - is the first error covariance matrix, P t is pseudo-range error at time t, and R is the unmanned plane measurement noise;
s934, processing the current state variable and the Kalman gain of the unmanned aerial vehicle by using a position acquisition algorithm model to obtain the fourth unmanned aerial vehicle position information;
The position acquisition algorithm model expression is:
Xt＝Xt -+Kt(Zt-HXt -)
Wherein, h= [ 10 ], X t is a state estimation value, X t - is a state one-step prediction, K t is the kalman gain, and Z t is the position of the unmanned plane at time t;
S935, updating the first error covariance matrix by using an error covariance estimation model to obtain a second error covariance matrix;
the error covariance estimation model expression is:
Pt＝(I-KtH)Pt -
Wherein, h= [ 10 ], K t is the kalman gain, and P t is the time t error covariance matrix;
s936, updating the initial acceleration by using an acceleration update model to obtain the updated acceleration information;
the acceleration update model expression is:
at＝a0+j·t
wherein a 0 is initial acceleration, a t is acceleration at time t, j is jerk, and t is jerk duration;
S937, processing the acceleration a t at the time t by using the state variable acquisition model to obtain the fourth unmanned aerial vehicle speed information;
Wherein a 0 is initial acceleration, j is jerk, t is jerk duration, P t is pseudo-range error at time t, V t is speed of the unmanned aerial vehicle at time t, P t-1 is pseudo-range error at time t-1, V t-1 is speed of the unmanned aerial vehicle at time t-1, and Δt is time difference from t to t-1.
Therefore, the unmanned aerial vehicle navigation spoofing method described by the embodiment of the invention solves the problems of low spoofing precision of the traditional spoofing model and overlarge deviation of the actual spoofing position of the unmanned aerial vehicle from the spoofing target point, bypasses a spoofing detection system of the unmanned aerial vehicle by utilizing the alarm threshold value of the pixhawk module, ensures the concealment of navigation spoofing, and has the advantages of simplicity, practicability and strong mobility.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an unmanned aerial vehicle spoofing device according to an embodiment of the present invention. The device described in fig. 3 can be applied to a spoofing attack system of an unmanned aerial vehicle, such as a local server or a cloud server for spoofing attack management of the unmanned aerial vehicle, which is not limited in the embodiments of the present invention. As shown in fig. 3, the apparatus may include:
a memory 301 storing executable program code;
A processor 302 coupled with the memory 301;
The processor 302 invokes executable program code stored in the memory 301 for performing the steps in the unmanned aerial vehicle navigation spoofing method described in embodiment one.
The apparatus embodiments described above are merely illustrative, in which the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Finally, it should be noted that: the embodiment of the invention discloses a navigation spoofing method and a navigation spoofing device for an unmanned aerial vehicle, which are disclosed by the embodiment of the invention only for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A method of unmanned aerial vehicle navigation spoofing, the method comprising:
s1, acquiring first unmanned aerial vehicle position information, first unmanned aerial vehicle speed information, unmanned aerial vehicle deception target position information and unmanned aerial vehicle distance information;
S2, processing the first unmanned aerial vehicle position information, the first unmanned aerial vehicle speed information, the unmanned aerial vehicle deception target position information and the unmanned aerial vehicle distance information to obtain unmanned aerial vehicle deception tracks, unmanned aerial vehicle false position information and unmanned aerial vehicle false area information;
S3, processing the unmanned aerial vehicle deception track and the unmanned aerial vehicle false position information to obtain a first unmanned aerial vehicle deception signal;
s4, processing the first unmanned aerial vehicle deception signal by using a signal simulator to obtain a second unmanned aerial vehicle deception signal, and sending the second unmanned aerial vehicle deception signal to the unmanned aerial vehicle;
s5, processing the second unmanned aerial vehicle deception signal to obtain second unmanned aerial vehicle position information and second unmanned aerial vehicle speed information;
S6, correcting the second unmanned aerial vehicle position information and the second unmanned aerial vehicle speed information to obtain third unmanned aerial vehicle position information and third unmanned aerial vehicle speed information;
S7, judging whether the false area information of the unmanned aerial vehicle is consistent with the position information of the third unmanned aerial vehicle, and obtaining a position consistency judging result;
when the position consistency judging result is negative, executing S8;
when the position consistency judging result is yes, ending the unmanned aerial vehicle deception task;
s8, judging the deception effectiveness of the third unmanned aerial vehicle position information by using a deception effect evaluation algorithm to obtain a deception effectiveness judgment result;
when the spoofing validity judgment result is yes, executing S9;
when the deception validity judgment result is negative, executing S1;
S9, processing the third unmanned aerial vehicle position information and the third unmanned aerial vehicle speed information by utilizing the unmanned aerial vehicle deception track to obtain fourth unmanned aerial vehicle position information and fourth unmanned aerial vehicle speed information, updating the first unmanned aerial vehicle position information by utilizing the fourth unmanned aerial vehicle position information, updating the first unmanned aerial vehicle speed information by utilizing the fourth unmanned aerial vehicle speed information, and executing S2.
