CN110855342B - Control method and device for unmanned aerial vehicle communication safety, electronic equipment and storage medium - Google Patents

Control method and device for unmanned aerial vehicle communication safety, electronic equipment and storage medium Download PDF

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CN110855342B
CN110855342B CN201911035322.9A CN201911035322A CN110855342B CN 110855342 B CN110855342 B CN 110855342B CN 201911035322 A CN201911035322 A CN 201911035322A CN 110855342 B CN110855342 B CN 110855342B
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aerial vehicle
unmanned aerial
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CN110855342A (en
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高莹
唐洪莹
李宝清
袁晓兵
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Shanghai Institute of Microsystem and Information Technology of CAS
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Shanghai Institute of Microsystem and Information Technology of CAS
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18502—Airborne stations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/02—Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]

Abstract

The application relates to a control method, a device, electronic equipment and a storage medium for unmanned aerial vehicle communication safety, wherein the method determines a first non-convex problem model based on a flight path limiting condition, a transmitting power limiting condition, a legal user position and an illegal user position of an unmanned aerial vehicle; converting the first non-convex problem model into a second non-convex problem model according to the average privacy rate function under the worst condition; determining a power sub-problem model based on the current flight track and a flight track sub-problem model based on the current transmitting power based on the second non-convex problem model; and determining the target transmitting power and the target flight path of the unmanned aerial vehicle based on the power subproblem model and the flight path subproblem model. According to the method, the uncertainty of the user position in the secret communication system of the unmanned aerial vehicle is considered, the maximum average secret rate of a legal user under the worst condition is obtained by optimizing the flight track and the transmitting power of the unmanned aerial vehicle in the given flight time, and therefore the safe transmission of information can be effectively guaranteed.

Description

Control method and device for unmanned aerial vehicle communication safety, electronic equipment and storage medium
Technical Field
The application relates to the technical field of wireless communication, in particular to a control method and device for communication safety of an unmanned aerial vehicle, electronic equipment and a storage medium.
Background
In recent years, unmanned aerial vehicles with the advantages of controllable mobility, flexible deployment, low cost and the like are widely used in civil and commercial fields, such as rescue after disaster, environmental monitoring, cargo transportation, power inspection and the like. In addition, the possibility of establishing a line-of-sight (LoS) communication link between the drone and a ground user is very high, so that the drone can be deployed as an aerial wireless communication platform, such as a base station, a relay, an access point, and the like, to improve the performance of an existing cellular network or provide temporary communication service for equipment in a disaster area. Although many attractive advantages of drones exist, the broadcast nature of the LoS link makes the transmitted information easily eavesdropped by unauthorized terrestrial users, which presents a serious challenge to the information security of drone-to-ground communications. In conventional wireless communication systems, key encryption techniques are used to protect the transmission of data. However, the encryption technique has high computational complexity and high energy consumption. Moreover, with the rapid development of computing technology, keys are continuously cracked. Therefore, the key encryption technology can only protect the transmitted confidential information to a certain extent, and the security constructed by the key encryption technology is limited. As an effective alternative to the conventional encryption technology, the physical layer security technology does not rely on a secret key, and utilizes the inherent characteristics of a wireless channel from the viewpoint of information theory to realize the secure transmission of information. The secret rate is an important index for measuring the security of the physical layer, and is defined as: under the condition of existence of an illegal eavesdropping end, the maximum rate of complete secret transmission can be realized from a legal sending end to a legal receiving end. The secret rate is numerically equal to the difference between the data transmission rate obtained by the legitimate receiver and the data transmission rate obtained by the illegitimate eavesdropping peer.
The existing unmanned aerial vehicle secret communication research makes full use of the high controllable mobility of the unmanned aerial vehicle, and the average secret speed of the system is maximized by jointly designing the track and the communication resources of the unmanned aerial vehicle. Since the eavesdropper usually hides the existence of the eavesdropper, the situation that the unmanned aerial vehicle only grasps partial position information of the eavesdropper is considered in partial research. However, existing work assumes that the drone knows the exact location of the legitimate recipient. In practice, it is difficult to obtain absolutely accurate position information, and its corresponding hardware cost and power consumption are also enormous. Due to the limited accuracy of positioning technologies such as Global Positioning System (GPS), the position of a legal receiver grasped by an unmanned aerial vehicle has estimation errors, and the uncertainty of the position of the legal receiver in the unmanned aerial vehicle secure communication system is not considered in the prior art, so the calculated security rate may not be the actual maximum security rate in the worst case. In addition, the unmanned aerial vehicle trajectory planning algorithm in the prior art mostly depends on a general solver, such as CVX, which has high computational complexity and takes a long time, so that it is necessary to construct a more efficient low-complexity algorithm.
Disclosure of Invention
The embodiment of the application provides a control method, a device, electronic equipment and a storage medium for communication safety of an unmanned aerial vehicle, and by considering the uncertainty of user positions in an unmanned aerial vehicle secret communication system, the average secret rate of a legal user under the worst condition is maximized by jointly optimizing the flight track and the transmitting power of the unmanned aerial vehicle in given flight time, so that the safe transmission of information can be effectively guaranteed.
