CN111565395B - Unmanned aerial vehicle obstacle avoidance and three-dimensional deployment method for enhancing cellular mobile communication safety - Google Patents

Unmanned aerial vehicle obstacle avoidance and three-dimensional deployment method for enhancing cellular mobile communication safety Download PDF

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CN111565395B
CN111565395B CN202010326011.4A CN202010326011A CN111565395B CN 111565395 B CN111565395 B CN 111565395B CN 202010326011 A CN202010326011 A CN 202010326011A CN 111565395 B CN111565395 B CN 111565395B
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CN111565395A (en
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王东
张明义
王涛
陈卫
黄克明
李猛
汪玉美
程龙
郑臣
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PLA Army Academy of Artillery and Air Defense
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Abstract

The invention discloses an unmanned aerial vehicle obstacle avoidance and three-dimensional deployment method for enhancing cellular mobile communication safety, which comprises the steps of firstly carrying out mathematical modeling on problems, then gradually converting the problems into quadratic fractional planning, and then equivalently converting the problems into quadratic planning by skillfully defining intermediate variables; then carrying out variable substitution, and equivalently converting the quadratic programming into a semi-definite relaxation problem; and finally, omitting the constraint on the matrix rank to obtain a semi-definite programming problem, and solving the problem by using a semi-definite programming correlation algorithm. Since the solution obtained by semi-definite programming may not be optimal for the original problem, an approximate solution of the original problem needs to be extracted. The method has the advantages that a complex non-convex dynamic position optimization problem is finally converted into a simple convex semi-definite programming problem, the given solving steps are simple and easy to realize, and the probability of non-zero secret rate can be obviously improved.

Description

Unmanned aerial vehicle obstacle avoidance and three-dimensional deployment method for enhancing cellular mobile communication safety
Technical Field
The invention relates to the technical field of mobile communication, in particular to an unmanned aerial vehicle obstacle avoidance and three-dimensional deployment method for enhancing cellular mobile communication safety.
Background
The unmanned aerial vehicle has unique advantages and can be widely applied to a 5G mobile communication network. Because the mobility of the unmanned aerial vehicle is completely controllable, the flight height can be adjusted in a self-adaptive manner, the use cost is low, the coverage range is wide, and the deployment as required can be realized, the unmanned aerial vehicle can be used for carrying an aerial base station to enhance the coverage range, transmission capacity, reliability, safety and energy efficiency of cellular mobile communication. For example, the drone can be applied to cellular mobile communication to support some temporary and bursty communication services, such as emergency communication after disaster, ultra-dense user data distribution in a hot spot area, temporary communication service establishment in a complex terrain, and the like.
However, due to the broadcast nature of the ground-to-air wireless link, the wide range of coverage signals emitted by the drone base station is more likely to be eavesdropped by a potential eavesdropper on the ground. Therefore, cellular mobile communications based on drones face more serious safety issues. How to ensure that such a drone communication network is not subject to eavesdropping is a very challenging problem. To solve this problem, physical layer security is considered as a very promising information security technology. The technology can flexibly match the dynamic change of the wireless channel through the real-time optimization design of the information transmission strategy, thereby achieving the purpose of optimal transmission. For example, a high degree of information confidentiality can be achieved through optimal design in aspects of security coding, resource allocation, signal processing, node cooperation, network deployment and the like.
In the unmanned aerial vehicle cellular mobile communication system, the completely controllable mobility and flexibility of the unmanned aerial vehicle provide new dimensionalities for the safety design of a physical layer, namely, the mobility and the controllability of the unmanned aerial vehicle are fully utilized, the spatial position of the unmanned aerial vehicle is flexibly adjusted along with the time lapse, and the purpose of confidential transmission is achieved. Therefore, much research work is focused on improving the performance of physical layer secure transport through suitable network node deployment or drone trajectory optimization. Most research work has employed secret rates to measure the performance of secure transmissions. However, calculating the real-time secret rate requires the sender to know the exact channel state information of the legitimate channel and the eavesdropped channel. However, accurate channel state information, particularly that of an eavesdropper, is difficult to acquire in many cases, in which case it is impracticable to optimize the privacy rate in real time to improve information security.
Disclosure of Invention
The invention aims to make up for the defects of the prior art and provides an unmanned aerial vehicle obstacle avoidance and three-dimensional deployment method for enhancing cellular mobile communication safety.
