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CN113193900A - Network connectivity subsection integral calculation method for ultraviolet light cooperation unmanned aerial vehicle communication - Google Patents

Network connectivity subsection integral calculation method for ultraviolet light cooperation unmanned aerial vehicle communication Download PDF

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CN113193900A
CN113193900A CN202110335045.4A CN202110335045A CN113193900A CN 113193900 A CN113193900 A CN 113193900A CN 202110335045 A CN202110335045 A CN 202110335045A CN 113193900 A CN113193900 A CN 113193900A
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unmanned aerial
aerial vehicle
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connectivity
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赵太飞
林亚茹
张倩
张爽
薛蓉莉
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Xian University of Technology
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Abstract

本发明提供了一种紫外光协作无人机通信的网络连通性分部积分计算方法,首先选取移动自组网的运动模型,对无人机运动模型进行量化分析,得到无人机遵循RWP模型运动时的概率密度函数;其次,采用PPM调制方式,得到无线紫外光通信方式下的覆盖范围;之后引入网络连通性近似方法表达式,并对连通性计算进行数学建模;再在所建立模型的基础上实施分部积分,并通过建立模型计算得到不同参数配置下的网络k‑连通概率;最终将基于分部积分建模的计算方法与已有的泊松强度近似法进行比较,验证分部积分法的有效性。本发明在极坐标系下实现连通性近似分析,减少了积分区间的划分,简化了计算过程。

Figure 202110335045

The invention provides a method for calculating the network connectivity division integral of ultraviolet light cooperative unmanned aerial vehicle communication. First, the motion model of the mobile ad hoc network is selected, and the motion model of the unmanned aerial vehicle is quantitatively analyzed, and the unmanned aerial vehicle follows the RWP model. The probability density function during motion; secondly, the PPM modulation method is used to obtain the coverage under the wireless ultraviolet light communication method; then the approximate method expression of network connectivity is introduced, and the connectivity calculation is mathematically modeled; On the basis of the integral part by part, the network k-connectivity probability under different parameter configurations is calculated by establishing a model. Finally, the calculation method based on the integral part by part modeling is compared with the existing Poisson strength approximation method to verify the Validity of partial integration. The invention realizes the approximate analysis of connectivity in the polar coordinate system, reduces the division of the integral interval, and simplifies the calculation process.