2. The unmanned aerial vehicle navigation spoofing method of claim 1, wherein the processing the first unmanned aerial vehicle location information, the first unmanned aerial vehicle speed information, the unmanned aerial vehicle spoofing target location information, and the unmanned aerial vehicle distance information to obtain unmanned aerial vehicle spoofing trajectories, unmanned aerial vehicle false location information, and unmanned aerial vehicle false area information comprises:
s21, processing the first unmanned aerial vehicle position information by utilizing radar detection precision to obtain unmanned aerial vehicle range precision;
s22, carrying out fusion processing on the first unmanned aerial vehicle position information and the unmanned aerial vehicle range precision to obtain an unmanned aerial vehicle actual position range;
s23, utilizing the actual position range of the unmanned aerial vehicle and the target position information of the unmanned aerial vehicle spoofing, and presetting a spoofing area; the deception area is located in the actual position range of the unmanned aerial vehicle;
S24, the first unmanned aerial vehicle position information and the first unmanned aerial vehicle speed information are processed, and unmanned aerial vehicle deception tracks, unmanned aerial vehicle false position information and unmanned aerial vehicle false area information are obtained.
3. The unmanned aerial vehicle navigation spoofing method of claim 2, wherein the processing the first unmanned aerial vehicle location information with radar detection accuracy results in unmanned aerial vehicle range accuracy, comprising:
S211, detecting the unmanned aerial vehicle by using a radar to obtain the distance L between the radar and the unmanned aerial vehicle;
s212, obtaining the unmanned aerial vehicle range accuracy by using an unmanned aerial vehicle range accuracy algorithm, the distance L between the radar and the unmanned aerial vehicle and the radar detection accuracy;
The unmanned aerial vehicle range accuracy algorithm expression is:
δL＝L×tanθ
Delta L is the range precision of the unmanned aerial vehicle, L is the distance between the radar and the unmanned aerial vehicle, and theta is the radar detection precision.
4. The unmanned aerial vehicle navigation spoofing method of claim 2, wherein the processing the first unmanned aerial vehicle location information, the first unmanned aerial vehicle speed information, and the spoofing area to obtain unmanned aerial vehicle spoofing trajectories, unmanned aerial vehicle false location information, and unmanned aerial vehicle false area information comprises:
s241, carrying out fusion processing on the first unmanned aerial vehicle position information and the first unmanned aerial vehicle speed information to obtain the unmanned aerial vehicle state variable;
The fusion processing expression is:
Xk＝[γx,k,γy,k,γz,k,vx,k,vy,k,vz,k]
Wherein (gamma x,k,γy,k,γz,k) is the first unmanned aerial vehicle position information, (v x,k,vy,k,vz,k) is the first unmanned aerial vehicle speed information, and X k is the unmanned aerial vehicle state variable;
S242, processing the unmanned aerial vehicle state variable by using an unmanned aerial vehicle motion model to obtain the unmanned aerial vehicle state value;
The unmanned aerial vehicle motion model is:
Xk＝Φk|k-1Xk-1+Γk-1Wk-1；
Wherein X k is the state quantity of the unmanned aerial vehicle at the moment k, phi k|k-1 is a state transition matrix of the unmanned aerial vehicle from the moment k-1 to the moment k, Γ k-1 is a noise matrix of the unmanned aerial vehicle at the moment k-1, and W k-1 is the system noise of the unmanned aerial vehicle at the moment k-1;
s243, carrying out observation calculation on the unmanned aerial vehicle state variable to obtain an unmanned aerial vehicle state observation value;
The observation calculation expression is:
Zk＝HkXk+Vk