The invention mainly considers that in a temporary burst communication scene, the ground user is served by the base station carried by the low-altitude flying unmanned aerial vehicle, cellular mobile communication is realized, and the eavesdropper possibly exists in the ground user. The invention fully utilizes the mobility and controllability of the unmanned aerial vehicle, optimizes the space position of the unmanned aerial vehicle in real time according to the movement of a ground eavesdropper, and improves the probability that the system reaches the non-zero secret rate as much as possible, thereby improving the opportunity of implementing secret transmission of the system to the maximum extent. Specifically, the influence of factors such as the position change of an eavesdropper, the limitation of flight airspace and ground obstacles on the spatial deployment position of the unmanned aerial vehicle is considered, an optimization problem model about the spatial position of the unmanned aerial vehicle is established by taking the probability of the maximized non-zero secret rate as a target, and unmanned aerial vehicle obstacle avoidance and three-dimensional deployment strategies for enhancing the safety of a cellular mobile communication physical layer are provided by solving the problem.
The method provided by the invention is realized without knowing the accurate channel state information of a legal channel and an eavesdropping channel, and only needs to know the positions of a ground user and an eavesdropper. For airborne platforms equipped with both vision and positioning means, position acquisition by ground users and eavesdroppers is possible.
The invention is realized by the following technical scheme:
an unmanned aerial vehicle obstacle avoidance and three-dimensional deployment method for enhancing cellular mobile communication safety comprises the following specific steps:
(1) performing mathematical modeling on the problem;
(2) gradually converting the problem into quadratic fractional programming, and then equivalently converting the problem into quadratic programming by defining intermediate variables; then carrying out variable substitution, and equivalently converting quadratic programming into a semi-definite relaxation problem; finally, omitting the constraint on the matrix rank to obtain a semi-definite programming problem; solving the problem by using a semi-definite programming correlation algorithm;
(3) since the solution obtained by semi-definite programming may not be optimal for the original problem, an approximate solution of the original problem needs to be extracted. The extraction of the approximate solution can adopt a matrix characteristic decomposition method or a random sampling method.
The mathematical modeling of the problem in the step (1) comprises the following specific steps:
in a cellular mobile communication scene based on an unmanned aerial vehicle, a coordinate system is established by taking a ground user as a coordinate origin (0,0,0), the position of an eavesdropper is marked as (u, v,0), the position of an aerial unmanned aerial vehicle is marked as (x, y, z), the unmanned aerial vehicle positions the ground user and the eavesdropper to obtain the positions of the ground user and the eavesdropper relative to the coordinate origin, and then an optimization problem model is established; the factors considered for problem modeling are:
(1) a location of a ground eavesdropper;
(2) a horizontal airspace and a vertical airspace in which the unmanned aerial vehicle flies;
(3) ground obstacle position and characteristics;
the problem form of modeling is as follows:
Figure BDA0002463237770000031
wherein p issThe probability of a non-zero secret capacity is represented,
Figure BDA0002463237770000032
the effect of the eavesdropper location change on the secure transmission is reflected in psThe above step (1);
Figure BDA0002463237770000033
denotes a radius rmaxHorizontal spatial domain constraint of (2); h ismin≤z≤hmaxMeans lowest and highestFlying height of hminAnd hmaxVertical airspace constraint of (1);
Figure BDA0002463237770000034
k is 1,2, …, K represents the ground obstacle of the ellipsoid, and K is total, wherein (m) isk,nk0) is the center of the kth ellipsoid hemisphere, δk,βk,
Figure BDA0002463237770000035
The length of the half shaft of the elliptic hemisphere in the x, y and z coordinate axes respectively; α is a path loss exponent, and the above problem form (1) is equivalent to
Figure BDA0002463237770000036
Gradually converting the problem in the step (2), firstly converting the problem into quadratic fractional programming, and then equivalently converting the problem into quadratic programming by defining intermediate variables; then carrying out variable substitution, and equivalently converting quadratic programming into a semi-definite relaxation problem; finally, omitting the constraint on the matrix rank to obtain a semi-definite programming problem; the method comprises the following specific steps:
step1 is converted into quadratic programming: defining x as (x, y, z), problem (2) translates into an equivalent quadratic programming
Figure BDA0002463237770000041
Wherein e ═ u2+v2，a＝[-2u -2v 0]T，
Figure BDA0002463237770000042
d＝[0 0 1]T，
Figure BDA0002463237770000043
Step2 is converted into quadratic programming: variable substitution, definition
Figure BDA0002463237770000044
Figure BDA0002463237770000045
Problem (3) translates into the following equivalent quadratic programming
Figure BDA0002463237770000046
Step3 translates to the semi-definite relaxation problem: define variable substitution
Figure BDA0002463237770000047
And
Figure BDA0002463237770000048
problem (4) is equivalently converted into
Figure BDA0002463237770000051
Wherein the content of the first and second substances,
Figure BDA0002463237770000052
o3×1＝[0 0 0]T，
Figure BDA0002463237770000053
Figure BDA0002463237770000054
Figure BDA0002463237770000055
step4 is converted into semi-definite programming: neglecting the constraint rank (w) ═ 1, a semi-definite programming problem is obtained:
Figure BDA0002463237770000056
for problem (6), a semi-definite programming correlation algorithm is applied to find its optimal solution W*。
Extracting an approximate solution of the original problem in the step (3), which comprises the following specific steps: optimal solution W from problem (6)*There are two methods for extracting the approximate solution of the optimal solution of the original problem, one is the feature vector approximation method, the other is the random sampling method;
method of approximating a feature vector
The feature vector approximation method takes the optimal solution W of the problem (6)*And mapping the principal feature vector to a feasible set of the original problem;
let xi denote W*Rank of (d), i.e. ξ ═ rank (W)*) To W*Performing characteristic decomposition to obtain
Figure BDA0002463237770000061
Wherein gamma is1≥γ2≥…≥γξ> 0 is the characteristic value, rho, in descending order1，ρ2，…，ρξIs the corresponding feature vector, thus, for W*The best rank 1 approximation is
Figure BDA0002463237770000062
Due to the fact that
Figure BDA0002463237770000063
Obtaining x' as an approximate solution of the problem (3), thereby directly obtaining an approximate solution of the problem (2); if the feature is decomposed to obtain
Figure BDA0002463237770000064
If the problem (4) is not feasible, mapping the obtained infeasible solution to a close feasible solution of the problem (3);
(II) random sampling method
Optimal solution W to problem (6)*Is a symmetrical semi-positive definite matrix,generating a random vector mu obeying a mean of 0 covariance matrix of W*Is a Gaussian distribution of
Figure BDA0002463237770000065
Now consider the following random optimization problem
Figure BDA0002463237770000066
Wherein EμExpress a mathematical expectation about μ, resulting in Eμ{μμT}＝W*，Eμ{μTQμ}＝Tr{QW*}; the covariance matrix of the steering random vector mu is W*The problem (7) is made to hold in the mathematically expected sense, which means that an approximate solution to the problem (3) is obtained by generating the random vector μ a sufficient number of times.
The invention has the advantages that: the key points of the present invention are the stepwise transformation process of the problem, the extraction of approximate solutions, and the mapping of infeasible solutions to feasible solutions. The method has the advantages that a complex non-convex dynamic position optimization problem is finally converted into a simple convex semi-definite programming problem, the given solving steps are simple and easy to realize, and the probability of non-zero secret rate can be obviously improved. In addition, the invention can avoid the difficulty of obtaining accurate channel state information during the implementation, and only needs to obtain the position information of the eavesdropper through a positioning device carried by the platform.
Drawings
Fig. 1 is a diagram of cellular mobile communication scenario based on unmanned aerial vehicle in the invention.
Fig. 2 is a schematic diagram of a specific solving step for converting an original problem into semi-definite programming.
Fig. 3 is a diagram showing the simulation results of the probability of non-zero privacy capacity during the change of the eavesdropper location, where the eavesdropper moves on the line v-u + 50.
Fig. 4 is a graph showing a result of simulation of a position change curve of each communication node, where an eavesdropper moves on a straight line v-u + 50.
FIG. 5 is a non-zero security during a change in the eavesdropper's locationProbability of secret volume, eavesdropper on curve v ═ u2And 10-150.
FIG. 6 is a graph showing a change in the location of each communication node, where an eavesdropper is present on the curve v-u2And 10-150.