Figure 202110335045

Description

Network connectivity subsection integral calculation method for ultraviolet light cooperation unmanned aerial vehicle communication
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle network communication, and particularly relates to a network connectivity subsection integral calculation method for ultraviolet light cooperation unmanned aerial vehicle communication.
Background
Nowadays, with the wide application of unmanned aerial vehicles, especially in the military, covert communication under the battlefield environment is very important. But whether can realize reliable and stable communication between the unmanned aerial vehicle not only depends on the selection of communication mode, and whether communication link communicates in real time also influences the inside communication of unmanned aerial vehicle constantly.
The unmanned aerial vehicle can provide large capacity, long distance transmission and cover on a large scale, can extensively be used for fields such as communication, reconnaissance, supervision. However, communication between drones, especially communication between drones in a battlefield environment, has higher requirements on communication security and communication quality, and therefore, while ensuring reliable communication, the problem of network connectivity is also increasingly emphasized. Because the real-time change of the link, the network connectivity is influenced by the movement of the nodes, when the unmanned aerial vehicles in the network follow the static distribution, the network connectivity can be conveniently and quantitatively analyzed, but when the unmanned aerial vehicles in the network are dynamically distributed, the quantitative analysis of the connectivity becomes a difficult problem, so that a new research idea is provided for the calculation of the network connectivity of the unmanned aerial vehicles by providing an approximate method of the network connectivity of the ultraviolet light cooperation unmanned aerial vehicles, and a foundation is laid for the unmanned aerial vehicle networking to more efficiently and cooperatively complete tasks.
It is noted that this section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
Disclosure of Invention
The invention aims to provide a network connectivity subsection integral calculation method for ultraviolet light cooperation unmanned aerial vehicle communication, which realizes efficient cooperative task completion of unmanned aerial vehicle networking.
In order to achieve the purpose, the invention adopts the following technical scheme:
the network connectivity subsection integral calculation method for the ultraviolet light cooperation unmanned aerial vehicle communication comprises the following steps:
s1: selecting a motion model of the mobile ad hoc network, and carrying out quantitative analysis on the motion model of the unmanned aerial vehicle to obtain a probability density function when the unmanned aerial vehicle moves along the RWP model;
s2: obtaining a coverage range in a wireless ultraviolet communication mode by adopting a PPM (pulse position modulation) mode;
s3: introducing a network connectivity approximate method expression, and performing mathematical modeling on connectivity calculation;
s4: implementing division integration on the basis of the established model, and calculating to obtain the network k-connectivity probability under different parameter configurations through the established model;
s5: and comparing the calculation method based on the fractional integral modeling with the existing Poisson intensity approximation method, thereby verifying the effectiveness of the fractional integral method.
Further, the unmanned aerial vehicle motion model in step S1 follows the following rule, taking unmanned aerial vehicle i as an example:
(a) motion point from PiMoving along a zigzag line to the next point of pause Pi+1
(b) The pause points are uniformly and randomly distributed in a convex area;
(c) before each pause point starts to move, a speed is randomly acquired from the uniform speed distribution;
(d) when the node continues to the next section and reaches the next pause point, a short thinking time exists, wherein the thinking time is independent and distributed random variables.
Further, the probability density function in step S1 is as follows:
Figure RE-GDA0003133304660000021
wherein C is a fixed constant and is equal to
Figure RE-GDA0003133304660000022
Further, the step S2 is specifically as follows:
in the communication process of the unmanned aerial vehicle, an ultraviolet light non-direct-view communication mode is introduced, and a PPM (pulse position modulation) mode is adopted to obtain a coverage range under a wireless ultraviolet light communication mode; wherein, coverage is with unmanned aerial vehicle as the center, and effective communication distance is the circle territory of radius, and communication radius can be expressed as:
Figure RE-GDA0003133304660000031
where η is the product of the filter efficiency and the photomultiplier quantum efficiency, RbFor information modulation rate, M is PPM modulation code length, xi is path loss factor, and alpha is path loss index.
Further, the step S3 is specifically as follows:
when being discrete probability distribution to unmanned aerial vehicle, it is discrete poisson static distribution to establish n unmanned aerial vehicle in the network, and unmanned aerial vehicle i's minimum neighbor number is counted and is done: dminAnd satisfy dminAnd the probability that k is communicated in the network is equal to the probability that the minimum neighbor number of any unmanned aerial vehicle in the network is not less than k, and the following conditions are met:
Figure RE-GDA0003133304660000032
when the drones are in the form of continuous probability distribution, and convert to the form of integral, equation (3) should be converted to equation (4) to adapt to the scenario of mobile ad hoc network:
Figure RE-GDA0003133304660000033
wherein r is the distance from the unmanned aerial vehicle i to the center of the distribution area, f (r) is the probability density function of unmanned aerial vehicle distribution, and A is the area of the distribution area.
Further, the step S4 is specifically as follows:
let communication radius of unmanned aerial vehicle i be r0The probability of a neighboring drone existing within the coverage of drone i is denoted p (r)i,r0) The probability of no unmanned plane in the coverage range is 1-p (r)i,r0) Then, UVAiThe number of neighbor unmanned aerial vehicles in the coverage area follows binomial distribution, and is represented as Nn,k~Bin(n,p(ri,r0) ); probability of having at least k neighbor drones in the coverage area of drone i is
Figure RE-GDA0003133304660000034
Substituting the formula (5) into the formula (4) to obtain the k-connectivity probability of the unmanned aerial vehicle network; wherein, p (r)i,r0) Is shown as
Figure RE-GDA0003133304660000041
Wherein, S is the intersection of the unmanned aerial vehicle network distribution area and the coverage of unmanned aerial vehicle i and is represented as:
Figure RE-GDA0003133304660000042
the unmanned aerial vehicle network is divided into two conditions under polar coordinates, when ri>r0When r is calculated as in the formula (6)i≤r0When f (r) integral on S is converted into fractional integral which is the probability density function on S1And S2Sum of integrals over two parts
Figure RE-GDA0003133304660000043
The invention has the beneficial effects that:
(1) according to the network connectivity subsection integral calculation method for the ultraviolet light cooperation unmanned aerial vehicle communication, connectivity approximate analysis is achieved under a polar coordinate system, division of integral intervals is reduced, and the calculation process is simplified;
(2) the invention relates to a network connectivity subsection integral calculation method for ultraviolet light cooperation unmanned aerial vehicle communication, which provides a research method when the probability density of an unmanned aerial vehicle is continuous according to a research idea when the probability density of the unmanned aerial vehicle is discrete;
(3) according to the network connectivity subsection integral calculation method for the ultraviolet light cooperation unmanned aerial vehicle communication, the convergence of the connectivity approximation method based on subsection integral is faster than that of a Poisson strength approximation method in view of simulation results;
(4) according to the network connectivity subsection integral calculation method for the ultraviolet light cooperation unmanned aerial vehicle communication, the connectivity probability can be converged to 1 more quickly under the condition of the same parameter configuration based on the connectivity approximation method of subsection integral, which means that the resource configuration is saved more in the actual situation.
Drawings