Wherein Z k is the unmanned aerial vehicle state observation value at the moment k, X k is the unmanned aerial vehicle state quantity at the moment k, H k is the unmanned aerial vehicle measurement matrix at the moment k, and V k is the unmanned aerial vehicle measurement noise at the moment k;
S244, carrying out fusion processing on the unmanned aerial vehicle state value and the unmanned aerial vehicle state observation value to obtain an unmanned aerial vehicle state estimation value;
The fusion processing expression is:
Wherein Z k is the observation vector matrix of the unmanned aerial vehicle at the moment k, For the unmanned plane state vector matrix from time k-1 to time k,For the unmanned plane state estimation matrix at the time K, K k is the gain at the time K, P k|k-1 is the unmanned plane prediction mean square error from the time K-1 to the time K, P k is the unmanned plane estimation mean square error at the time K, Γ k-1 is the unmanned plane noise matrix, Q k-1 is the unmanned plane noise variance at the time K-1, and H k is the unmanned plane measurement matrix at the time K;
S245, processing the unmanned aerial vehicle state estimation value by using a false flight control model to obtain the unmanned aerial vehicle deception track;
The false flight control model is as follows:
Wherein K is the unmanned aerial vehicle control parameter matrix, Tracking a reference track for the unmanned aerial vehicle,Estimating a vector matrix for the unmanned aerial vehicle state;
S246, traversing the unmanned aerial vehicle deception track to acquire the unmanned aerial vehicle false position information and the unmanned aerial vehicle false area information.
5. The unmanned aerial vehicle navigation spoofing method of claim 1, wherein the processing the unmanned aerial vehicle spoofing trajectory and the unmanned aerial vehicle false position information to obtain a first unmanned aerial vehicle spoofing signal comprises:
S31, processing the unmanned aerial vehicle deception track by utilizing the unmanned aerial vehicle false position information to obtain unmanned aerial vehicle speed information;
s32, packaging the false position information of the unmanned aerial vehicle and the speed information of the unmanned aerial vehicle to obtain false motion data of the unmanned aerial vehicle;
And S33, modulating the false motion data of the unmanned aerial vehicle to obtain the first unmanned aerial vehicle deception signal.
6. The unmanned aerial vehicle navigation spoofing method of claim 1, wherein the processing the first unmanned aerial vehicle spoofing signal with a signal simulator to obtain a second unmanned aerial vehicle spoofing signal and transmitting the second unmanned aerial vehicle spoofing signal to the unmanned aerial vehicle comprises;
S41, acquiring a real GPS signal of the unmanned aerial vehicle;
S42, adjusting the power, the code phase and the Doppler frequency shift of the first unmanned aerial vehicle deception signal by using the signal simulator, so that the first unmanned aerial vehicle deception signal is synchronous with the real GPS signal, and the second unmanned aerial vehicle deception signal is obtained;
S43, acquiring a loop updating interval of the GPS receiver of the unmanned aerial vehicle to obtain a deception signal transmitting time slot;
and S44, sending the second unmanned aerial vehicle deception signal to the unmanned aerial vehicle in the deception signal transmission time slot by using the signal simulator.
7. The unmanned aerial vehicle navigation spoofing method of claim 1, wherein processing the second unmanned aerial vehicle spoofing signal to obtain second unmanned aerial vehicle position information and second unmanned aerial vehicle speed information comprises:
S51, demodulating the second unmanned aerial vehicle deception signal by using the GPS receiver of the unmanned aerial vehicle to obtain a second deception signal information set;
And S52, analyzing the second deception signal information set to obtain the second unmanned aerial vehicle position information and the second unmanned aerial vehicle speed information.