In one aspect, an embodiment of the present application provides a control method for unmanned aerial vehicle communication security, including:
determining the maximum horizontal flying speed, the maximum peak power, the maximum average power, the flying time, the flying starting point position, the flying end point position, the legal user position and the illegal user position of the unmanned aerial vehicle;
determining a first non-convex problem model based on the maximum horizontal flying speed, the maximum peak power, the maximum average power, the flying time, the flying start position, the flying end position, the legal user position and the illegal user position of the unmanned aerial vehicle;
converting the first non-convex problem model into a second non-convex problem model according to the average privacy rate function under the worst condition;
determining a power sub-problem model based on the current flight track and a flight track sub-problem model based on the current transmitting power based on the second non-convex problem model;
and determining the target transmitting power and the target flight path of the unmanned aerial vehicle based on the power subproblem model and the flight path subproblem model.
On the other hand, an embodiment of the present application provides a control device for communication security, including:
the first determining module is used for determining the maximum horizontal flying speed, the maximum peak power, the maximum average power, the flying time, the flying starting point position, the flying end point position, the legal user position and the illegal user position of the unmanned aerial vehicle;
a second determining module, configured to determine a first non-convex problem model based on a maximum horizontal flying speed, a maximum peak power, a maximum average power, a flying time, a flying start position, a flying end position, a legal user position, and an illegal user position of the unmanned aerial vehicle;
a third determining module, configured to convert the first non-convex problem model into a second non-convex problem model according to the worst-case average secret rate function;
a fourth determination module, configured to determine, based on the second non-convex problem model, a power sub-problem model based on the current flight trajectory and a flight trajectory sub-problem model based on the current transmit power;
and the fifth determining module is used for determining the target transmitting power and the target flight path of the unmanned aerial vehicle based on the power subproblem model and the flight path subproblem model.
In another aspect, an embodiment of the present application provides an electronic device, where the electronic device includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the above-mentioned control method for unmanned aerial vehicle communication security.
In another aspect, an embodiment of the present application provides a computer-readable storage medium, where at least one instruction, at least one program, a code set, or a set of instructions is stored in the storage medium, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the above-mentioned control method for unmanned aerial vehicle communication security.
The control method, the device, the electronic equipment and the storage medium for unmanned aerial vehicle communication safety provided by the embodiment of the application have the following beneficial effects:
determining the maximum horizontal flying speed, the maximum peak power, the maximum average power, the flying time, the flying starting point position, the flying end point position, the legal user position and the illegal user position of the unmanned aerial vehicle; determining a first non-convex problem model based on the maximum horizontal flying speed, the maximum peak power, the maximum average power, the flying time, the flying start position, the flying end position, the legal user position and the illegal user position of the unmanned aerial vehicle; converting the first non-convex problem model into a second non-convex problem model according to the average privacy rate function under the worst condition; determining a power sub-problem model based on the current flight track and a flight track sub-problem model based on the current transmitting power based on the second non-convex problem model; and determining the target transmitting power and the target flight path of the unmanned aerial vehicle based on the power subproblem model and the flight path subproblem model. According to the method, the uncertainty of the user position in the unmanned aerial vehicle secret communication system is considered, the maximum average secret rate of a legal user under the worst condition can be obtained by solving the optimal flight track and the optimal transmitting power of the unmanned aerial vehicle in the given flight time, and therefore the safe transmission of information can be effectively guaranteed.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of a control method for communication security of an unmanned aerial vehicle according to an embodiment of the present application;
FIG. 3 is a diagram illustrating simulation results provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of a control device for communication security of an unmanned aerial vehicle according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application, including an unmanned aerial vehicle 101, a valid user 102, and an invalid user 103. In the application scenario, a channel model of the ground-to-ground wireless communication of the drone 101 is established, the drone 101 can provide data transmission services to the legitimate user 102 on the ground, and the illegitimate user 103 can eavesdrop data transmitted by the drone 101 to the legitimate user 102.
The method provided by the embodiment of the application determines the flight path limiting condition and the transmitting power limiting condition of the unmanned aerial vehicle 101, the position of a legal user 102 and the position of an illegal user 103; the flight path limiting conditions of the unmanned aerial vehicle 101 comprise the maximum horizontal flight speed, the flight time, the flight starting point position and the flight ending point position of the unmanned aerial vehicle 101, and the transmitting power limiting conditions comprise the maximum peak power and the maximum average power of the unmanned aerial vehicle 101; determining a first non-convex problem model based on the maximum horizontal flying speed, the maximum peak power, the maximum average power, the flying time, the flying start position, the flying end position, the position of the legal user 102 and the position of the illegal user 103; converting the first non-convex problem model into a second non-convex problem model according to the average privacy rate function under the worst condition; determining a power sub-problem model based on the current flight track and a flight track sub-problem model based on the current transmitting power based on the second non-convex problem model; and determining the target transmitting power and the target flight trajectory of the unmanned aerial vehicle 101 based on the power subproblem model and the flight trajectory subproblem model.
The positions of the legitimate user 102 and the illegitimate user 103 are both estimated positions obtained by the unmanned aerial vehicle 101, and have errors with the accurate positions. Therefore, the method in the embodiment of the application considers the uncertainty of the positions of the legal users 102 and the positions of the illegal users 103, and obtains the maximum average privacy rate of the legal users 102 under the worst condition by solving the optimal flight trajectory and the optimal transmitting power of the unmanned aerial vehicle 101 within the given flight time, so that the safe transmission of information can be effectively guaranteed.
Optionally, the drone 101, the legitimate user 102, and the illegitimate user 103 may all be single-antenna devices.