Detailed Description
The invention relates to an unmanned aerial vehicle obstacle avoidance and three-dimensional deployment method for enhancing cellular mobile communication safety. In order to achieve the purpose, firstly, mathematical modeling is carried out on the problem, then the problem is gradually transformed, firstly, the problem is transformed into quadratic fraction planning, and then, the problem is equivalently transformed into quadratic planning by skillfully defining intermediate variables; then carrying out variable substitution, and equivalently converting quadratic programming into a semi-definite relaxation problem; and finally, omitting the constraint on the matrix rank to obtain a semi-definite programming problem, and solving the problem by using a semi-definite programming correlation algorithm. Since the solution obtained by semi-definite programming may not be optimal for the original problem, an approximate solution of the original problem needs to be extracted. The extraction of the approximate solution can adopt a matrix characteristic decomposition method or a random sampling method.
Aiming at the sudden temporary communication service scene in the urban environment with densely distributed buildings, the invention provides the method for realizing the cellular mobile communication service by using the unmanned aerial vehicle flying at low altitude to install the base station, and the spatial position of the unmanned aerial vehicle is optimally deployed to resist the possible eavesdropping attack from the perspective of the physical layer safety, thereby improving the confidentiality of information transmission. The communication scenario is shown in fig. 1.
In the scenario shown in fig. 1, a coordinate system is established with a ground user as an origin of coordinates (0,0,0), the eavesdropper position is denoted as (u, v,0), and the aerial drone position is denoted as (x, y, z). The unmanned aerial vehicle positions the ground user and the eavesdropper, obtains the positions of the ground user and the eavesdropper relative to the origin of coordinates, and then establishes an optimization problem model. The problem modeling mainly considers the following factors:
(1) a location of a ground eavesdropper;
(2) a horizontal airspace and a vertical airspace in which the unmanned aerial vehicle flies;
(3) ground obstacle location and features.
The problem form of modeling is as follows:
Figure BDA0002463237770000081
wherein p issThe probability of a non-zero secret capacity is represented,
Figure BDA0002463237770000082
the influence of the eavesdropper location change on the secure transmission is mainly reflected in psThe above.
Figure BDA0002463237770000083
Denotes a radius rmaxIs restricted in the horizontal space. h ismin≤z≤hmaxRepresenting a minimum and maximum flying height of hminAnd hmaxVertical spatial constraints.
Figure BDA0002463237770000084
K is 1,2, …, K represents the ground obstacle of the ellipsoid, and K is total, wherein (m) isk,nk0) is the center of the kth ellipsoid hemisphere, δk,βk,
Figure BDA0002463237770000085
Respectively, the half-axis lengths of the ellipsoid along the x, y and z coordinate axes. α is the path loss exponent. The above problems can be equated with
Figure BDA0002463237770000086
The problem solving method provided by the invention needs to gradually convert the original problem (the conversion process is shown in figure 2), and finally obtains a semi-definite programming problem, so that the semi-definite programming correlation algorithm is used for solving. Because the constraint is relaxed in the problem transformation process, the solution obtained by the semi-definite programming algorithm may not be the optimal solution of the original problem. Therefore, the optimal approximate solution of the original problem needs to be extracted from the solution obtained by the semi-definite programming. The problem solving process comprises two steps: firstly, converting an original problem into a semi-definite plan; the second is the approximation of the optimal solution of the original problem.
Converting original problems into semi-definite plans
The specific solving steps for converting the original problem into semi-definite programming are shown in fig. 2.
Step1 is converted into quadratic programming: defining x as (x, y, z), problem (2) translates into an equivalent quadratic programming
Figure BDA0002463237770000091
Wherein e ═ u2+v2，a＝[-2u -2v 0]T，
Figure BDA0002463237770000092
d＝[0 0 1]T，
Figure BDA0002463237770000093
Step2 is converted into quadratic programming: variable substitution, definition
Figure BDA0002463237770000094
Figure BDA0002463237770000095
Problem (3) translates into the following equivalent quadratic programming
Figure BDA0002463237770000096
Step3 translates to the semi-definite relaxation problem: define variable substitution
Figure BDA0002463237770000097
And
Figure BDA0002463237770000098
problem (4) can be equivalently converted into
Figure BDA0002463237770000101
Wherein the content of the first and second substances,
Figure BDA0002463237770000102
o3×1＝[0 0 0]T，
Figure BDA0002463237770000103
Figure BDA0002463237770000104
Figure BDA0002463237770000105
step4 is converted into semi-definite programming: neglecting the constraint rank (w) ═ 1, a semi-definite programming problem is obtained:
Figure BDA0002463237770000106
for problem (6), semi-definite programming correlation algorithm can be applied to find its optimal solution W*. Since relaxation of the rank 1 constraint may cause an optimal solution W to the problem (6)*And is not the optimal solution to the original problem. Thus, an optimal solution W from the problem (6) is required*And extracting an approximate solution of the optimal solution of the original problem.