Fig. 1 is a diagram of the inventive drone following the RWP model movement;
FIG. 2(a) is the fractional integral modeling (r) of the present inventioni>r0) A schematic diagram;
FIG. 2(b) is the fractional integral modeling (r) of the present inventioni≤r0) A schematic diagram;
FIG. 3(a) is a graph illustrating the variation of the network 2-connectivity probability with transmit power in accordance with the present invention;
FIG. 3(b) is a schematic diagram of the variation of the network 2-connectivity probability with information rate according to the present invention;
fig. 4 is a flow chart of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features or characteristics may be combined in any suitable manner in one or more embodiments.
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Aiming at the high-mobility unmanned aerial vehicle network and considering the problem of safety of communication between unmanned aerial vehicles in strong electromagnetic interference environment, the importance of network connectivity to the cooperative task execution of the unmanned aerial vehicles is clarified. The unmanned aerial vehicle follows the RWP model motion rule in the designated convex area, and for convenience of analysis, the designated motion area is a circular area to obtain a probability density function of the unmanned aerial vehicle under polar coordinates; introducing a wireless ultraviolet PPM modulation mode, and calculating an ultraviolet communication range under the modulation mode; and carrying out research and derivation by taking a certain unmanned aerial vehicle as a center and a communication range as a radius, modeling connectivity calculation in a fractional integration mode, and finally comparing the result obtained by the method with the result obtained by a Poisson strength approximation method.
The method adopts the technical scheme that firstly, a motion model of the mobile ad hoc network is selected, a RWP (random waypoint) model is taken as an example, the motion model of the unmanned aerial vehicle is subjected to quantitative analysis, and a probability density function when the unmanned aerial vehicle moves along the RWP model is obtained; secondly, a PPM (pulse Position modulation) modulation mode is adopted to obtain a coverage range under a wireless ultraviolet communication mode; introducing a network connectivity approximate method expression, and performing mathematical modeling on connectivity calculation; then, implementing division integration on the basis of the established model, and calculating to obtain the network k-connectivity probability under different parameter configurations through the established model; and finally, comparing the calculation method based on fractional integral modeling with the existing Poisson intensity approximation method, and verifying the effectiveness of the proposed fractional integral method.
The method comprises the following specific implementation steps:
step 1: distribution of unmanned aerial vehicles under RWP model
First, assume that the drone follows the RWP model in a specified circular field, as shown in fig. 1. The RWP mobility model can be briefly summarized as a mobility procedure and a suspension procedure. Take UAV i as an example, UAViThe following rules were followed:
(a) motion point from PiMoving along a zigzag line to the next point of pause Pi+1
(b) The pause points are uniformly randomly distributed in a convex area, such as a unit circle;
(c) before each pause point starts to move, a speed is randomly acquired from the uniform speed distribution;
(d) when the node continues to the next section and reaches the next pause point, a short thinking time exists, wherein the thinking time is independent and distributed random variables.
The probability density function of the RWP model in polar coordinates is expressed as:
Figure RE-GDA0003133304660000061
wherein C is a fixed constant and is equal to
Figure RE-GDA0003133304660000062
Step 2: communication range of unmanned aerial vehicle under PPM modulation
In the unmanned aerial vehicle communication process, in order to obtain omnidirectional coverage, introduce ultraviolet light non-direct-view (a) type communication mode, adopt PPM modulation mode, can confirm to use unmanned aerial vehicle as the centre of a circle under the circumstances that transmitted power and each parameter of system confirm, communication distance is the coverage circle region of radius, and communication radius can express as:
Figure RE-GDA0003133304660000063
where η is the product of the filter efficiency and the photomultiplier quantum efficiency, RbFor information modulation rate, M is PPM modulation code length, xi is path loss factor, and alpha is path loss index.
And step 3: connectivity calculation for continuous probability distribution
When the unmanned aerial vehicles are in discrete probability distribution, it is assumed that n unmanned aerial vehicles in the network are in discrete poisson static distribution, and the minimum neighbor number of a certain unmanned aerial vehicle i is recorded as: dminAnd satisfy dminAnd the probability of k connection in the network is approximately equal to the probability that the minimum neighbor number of any unmanned aerial vehicle in the network is not less than k, and the following conditions are met:
Figure RE-GDA0003133304660000071
the above formula (3) is connectivity calculation for the drone with discrete probability distribution. Then when the drones are in a continuous probability distribution, it should be converted into an integral form, and equation (3) should be converted into equation (4) to adapt to the scenario of mobile ad hoc network
Figure RE-GDA0003133304660000072
Wherein r is the distance between the unmanned aerial vehicle i and the center of the distribution area, f (r) is the probability density function of the unmanned aerial vehicle distribution, A is the area of the distribution area, and the core problem is converted into P under the continuous statei(dminK) or more.
And 4, step 4: modeling based on fractional integration
As shown in fig. 2, assume that communication radius of drone i is r0The probability of a neighboring drone existing within the coverage of drone i is denoted p (r)i,r0) The probability of no unmanned plane in the coverage range is 1-p (r)i,r0) Then UVAiThe number of neighbor unmanned aerial vehicles in the coverage area follows binomial distribution, and is represented as Nn,k~Bin(n,p(ri,r0)). Probability of having at least k neighbor drones in the coverage area of drone i is
Figure RE-GDA0003133304660000073
Substituting the formula (5) into the formula (4) can obtain the k-connectivity probability of the unmanned aerial vehicle network. Therefore, the solution problem of k-connected probability will be converted into p (r)i,r0) To solve the problem. p (r)i,r0) Is shown as
Figure RE-GDA0003133304660000081
Wherein S is unmanned aerial vehicle network distribution area and unmanned aerial vehicleThe intersection of the coverage of machine i is represented as:
Figure RE-GDA0003133304660000082
taking the example of not considering the boundary effect as an example for analysis, the unmanned aerial vehicle network is divided into two cases under the polar coordinate, namely ri>r0And ri£r0In two cases, when ri>r0When r is calculated as in the formula (6)i£r0When the formula (6) is converted into the formula (7) for calculation, the integral of f (r) on S should be converted into fractional integral which is the probability density function on S1And S2The sum of the integrals over the two parts.
Figure RE-GDA0003133304660000083
And 5: comparison of different approximation methods
The connectivity approximation method based on fractional integral modeling proposed in step 4 is denoted as a2, the poisson strength approximation method is denoted as a1, and the radius under the PPM modulation mode in step 2 is used as the coverage.
By adopting different approximation methods and different numbers of drones, six different situations are set, as shown in fig. 3. Fig. 3(a) is the variation of the network 2-connected probability with the transmission power (Transmitted power), and fig. 3(b) is the variation of the network 2-connected probability with the information rate (Data rate), and it can be seen that, in the case that the transmission power and the number of the drones are the same, the a2 approximate method can achieve a higher connected probability than the a 1; when the information rate is the same as the number of the unmanned aerial vehicles, the connection probability under the A2 approximate method is greater than A1; therefore, from the comparison of the results in fig. 3, the effectiveness and correctness of the a2 approximation method can be clearly seen. Also in practical cases, when the same probability of connectivity needs to be achieved, the approximation method a2 based on fractional integration will require less transmission power and information rate, which saves resource allocation to some extent.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