The following describes a specific embodiment of the control method for communication security of the unmanned aerial vehicle, and fig. 2 is a schematic flowchart of the control method for communication security of the unmanned aerial vehicle provided in the embodiment of the present application, and the present specification provides the method operation steps as in the embodiment or the flowchart, but may include more or fewer operation steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In practice, the system or server product may be implemented in a sequential or parallel manner (e.g., parallel processor or multi-threaded environment) according to the embodiments or methods shown in the figures. Specifically, as shown in fig. 2, the method may include:
s201: determining the maximum horizontal flying speed, the maximum peak power, the maximum average power, the flying time, the flying starting point position, the flying end point position, the legal user position and the illegal user position of the unmanned aerial vehicle.
In the embodiment of the application, the unmanned aerial vehicle, the legal user and the illegal user are single-antenna equipment. Determining the maximum horizontal flying speed of the unmanned aerial vehicle as VmaxMeters per second (m/s); the flight time of the unmanned aerial vehicle is T seconds(s), the unmanned aerial vehicle is divided into a plurality of equal-length time slots, and assuming that the duration of each time slot is δ (usually set to be 0.5s-1s), the total number of time slots is N ═ T/δ; based on this time slot division, the horizontal trajectory of the drone within the time of flight T may be represented as
Figure GDA0003037778650000061
Unmanned aerial vehicle's flight starting point position is q0The position of the flight end point is qF(ii) a The transmitting power of the unmanned plane in the nth time slot is p [ n ]]Maximum peak power value of PpeakMaximum average power value of
Figure GDA00030377786500000610
The accurate positions of the legal user and the illegal user are
Figure GDA0003037778650000062
Figure GDA0003037778650000063
i belongs to { D, E }, wherein D represents a legal user, and E represents an illegal user; the unmanned plane only masters the estimated position of the node i
Figure GDA0003037778650000064
Estimate error as riFrom this can be obtained
Figure GDA0003037778650000065
Figure GDA0003037778650000066
i E D, E, where | represents the euclidean norm,
Figure GDA0003037778650000067
representing the set of locations where node i may be located.
In the existing technology for guaranteeing the information transmission safety of the unmanned aerial vehicle, the position of the user is known through a Positioning technology such as a Global Positioning System (GPS), but the accuracy of the Positioning technology is limited, so that the position of the user known by the unmanned aerial vehicle has an estimation error. Therefore, the method provided by the embodiment of the application considers the uncertainty of the user position, and the calculation result is more accurate and effective.
S203: a first non-convex problem model is determined based on a maximum horizontal flying speed, a maximum peak power, a maximum average power, a flying time, a flying start position, a flying end position, a legal user position, and an illegal user position of the unmanned aerial vehicle.
In the embodiment of the application, maximum horizontal flying speed V based on unmanned aerial vehiclemaxMaximum peak power PpeakMaximum average power
Figure GDA0003037778650000068
Time of flight T, starting position of flight q0The flight end position qFLegal user location wDAnd an illegal user position wEThe determined first non-convex problem model may be represented by equation (1):
Figure GDA0003037778650000069
s.t.C1:‖q[1]-q0‖≤L,
C2:‖q[n+1]-q[n]‖≤L,n＝1,…,N-1,
C3:‖q[N]-qF‖≤L,
C4：
Figure GDA0003037778650000071
C5：
Figure GDA0003037778650000072
wherein, the total time slot number N is determined by the flight time T and the duration delta of a single time slot; rD[n]And RE[n]Indicating that it is never used in the nth time slotThe data transmission rate from the man-machine to the legal user D and the illegal user E belongs to { D, E };
Figure GDA0003037778650000073
L＝Vmaxδ, L represents the maximum horizontal flight distance of the drone within a time slot; C1-C3 represent flight trajectory constraints for the drone, and C4-C5 represent transmit power constraints for the drone. Due to the coupling of the variables q and p, the positions of the legal user D and the illegal user E are uncertain, and the formula (1) is a non-convex semi-infinite optimization problem.
S205: the first non-convex problem model is transformed into a second non-convex problem model according to a worst-case average privacy rate function.
In the embodiment of the application, a channel between the unmanned aerial vehicle and the ground node i obeys a free space path loss model. By obtaining the flying height H of the unmanned aerial vehicle, based on the flying height H, the flying time T and the legal user position wDAnd an illegal user position wEEstablishing a worst-case average secret rate function corresponding to the target flight trajectory q and the target transmitting power p, wherein the worst-case average secret rate function can be expressed as formula (2):
Figure GDA0003037778650000074
Figure GDA0003037778650000081
wherein, beta0/(H2+‖q[n]-wi‖2) Representing the channel gain between the drone and node i; beta is a0Representing the channel gain reference value at a distance of 1 meter; gamma ray0＝β0/σ2，σ2Representing additive white gaussian noise at node i. R is as defined aboveD[n]And RE[n]Can be determined according to equation (2).
In the embodiment of the present application, the first non-convex problem model is converted into the second non-convex problem model according to formula (2), and the second non-convex problem model can be expressed as formula (3):
Figure GDA0003037778650000082
where | x | represents taking the absolute value of x. It should be noted that by optimizing the transmit power of the drone in each timeslot, a non-negative privacy rate is always obtained, so]+Are omitted. In comparison with formula (1), formula (3) has an explicit expression.
S207: and determining a power subproblem model based on the current flight path and a flight path subproblem model based on the current transmitting power based on the second non-convex problem model.
In the embodiment of the application, the current flight track of the unmanned aerial vehicle is determined; converting the second non-convex problem model into a power sub-problem model according to the current flight track; by determining a current transmit power; and converting the second non-convex problem model into a flight path subproblem model based on the current transmitting power.