Approximation of optimal solution of original problem
The present invention is based on the optimal solution W to the problem (6)*Two methods can be adopted for extracting the approximate solution of the optimal solution of the original problem, one is a feature vector approximation method, and the other is a random sampling method.
Method of approximating a feature vector
The feature vector approximation method takes the optimal solution W of the problem (6)*And maps it into a feasible set of the original problem.
Let xi denote W*Rank of (d), i.e. ξ ═ rank (W)*) To W*Performing characteristic decomposition to obtain
Figure BDA0002463237770000111
Wherein gamma is1≥γ2≥…≥γξ> 0 is the characteristic value, rho, in descending order1，ρ2，…，ρξIs the corresponding feature vector. Thus, for W*The best rank 1 approximation is
Figure BDA0002463237770000112
Due to the fact that
Figure BDA0002463237770000113
X '/t ' is available '
As an approximate solution to problem (3), an approximate solution to problem (2) is thus directly available. If the feature is decomposed to obtain
Figure BDA0002463237770000114
If the problem (4) is not feasible, the obtained infeasible solution is mapped to a near feasible solution of the problem (3). The steps of the feature vector approximation method are shown in algorithms 1 and 2.
Algorithm 1: method of approximating a feature vector
Figure BDA0002463237770000115
And 2, algorithm: mapping of infeasible solutions to feasible solutions
Figure BDA0002463237770000116
Figure BDA0002463237770000121
(II) random sampling method
Optimal solution W to problem (6)*Is a symmetric semi-positive definite matrix. Generating a random vector mu obeying a mean of 0 covariance matrix of W*Is a Gaussian distribution of
Figure BDA0002463237770000122
Now consider the following random optimization problem
Figure BDA0002463237770000123
Wherein EμIndicating a mathematical expectation with respect to mu. Can obtain Eμ{μμT}＝W*，Eμ{μTQμ}＝Tr{QW*}. We can manipulate the covariance matrix of the random vector μ to W*So that the problem (7) is true in the mathematically expected sense. This means that a better approximate solution to the problem (3) can be obtained by generating the random vector μ a sufficient number of times.
The detailed steps are shown in algorithm 3.
And (3) algorithm: random sampling method
Figure BDA0002463237770000124
Figure BDA0002463237770000131
To test the performance of the present invention, we compared the method of the present invention with a poor search method. Assuming that the eavesdropper changes its position continuously in order to improve the quality of eavesdropping, the movement trace includes: (1) moving from point (-250,300) to point (250, -200) on line v-u + 50; (2) at curve v ═ u2From point (-100,850) to (100, 85) at 10-1500) And (4) moving. Other parameter settings are shown in the table below.
Figure BDA0002463237770000132
Assuming that the eavesdropper moves on the line v-u +50, the simulation results are shown in fig. 3 and 4. In fig. 3, we compare the probability of non-zero privacy capacity for poor search, random sampling and feature vector approximation. It can be seen that the difference between the three methods is small. In particular, the probability of the non-zero secret volume of random sampling is only a little lower than the result of the poor search, and is only weakly superior to the feature vector approximation. In addition, the probability of non-zero privacy capacity for all three methods decreases and increases as the eavesdropper moves, because the eavesdropper moves closer to the ground user and then moves further away from the ground user. It is worth noting that the performance of the random sampling method is closely related to the random sampling times, and the performance is better when the sampling times are more. To achieve better performance, we set the sampling times to 1000 in the simulation.
Fig. 4 depicts the change in location of each node in the system. As can be seen, the drone intelligently flies around the ground user and adaptively changes its location as the ground eavesdropper changes location. As can be seen from the horizontal projection of the three-dimensional spatial position of the drone, the drone is typically deployed on one side of the ground user, while the eavesdropper is on the other side. This is intuitive, so deployment keeps the drone away from the eavesdropper, reducing the interception probability of the eavesdropper. Furthermore, it can be seen through simulation that the height of the drone is not constant, but there are minor undulations above the lowest height. This is because the drone flies as low as possible to get close to the ground user, which is beneficial to reducing the path loss of the legitimate channel, thus improving the probability of non-zero secret capacity.