8. The unmanned aerial vehicle navigation spoofing method of claim 1, wherein the correcting the second unmanned aerial vehicle position information and the second unmanned aerial vehicle speed information to obtain third unmanned aerial vehicle position information and third unmanned aerial vehicle speed information comprises:
S61, using a pseudo-range generation model to process the second unmanned aerial vehicle position information to obtain the second unmanned aerial vehicle deception signal pseudo-range;
The pseudo-range generation model is as follows:
Wherein ρ is the pseudo range of the satellite to the GPS receiver of the unmanned aerial vehicle, (X k,Yk,Zk) is the second unmanned aerial vehicle position coordinate, (X j,Yj,Zj) is the coordinate of the satellite participating in position calculation, δ b is the total equivalent distance between the satellite and the GPS receiver which can be eliminated, orbit error and atmospheric delay error, δ k is the total equivalent distance between the random error which cannot be eliminated and the signal simulator performance error;
S62, processing the deception signal pseudo range of the second unmanned aerial vehicle to obtain a second deception signal pseudo range error dP;
S63, acquiring the second unmanned aerial vehicle deception signal positioning error by utilizing the relation between the pseudo-range error dP and the false positioning error dR;
the relation expression of the pseudo-range error dP and the false positioning error dR is as follows:
dP＝H·dR
Wherein, N is the number of satellites, (X k,Yk,Xk) is the coordinate position of the unmanned aerial vehicle, (X j,Yj,Zj) is the position of the satellites,A geometric distance between the satellite and the unmanned aerial vehicle;
the pseudo-range error expression of the satellite is:
Wherein N is the number of satellites;
The covariance matrix of the false positioning error dR is as follows:
Wherein, For the location coordinate variance of the second unmanned aerial vehicle spoofing signal, (σ XY,σXZ,σYX,σYZ,σZX,σZY) for the location coordinate covariance of the second unmanned aerial vehicle spoofing signal;
s64, using false positioning error estimation algorithm to estimate false positioning error caused by the pseudo range error Solving to obtain the estimated value/>, of the positioning error of the spoofing signal of the second unmanned aerial vehicle
S65, acquiring a positioning error sigma M of the GPS receiver of the unmanned aerial vehicle;
S66, processing the variance of the positioning coordinates of the second unmanned aerial vehicle spoofing signal by using a positioning error precision factor to obtain the second unmanned aerial vehicle spoofing signal positioning error;
the positioning error precision factor expression is:
Wherein, Positioning coordinate variance of the spoofing signal for the second unmanned aerial vehicle;
s67, using pseudo-range measurement error and false positioning error estimation value Processing the second unmanned aerial vehicle spoofing signal positioning error sigma R and the positioning error sigma M of the unmanned aerial vehicle GPS receiver to obtain a combined navigation system spoofing positioning error radius delta F;
The integrated navigation system spoofing positioning error radius δ F expression is:
Wherein σ R is the positioning error of the second unmanned aerial vehicle spoofing signal, and σ M is the positioning error of the unmanned aerial vehicle GPS receiver;
and S68, carrying out fusion processing on the second unmanned aerial vehicle position information, the second unmanned aerial vehicle speed information and the positioning error to obtain the third unmanned aerial vehicle position information and the third unmanned aerial vehicle speed information.
9. The unmanned aerial vehicle navigation spoofing method of claim 1, wherein the determining the spoofing validity of the third unmanned aerial vehicle location information by using a spoofing effect evaluation algorithm, to obtain a spoofing validity determination result, comprises:
S81, processing the third unmanned aerial vehicle position information by using the deception effect evaluation algorithm to obtain a deception effect detection value;
The deception effect evaluation algorithm expression is:
Wherein S k＝HkPk|k-1Hk T+Rk,εk is a deception effect detection value, Z k is the third unmanned plane position information, H k is a k-moment system measurement matrix, S k is an information covariance matrix and R k is a measurement noise variance matrix;
s82, acquiring an alarm threshold epsilon * of a pixhawk module of the unmanned aerial vehicle;
S83, judging whether the deception effect detection value is smaller than or equal to the alarm threshold, and obtaining the deception effectiveness judgment result.
10. An unmanned aerial vehicle spoofing device, the device comprising:
A memory storing executable program code;
A processor coupled to the memory;
The processor invokes the executable program code stored in the memory to perform the unmanned aerial vehicle navigation spoofing method of any of claims 1-9.
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