In the embodiment of the present application, the power sub-problem model may be expressed as formula (4):
Figure GDA0003037778650000083
wherein the content of the first and second substances,
Figure GDA0003037778650000084
Figure GDA0003037778650000085
in the embodiment of the present application, the flight path subproblem model can be expressed as formula (5):
Figure GDA0003037778650000086
Figure GDA0003037778650000091
wherein the content of the first and second substances,
Figure GDA0003037778650000092
s209: and determining the target transmitting power and the target flight path of the unmanned aerial vehicle based on the power subproblem model and the flight path subproblem model.
In the embodiment of the application, the preset flight track of the unmanned aerial vehicle is obtained; taking the preset flight track as the current flight track; and determining the transmitting power of the unmanned aerial vehicle according to the current flight track and the power subproblem model.
Specifically, the step of solving equation (4) by the KKT condition may include:
from equation (4) it can be determined: when a isn＜bnIn the meantime, the channel between the unmanned aerial vehicle and the illegal user E is superior to the channel between the unmanned aerial vehicle and the legal user D, the illegal user E can steal and hear all confidential information, and at the moment, the unmanned aerial vehicle stops transmitting the information, namely p*[n]0; when a isn≥bnEquation (4) is a convex optimization problem. In summary, equation (6) can be derived from the KKT condition:
Figure GDA0003037778650000093
wherein the content of the first and second substances,
Figure GDA0003037778650000094
in equation (7), λ is a lagrange multiplier with respect to constraint C4. Based on the complementary relaxation theorem, there are
Figure GDA0003037778650000095
Thus, λ*The value of (d) can be determined by a binary search method. So, can try to get out unmanned aerial vehicle's transmitting power.
In the embodiment of the application, the transmitting power of the unmanned aerial vehicle is taken as the current transmitting power; and determining the flight track of the unmanned aerial vehicle according to the current transmitting power and the flight track subproblem model.
Specifically, the step of solving the formula (5) by using the successive convex approximation method and the alternating direction multiplier method may include: introducing relaxation variables
Figure GDA0003037778650000096
Converting equation (5) to equation (8):
Figure GDA0003037778650000097
s.t.C6:
Figure GDA0003037778650000098
C7:
Figure GDA0003037778650000099
C1-C3. ……(8)
obviously, the equality signs in constraints C6 and C7 hold when equation (8) achieves the optimal solution, otherwise by decreasing u [ n ]](or increase t [ n ]]) The objective function value can be further increased. Solving equation (5) is equal to solving equation (8). Log in the objective function2(1+γn/u[n]) And the left term of the constraint C7 inequality is with respect to u [ n ], respectively]And q [ n ]]Such that equation (8) is a non-convex problem. These two terms are replaced by their respective first order Taylor expansions according to the successive convex approximation method, thereby approximating equation (8) to a convex optimization problem. In particular, in the r-th iteration, for a given drone trajectory coordinate qr[n]And the relaxation variable ur[n]The following inequalities (9) and (10):
Figure GDA0003037778650000101
Figure GDA0003037778650000102
wherein q isr[n]And ur[n]The solution for the r-1 th iteration is taken.
From equation (9) and equation (10), equation (8) is approximated as equation (11):
Figure GDA0003037778650000103
s.t.C8：
Figure GDA0003037778650000104
C1-C3,C6. ……(11)
wherein the content of the first and second substances,
Figure GDA0003037778650000105
equation (11) is a standard convex optimization problem, transforming equation (11) into an equivalent form that can be solved using an Alternating Directional Method of Multipliers (ADMM). First, define:
Figure GDA0003037778650000106
Figure GDA0003037778650000107
a feasible field of z is set as the field,
Figure GDA0003037778650000108
wherein [ ·]TDenotes transposition, 02(N-1)Representing a 2(N-1) -dimensional all 0 vector. Next, an appropriate matrix B e {0,1, -1} is constructed2(N+1)×2NThen, constraints C1-C3 may be equivalently represented as
Figure GDA0003037778650000109
Next, replication variables are introduced, defining:
Figure GDA00030377786500001010
equation (11) is equivalently converted to equation (12):
Figure GDA00030377786500001011
s.t.C9:q＝h,
C10:q＝m,
C11:
Figure GDA0003037778650000111
C12:
Figure GDA0003037778650000112
C13:
Figure GDA0003037778650000113
wherein the content of the first and second substances,
Figure GDA0003037778650000114
to convert equation (12) to a more compact form, constraints C9 and C10 are represented by Aq ═ C, where a ═ I2N；I2N]，I2NA unit vector of order 2N is represented,
Figure GDA0003037778650000115
then, equation (12) can be equivalently converted into equation (13):
Figure GDA0003037778650000116
s.t.
Figure GDA0003037778650000117
C14:Aq＝c,
C13. ……(13)
wherein the content of the first and second substances,
Figure GDA0003037778650000118
is the feasible field of (u, t, c). Introducing Lagrange multipliers beta and y and penalty factor rho1And ρ2The lagrangian function of equation (13) is given by equation (14):
Figure GDA0003037778650000119
according to the ADMM algorithm, dividing the variable { q, z, u, t, c, β, y } into three variable blocks and alternately iteratively updating the three variable blocks, the optimal solution of equation (13) can be obtained. Assume that the variable value obtained in the first iteration is ql,zl,ul,tl,cl,βl,ylThen the step of the (l + 1) th iteration may include:
(1) update { q, z }:
Figure GDA00030377786500001110
wherein the content of the first and second substances,
Figure GDA00030377786500001111
(2) update { u, t, c }: according to the definition of c, { u, t, c } can be divided into two parts { u, h } and { t, m }. The lagrange multiplier β is also decomposed into two parts accordingly: beta is a1And beta2. The update of { u, h } is equivalent to solving equation (16):
Figure GDA0003037778650000121
based on the KKT conditions, we obtained:
Figure GDA0003037778650000122
wherein v isnIs a lagrange multiplier for constraint C11 whose value can be determined by the complementary relaxation theorem and a binary search method.