When the eavesdropper is in the curve v ═ u2The simulation results when moving on/10-150 are shown in FIGS. 5 and 6. In fig. 5, the probability of non-zero privacy capacity appears in the shape of the letter "W". The reason is as follows: the distance between the eavesdropper and the ground user decreases first due to the movement of the eavesdropperPost increment knows that the eavesdropper moved to the curve v-u2A minimum point of 10 to 150. Thereafter, the trend of the distance between the eavesdropper and the ground user is similar, i.e., decreasing and then increasing. The position of the drone is also constantly changing as the eavesdropper moves. The trend of the distance between the unmanned aerial vehicle and the ground user is just opposite to the trend of the distance between the eavesdropper and the ground user. From the analysis, the numerical simulation results are consistent with the theoretical derivation.
Fig. 6 reflects the location change of each communication node. It can be seen that the drone intelligently changes its position according to the eavesdropper's change of position to increase the probability of non-zero privacy capacity. In fig. 6, we can draw a similar conclusion as in fig. 4, namely that the horizontal projection of the drone position and the eavesdropper are located on both sides of the ground user, respectively, because such deployment makes the drone as far away as possible from the eavesdropper, thus increasing the possibility of physical layer secure transmission as much as possible.

Claims (1)

1. An unmanned aerial vehicle obstacle avoidance and three-dimensional deployment method for enhancing cellular mobile communication safety is characterized in that: the method comprises the following specific steps:
(1) performing mathematical modeling on the problem;
the method comprises the following specific steps:
in a cellular mobile communication scene based on an unmanned aerial vehicle, a coordinate system is established by taking a ground user as a coordinate origin (0,0,0), the position of an eavesdropper is marked as (u, v,0), the position of an aerial unmanned aerial vehicle is marked as (x, y, z), the unmanned aerial vehicle positions the ground user and the eavesdropper to obtain the positions of the ground user and the eavesdropper relative to the coordinate origin, and then an optimization problem model is established; the factors considered for problem modeling are:
(1) a location of a ground eavesdropper;
(2) a horizontal airspace and a vertical airspace in which the unmanned aerial vehicle flies;
(3) ground obstacle position and characteristics;
the problem form of modeling is as follows:
Figure FDA0003537824370000011
wherein p issThe probability of a non-zero secret capacity is represented,
Figure FDA0003537824370000012
the effect of the eavesdropper location change on the secure transmission is reflected in psThe above step (1);
Figure FDA0003537824370000013
denotes a radius rmaxHorizontal airspace constraints of (2); h ismin≤z≤hmaxRepresenting the lowest and highest flying heights hminAnd hmaxVertical airspace constraint of (1);
Figure FDA0003537824370000014
k is 1,2, L, K represents the ground obstacle of the ellipsoid, and K is total, wherein (m)k,nk0) is the center of the kth ellipsoid, δk,βk,
Figure FDA0003537824370000015
The length of the half shaft of the ellipse hemisphere in the x, y and z coordinate axes; α is a path loss exponent, and the above problem form (1) is equivalent to
Figure FDA0003537824370000021
(2) Gradually converting the problem into quadratic fractional programming, and then equivalently converting the problem into quadratic programming by defining intermediate variables; then carrying out variable substitution, and equivalently converting quadratic programming into a semi-definite relaxation problem; finally, omitting the constraint on the matrix rank to obtain a semi-definite programming problem; the method comprises the following specific steps:
step1 is converted into quadratic programming: defining x as (x, y, z), problem (2) translates into an equivalent quadratic programming
Figure FDA0003537824370000022
Wherein e ═ u2+v2，a＝[-2u -2v 0]T，
Figure FDA0003537824370000023
d＝[0 0 1]T，
Figure FDA0003537824370000024
Step2 is converted into quadratic programming: variable substitution, definition
Figure FDA0003537824370000025
Figure FDA0003537824370000026
Problem (3) translates into the following equivalent quadratic programming
Figure FDA0003537824370000031