The update of { t, m } is equivalent to solving equation (18):
Figure GDA0003037778650000123
based on the KKT conditions, we obtained:
Figure GDA0003037778650000124
wherein, munIs a lagrange multiplier for constraint C12 whose value can be determined by the complementary relaxation theorem and a binary search method.
In summary,
Figure GDA0003037778650000125
(3) update { β, y }:
βl+1＝βl+Aql+1-cl+1,
yl+1＝yl+Bql+1-e-zl+1. ……(20)
and (4) repeatedly executing the steps (1) to (3) until the convergence condition of the ADMM algorithm is reached or the iteration number exceeds a preset iteration number. From this, can try to get out the solution of flight path subproblem model, unmanned aerial vehicle's flight path promptly.
In the embodiment of the application, the average privacy rate under the current worst case of a legal user is determined according to the current transmitting power, the flight track of the unmanned aerial vehicle and the average privacy rate function under the worst case; determining an average privacy rate increase rate according to the average privacy rate under the current worst case of a legal user and the average privacy rate under the historical worst case of the legal user; if the average privacy rate increase rate is larger than the preset increase rate, taking the flight track of the unmanned aerial vehicle as the current flight track; repeating the steps: determining the transmitting power of the unmanned aerial vehicle according to the current flight track and the power subproblem model; taking the transmitting power of the unmanned aerial vehicle as the current transmitting power; determining the flight track of the unmanned aerial vehicle according to the current transmitting power and the flight track subproblem model; and if the average secret rate increase rate is less than or equal to the preset increase rate, determining the current transmitting power as the target transmitting power, and determining the flight track of the unmanned aerial vehicle as the target flight track.
In summary, the specific implementation process of step S209 is:
first, let iteration index r equal to 0, initialize unmanned aerial vehicle trajectory q0The initialization scheme may be: unmanned aerial vehicle is with maximum airspeed VmaxFlies straight to the right above the legal user D from a specified starting point, hovers there as long as possible, and finally flies at the maximum flying speed VmaxFlying from the suspension point to the end point of the pointing;
secondly, at a given drone trajectory qrIn case of (2), the transmission power p is updated according to the power subproblem modelr+1(ii) a For example, initially, when r is 0, then the trajectory q is given according to the drone trajectory q0And equation (4) determining the transmit power p1；
Secondly, according to the transmission power pr+1Determining flight path q by using flight path subproblem modelr+1(ii) a In the process of solving the flight path subproblem model, the iteration index l is made to be 0, and the maximum iteration number I of the ADMM inner loop is setADMM. Initialization variable q0,z0,u0,t0,c0,β0,y0}. As a penalty factor p1And ρ2Assigning; update { q ] according to equation (15)l+1,zl+1}. Update { u ] according to equation (17) and equation (19)l+1,tl+1,cl+1}. Update { beta ] according to equation (20)l+1,yl+1}. Updating an iteration index: l + 1. Repeatedly executing the steps to update the parameter value until reaching the convergence condition or l of the ADMM algorithm>IADMMUpdating the unmanned aerial vehicle track: q. q.sr+1＝ql(ii) a Continuing with the description based on the above example, according to the transmission power p1And equation (5) determining the flight trajectory q1According to the current transmission power p1Current flight path q1And equation (2) determining the current worst-case average privacy rate R for legitimate user D1Since there is no historical worst case average privacy rate for legitimate user D at this time, the next step is performed;
second, the iteration index is updated: r +1, repeating the above steps, i.e. at a given drone trajectory qr+1In the case ofDetermining the transmission power p from the power sub-problem modelr+2According to the transmission power pr+2Determining q by flight path subproblem modelr+2(ii) a Continuing the description based on the above example, the flight trajectory q is used1And equation (4) determining the transmit power p2According to the transmission power p2And equation (5) determining the flight trajectory q2According to the current transmission power p2Current flight path q2And equation (2) determining the current worst-case average privacy rate R for legitimate user D2At this time, the worst case average secret rate R is taken into account1An average privacy rate growth rate of (R) may be determined2-R1)/R1(ii) a Suppose (R)2-R1)/R1If the increase rate is larger than the preset increase rate, the steps are continuously executed, and the flying track q is used2And equation (4) determining the transmit power p3According to the transmission power p3And equation (5) determining the flight trajectory q3According to the current transmission power p3Current flight path q3And equation (2) determining the current worst-case average privacy rate R for legitimate user D3At this time, the worst case average secret rate R is taken into account2An average privacy rate growth rate of (R) may be determined3-R2)/R2Suppose that at this time (R)3-R2)/R2Less than or equal to the preset growth rate, and determining the current transmitting power p3Determining the flight trajectory q of the drone for the target launch power3Is the target flight path.
The control method for the communication safety of the unmanned aerial vehicle can be realized by MATLAB simulation. Referring to fig. 3, fig. 3 is a schematic diagram of a simulation experiment result according to an embodiment of the present application.