Step3 translates into the semi-definite relaxation problem: define variable substitution
Figure FDA0003537824370000032
And
Figure FDA0003537824370000033
problem (4) is equivalently converted into
Figure FDA0003537824370000034
Wherein the content of the first and second substances,
Figure FDA0003537824370000035
o3×1＝[0 0 0]T，
Figure FDA0003537824370000036
Figure FDA0003537824370000037
Figure FDA0003537824370000038
step4 is converted into semi-definite programming: neglecting the constraint rank (w) ═ 1, a semi-definite programming problem is obtained:
Figure FDA0003537824370000041
for problem (6), a semi-definite programming correlation algorithm is applied to find its optimal solution W*；
(3) Extracting an approximate solution of the original problem, which comprises the following steps: from the optimal solution W of the problem (6)*There are two methods for extracting the approximate solution of the optimal solution of the original problem, one is the feature vector approximation method, the other is the random sampling method;
method of approximating a feature vector
The feature vector approximation method takes the optimal solution W of the problem (6)*And mapping the principal feature vector to a feasible set of the original problem;
let xi denote W*Rank of (c), i.e. ξ ═ rank (W)*) To W*Performing characteristic decomposition to obtain
Figure FDA0003537824370000042
Wherein gamma is1≥γ2≥L≥γξ> 0 is the characteristic value, rho, in descending order1，ρ2，…，ρξIs the corresponding feature vector, thus, for W*The best rank 1 approximation is
Figure FDA0003537824370000043
Due to the fact that
Figure FDA0003537824370000044
Obtaining x' as an approximate solution of the problem (3), thereby directly obtaining an approximate solution of the problem (2); if the feature is decomposed to obtain
Figure FDA0003537824370000045
If the problem (4) is not feasible, mapping the obtained infeasible solution to an adjacent feasible solution of the problem (3);
(II) random sampling method
Optimal solution W to problem (6)*Is a symmetric semi-positive definite matrix, generates a random vector mu, and has a covariance matrix W with a mean value of 0*A Gaussian distribution of (1), i.e., μ to N (0, W)*) (ii) a Now consider the following random optimization problem
Figure FDA0003537824370000051
Wherein EμExpress a mathematical expectation about μ, resulting in Eμ{μμT}＝W*，Eμ{μTQμ}＝Tr{QW*}; the covariance matrix of the steering random vector mu is W*So that problem (7) holds in the mathematically expected sense, an approximate solution to problem (3) is obtained by generating random vectors μ sufficiently many times.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105737819A (en) * 2016-02-25 2016-07-06 西北工业大学 Unmanned aerial vehicle three-dimensional airway planning method based on space compression and table-lookup calculation
CN108964730A (en) * 2018-07-16 2018-12-07 南京理工大学 The approximate linear pre-coding method of convex row is based in the modulating system of safe space
CN109587684A (en) * 2019-01-11 2019-04-05 中国人民解放军陆军炮兵防空兵学院 Enhance the low latitude mobile base station dynamic deployment method of wireless network safety of physical layer
CN109640257A (en) * 2019-01-23 2019-04-16 中国人民解放军陆军工程大学 A kind of IOT network security transmission method based on unmanned plane

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10555152B2 (en) * 2017-09-28 2020-02-04 At&T Intellectual Property I, L.P. Drone-to-drone information exchange

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105737819A (en) * 2016-02-25 2016-07-06 西北工业大学 Unmanned aerial vehicle three-dimensional airway planning method based on space compression and table-lookup calculation
CN108964730A (en) * 2018-07-16 2018-12-07 南京理工大学 The approximate linear pre-coding method of convex row is based in the modulating system of safe space
CN109587684A (en) * 2019-01-11 2019-04-05 中国人民解放军陆军炮兵防空兵学院 Enhance the low latitude mobile base station dynamic deployment method of wireless network safety of physical layer
CN109640257A (en) * 2019-01-23 2019-04-16 中国人民解放军陆军工程大学 A kind of IOT network security transmission method based on unmanned plane

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Joint Resource Allocation and Trajectory Design for UAV-Aided Wireless Physical Layer Security;Xiaofang Sun, Chao Shen,etc.;《2018 IEEE Globecom Workshops (GC Wkshps)》;20190221;全文 *
无人机通信中的物理层安全问题研究;汪昭;《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》;20200215;全文 *
王东 ; 张广政 ; 穆武第.多无人机协同作战通信自组网技术.《CNKI 飞航导弹》.2012, *

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