The simulation parameters are set as follows: the estimated positions of the legitimate user D and the illegitimate user E are respectively
Figure GDA0003037778650000141
Figure GDA0003037778650000142
The estimation errors are respectivelyrD＝10m，rE30 m. The pointed flight starting point and the pointed flight end point of the unmanned aerial vehicle are q respectively0＝[50,200]T，qF＝[50,-200]T. The flying height of the unmanned aerial vehicle is H-80 m, and the maximum horizontal flying speed is Vmax10 m/s. Further, δ is 0.5s, γ0＝80dB，
Figure GDA0003037778650000144
The preset growth rate is 10-4. Time of flight T and maximum average power value
Figure GDA0003037778650000145
The setting may be made according to the contents of the emulation. For convenience of explanation, the optimization algorithm proposed in the present application is abbreviated as Robust-ADMM, and the present application will also introduce other three comparison schemes, including:
Robust-CVX: this scheme differs from Robust-ADMM in that: the scheme adopts a CVX toolkit based on an interior point method solver SDP3 to solve formula (5), and the Robust-ADMM adopts an ADMM algorithm to solve formula (13) after equivalence transformation.
Non-robust-ADMM: in this scheme, the drone only grasps the estimated locations of legitimate and illegitimate users, but treats them as exact locations. The scheme utilizes the algorithm provided by the application to solve the optimization problem and assumes rD＝0，rE＝0。
Best-effort: the unmanned aerial vehicle orbit design of this scheme adopts the orbit initialization scheme of this application, then sets up unmanned aerial vehicle's transmitting power according to formula (6).
As shown in fig. 3(a), the maximum average power value is 80s at the time of flight T
Figure GDA0003037778650000143
Under the condition, the unmanned aerial vehicle track and the transmission power design result obtained by the optimization scheme and the other three comparison schemes are obtained. As can be seen from the figure, the optimization results of Robust-ADMM and Robust-CVX are consistent, and the change trend of the trajectory and the emission power obtained by Non-Robust-ADMM is similar to those of the two schemes. Comparison of RoThe tracks of the bust-ADMM and Best-efort show that the optimal suspension point is not directly above the legitimate user D. In addition, it can be seen from fig. 3(b) that the optimization results of power and trajectory are closely related, indicating the necessity of joint optimization of power and trajectory.
The worst-case average privacy rates and simulation times for the optimization scheme proposed by the present application and other comparison schemes are shown in fig. 3(c) and 3(d) at different single times of flight. As can be seen from fig. 3(c), the worst-case average privacy rate for all schemes increases with increasing single-flight time. The optimization results for Robust-ADMM and Robust-CVX are almost the same for the worst case average privacy rate and are both better than Non-Robust-ADMM and Best-efort. It is clear that ignoring the position estimation errors of legitimate and illegitimate users introduces a performance penalty to Non-robust-ADMM. It should be noted that FIG. 3(d) does not show the simulation time of Best-effort because it is much smaller than the other three schemes. As can be seen from fig. 3(d), the simulation time curves for all the schemes generally show an upward trend as T increases, and the simulation times for Robust-ADMM and Non-Robust-ADMM are much smaller than for Robust-CVX. The Robust-ADMM can achieve the worst-case average privacy rate almost the same as the Robust-CVX, but the simulation time is shortened by 60-140 times.
The effectiveness of the application is verified by the simulation experiment result of fig. 3, which shows that the application can effectively ensure the safe transmission of confidential information, and has high calculation efficiency and easy implementation.
This application embodiment still provides an unmanned aerial vehicle communication safety's controlling means, and fig. 4 is the unmanned aerial vehicle communication safety's that this application embodiment provided controlling means's schematic structure diagram, as shown in fig. 4, the device includes:
a first determining module 401, configured to determine a maximum horizontal flying speed, a maximum peak power, a maximum average power, a flying time, a flying start position, a flying end position, a valid user position, and an invalid user position of the unmanned aerial vehicle;
a second determining module 402, configured to determine a first non-convex problem model based on a maximum horizontal flying speed, a maximum peak power, a maximum average power, a flying time, a flying start position, a flying end position, a legal user position, and an illegal user position of the unmanned aerial vehicle;
a third determining module 403, configured to convert the first non-convex problem model into a second non-convex problem model according to the worst-case average secret rate function;
a fourth determining module 404, configured to determine, based on the second non-convex problem model, a power sub-problem model based on the current flight trajectory and a flight trajectory sub-problem model based on the current transmit power;
a fifth determining module 405, configured to determine a target transmit power and a target flight trajectory of the drone based on the power subproblem model and the flight trajectory subproblem model.
The device and method embodiments in the embodiments of the present application are based on the same application concept.
The embodiment of the application also provides electronic equipment, which comprises a processor and a memory, wherein at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to realize the control method for the communication security of the unmanned aerial vehicle.
The embodiment of the present application further provides a storage medium, where the storage medium may be disposed in a server to store at least one instruction, at least one program, a code set, or an instruction set related to a control method for implementing communication security of a drone in the method embodiments, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the control method for communication security of a drone.
Alternatively, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
As can be seen from the embodiments of the method, the apparatus, the electronic device, or the storage medium for controlling communication security of the unmanned aerial vehicle provided by the present application, in the present application, the maximum horizontal flight speed, the maximum peak power, the maximum average power, the flight time, the flight starting point position, the flight ending point position, the valid user position, and the invalid user position of the unmanned aerial vehicle are determined; determining a first non-convex problem model based on the maximum horizontal flying speed, the maximum peak power, the maximum average power, the flying time, the flying start position, the flying end position, the legal user position and the illegal user position of the unmanned aerial vehicle; converting the first non-convex problem model into a second non-convex problem model according to the average privacy rate function under the worst condition; determining a power sub-problem model based on the current flight track and a flight track sub-problem model based on the current transmitting power based on the second non-convex problem model; and determining the target transmitting power and the target flight path of the unmanned aerial vehicle based on the power subproblem model and the flight path subproblem model. According to the method, the uncertainty of the user position in the unmanned aerial vehicle secret communication system is considered, the maximum average secret rate of a legal user under the worst condition can be obtained by solving the optimal flight track and the optimal transmitting power of the unmanned aerial vehicle in the given flight time, and therefore the safe transmission of information can be effectively guaranteed.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (10)

1. A control method for unmanned aerial vehicle communication safety is characterized by comprising the following steps:
determining the maximum horizontal flying speed, the maximum peak power, the maximum average power, the flying time, the flying starting point position, the flying end point position, the legal user position and the illegal user position of the unmanned aerial vehicle;
determining a first non-convex problem model based on the maximum horizontal flying speed, the maximum peak power, the maximum average power, the flying time, the flying start position, the flying end position, the valid user position, and the invalid user position of the unmanned aerial vehicle; the first non-convex problem model is:
Figure FDA0003268757320000011
s.t.C1：||q[1]-q0||≤L，
C2：||q[n+1]-q[n]||≤L，n＝1，…，N-1，
C3：||q[N]-qF||≤L，
Figure FDA0003268757320000012
Figure FDA0003268757320000013
wherein the content of the first and second substances,
Figure FDA0003268757320000014
d denotes a legitimate user, E denotes an illegitimate user,
Figure FDA0003268757320000015
representing the estimated position of node i with an estimation error of ri；
Figure FDA0003268757320000016
Represents a set of locations where node i may be located; ppeakWhich represents the maximum peak power of the power amplifier,
Figure FDA0003268757320000017
representing maximum average power, T representing time of flight, q0Indicating the location of the origin of flight, qFIndicating a flight end position; n ═ T/δ, N denoting the total number of time slots, T denoting the time of flight, δ denoting the duration of a single time slot; rD[n]And RE[n]Indicating the data transmission rate from the unmanned aerial vehicle to the legal user D and the illegal user E in the nth time slot;
Figure FDA0003268757320000018
L＝Vmaxδ, L represents the maximum horizontal flight distance of the drone in a time slot, VmaxRepresents the maximum horizontal flying speed; C1-C3 represent flight trajectory constraints of the drone, C4-C5 represent transmit power constraints of the drone; q [ n ]]Indicating the horizontal position of the drone in the nth slot,
Figure FDA0003268757320000019
n＝1，...，N；p[n]indicating the transmitting power of the unmanned plane in the nth time slot; w is aiRepresents the precise location of user i;
Figure FDA00032687573200000110
i belongs to { D, E }, wherein D represents a legal user, and E represents an illegal user;
converting the first non-convex problem model into a second non-convex problem model according to an average privacy rate function under the worst condition; the second non-convex problem model is:
Figure FDA0003268757320000021
s.t.C1-C5.
determining a power sub-problem model based on the current flight trajectory and a flight trajectory sub-problem model based on the current transmission power based on the second non-convex problem model;
and determining the target transmitting power and the target flight path of the unmanned aerial vehicle based on the power subproblem model and the flight path subproblem model.
2. The method of claim 1, wherein transforming the first non-convex problem model into a second non-convex problem model according to a worst-case average privacy rate function comprises:
acquiring the flight height of the unmanned aerial vehicle;
establishing an average secret rate function under the worst condition corresponding to a target flight track and target transmitting power based on the flight altitude, the flight time, the legal user position and the illegal user position;
and converting the first non-convex problem model into a second non-convex problem model according to the worst-case average privacy rate function.
3. The method of claim 1, wherein determining a current flight trajectory based power sub-problem model and a current transmit power based flight trajectory sub-problem model based on the second non-convex problem model comprises:
determining the current flight track of the unmanned aerial vehicle;
converting the second non-convex problem model into the power sub-problem model according to the current flight trajectory;
determining the current transmitting power;
and converting the second non-convex problem model into the flight trajectory sub-problem model based on the current transmitting power.
4. The method of claim 3, wherein determining the target transmit power and the target flight trajectory of the drone based on the power subproblem model and the flight trajectory subproblem model comprises:
acquiring a preset flight track of the unmanned aerial vehicle;
taking the preset flight track as the current flight track;
determining the transmitting power of the unmanned aerial vehicle according to the current flight track and the power subproblem model;
taking the transmitting power of the unmanned aerial vehicle as the current transmitting power;
determining the flight track of the unmanned aerial vehicle according to the current transmitting power and the flight track subproblem model;
determining the current worst-case average secret rate of a legal user according to the current transmitting power, the flight path of the unmanned aerial vehicle and the worst-case average secret rate function;
determining an average privacy rate increase rate according to the average privacy rate under the current worst case condition of the legal user and the average privacy rate under the historical worst case condition of the legal user;
if the average privacy rate increase rate is larger than a preset increase rate, taking the flight track of the unmanned aerial vehicle as the current flight track; repeating the steps: determining the transmitting power of the unmanned aerial vehicle according to the current flight track and the power subproblem model; taking the transmitting power of the unmanned aerial vehicle as the current transmitting power; determining the flight track of the unmanned aerial vehicle according to the current transmitting power and the flight track subproblem model;
and if the average secret rate increase rate is less than or equal to a preset increase rate, determining the current transmitting power as a target transmitting power, and determining the flight track of the unmanned aerial vehicle as a target flight track.
5. The utility model provides a controlling means of unmanned aerial vehicle communication safety which characterized in that includes:
the first determining module is used for determining the maximum horizontal flying speed, the maximum peak power, the maximum average power, the flying time, the flying starting point position, the flying end point position, the legal user position and the illegal user position of the unmanned aerial vehicle;
a second determining module, configured to determine a first non-convex problem model based on a maximum horizontal flying speed of the unmanned aerial vehicle, the maximum peak power, the maximum average power, the flying time, the flying start position, the flying end position, the valid user position, and the invalid user position; the first non-convex problem model is:
Figure FDA0003268757320000041
s.t.C1：||q[1]-q0||≤L，
C2：||q[n+1]-q[n]||≤L，n＝1，…，N-1，
C3：||q[N]-qF||≤L，
Figure FDA0003268757320000042
Figure FDA0003268757320000043
wherein the content of the first and second substances,
Figure FDA0003268757320000044
d denotes a legitimate user, E denotes an illegitimate user,
Figure FDA0003268757320000045
representing the estimated position of node i with an estimation error of ri；
Figure FDA0003268757320000046
Represents a set of locations where node i may be located; ppeakWhich represents the maximum peak power of the power amplifier,
Figure FDA0003268757320000047
representing maximum average power, T representing time of flight, q0Indicating the location of the origin of flight, qFIndicating a flight end position; n ═ T/δ, N denoting the total number of time slots, T denoting the time of flight, δ denoting the duration of a single time slot; rD[n]And RE[n]Indicating the data transmission rate from the unmanned aerial vehicle to the legal user D and the illegal user E in the nth time slot;
Figure FDA0003268757320000048
L＝Vmaxδ, L represents the maximum horizontal flight distance of the drone in a time slot, VmaxRepresents the maximum horizontal flying speed; C1-C3 represent flight trajectory constraints of the drone, C4-C5 represent transmit power constraints of the drone; q [ n ]]Indicating the horizontal position of the drone in the nth slot,
Figure FDA0003268757320000049
n＝1，...，N；p[n]indicating the transmitting power of the unmanned plane in the nth time slot; w is aiRepresents the precise location of user i;
Figure FDA00032687573200000410
i belongs to { D, E }, wherein D represents a legal user, and E represents an illegal user;
a third determining module, configured to convert the first non-convex problem model into a second non-convex problem model according to a worst-case average secret rate function; the second non-convex problem model is:
Figure FDA00032687573200000411
s.t.C1-C5.
a fourth determination module, configured to determine, based on the second non-convex problem model, a power sub-problem model based on a current flight trajectory and a flight trajectory sub-problem model based on a current transmit power;
and the fifth determining module is used for determining the target transmitting power and the target flight path of the unmanned aerial vehicle based on the power subproblem model and the flight path subproblem model.
6. The apparatus of claim 5,
the third determining module is further configured to obtain a flight altitude of the unmanned aerial vehicle; establishing an average secret rate function corresponding to a target flight track and target transmitting power under the worst condition based on the flight altitude and the legal user position; and converting the first non-convex problem model into a second non-convex problem model according to the worst-case average privacy rate function.
7. The apparatus of claim 5,
the fourth determining module is further configured to determine a current flight trajectory of the unmanned aerial vehicle; converting the second non-convex problem model into the power sub-problem model according to the current flight trajectory; determining the current transmitting power; and converting the second non-convex problem model into the flight trajectory sub-problem model based on the current transmitting power.
8. The apparatus of claim 7,
the fifth determining module is further configured to obtain a preset flight trajectory of the unmanned aerial vehicle; taking the preset flight track as the current flight track; determining the transmitting power of the unmanned aerial vehicle according to the current flight track and the power subproblem model; taking the transmitting power of the unmanned aerial vehicle as the current transmitting power; determining the flight track of the unmanned aerial vehicle according to the current transmitting power and the flight track subproblem model; determining the current worst-case average secret rate of a legal user according to the current transmitting power, the flight path of the unmanned aerial vehicle and the worst-case average secret rate function; determining an average privacy rate increase rate according to the average privacy rate under the current worst case condition of the legal user and the average privacy rate under the historical worst case condition of the legal user;
the fifth determining module is further configured to take the flight trajectory of the unmanned aerial vehicle as the current flight trajectory if the average privacy rate increase rate is greater than a preset increase rate; repeating the steps: determining the transmitting power of the unmanned aerial vehicle according to the current flight track and the power subproblem model; taking the transmitting power of the unmanned aerial vehicle as the current transmitting power; determining the flight track of the unmanned aerial vehicle according to the current transmitting power and the flight track subproblem model;
the fifth determining module is further configured to determine that the current transmit power is a target transmit power and determine that the flight trajectory of the unmanned aerial vehicle is a target flight trajectory if the average secret rate increase rate is less than or equal to a preset increase rate.
9. An electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by the processor to implement the method for controlling drone communication security according to any one of claims 1-4.
10. A computer-readable storage medium, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored, loaded and executed by a processor to implement the method of controlling drone communication security of any one of claims 1-4.
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