Claims (6)

1.一种紫外光协作无人机通信的网络连通性分部积分计算方法,其特征在于,包括以下步骤:1. a network connectivity division integral calculation method of ultraviolet light cooperative unmanned aerial vehicle communication, is characterized in that, comprises the following steps: S1:选取一个移动自组网的运动模型,对无人机运动模型进行量化分析,得到无人机遵循RWP模型运动时的概率密度函数;S1: Select a motion model of the mobile ad hoc network, perform quantitative analysis on the UAV motion model, and obtain the probability density function when the UAV follows the RWP model; S2:采用PPM调制方式,得到无线紫外光通信方式下的覆盖范围;S2: The PPM modulation method is used to obtain the coverage under the wireless ultraviolet light communication method; S3:引入网络连通性近似方法表达式,并对连通性计算进行数学建模;S3: Introduce the approximate method expression of network connectivity, and conduct mathematical modeling for connectivity calculation; S4:在所建立模型的基础上实施分部积分,并通过建立模型计算得到不同参数配置下的网络k-连通概率;S4: Implement partial integration on the basis of the established model, and calculate the network k-connectivity probability under different parameter configurations by establishing the model; S5:将基于分部积分建模的计算方法与已有的泊松强度近似法进行比较,从而验证分部积分法的有效性。S5: Compare the calculation method based on integral by parts modeling with the existing Poisson strength approximation method to verify the validity of the integral by parts method. 2.根据权利要求1所述的紫外光协作无人机通信的网络连通性分部积分计算方法,其特征在于,所述步骤S1中无人机运动模型遵循以下规则,以无人机i为例:2. the network connectivity fractional integral calculation method of ultraviolet light cooperative unmanned aerial vehicle communication according to claim 1, is characterized in that, in described step S1, unmanned aerial vehicle motion model follows following rules, and unmanned aerial vehicle i is. example: (a)运动点从Pi沿锯齿形的线运动到下一个暂停点Pi+1(a) the moving point moves along the zigzag line from Pi to the next pause point Pi +1 ; (b)暂停点均匀地随机分布在一个凸区域;(b) Pause points are uniformly and randomly distributed in a convex region; (c)每个暂停点在开始运动前,将会从均匀的速度分布中随机获取一个速度;(c) Each pause point will randomly obtain a speed from a uniform speed distribution before starting to move; (d)当节点在继续下一段并到达下一个暂停点前,会有一个短暂的思考时间,其中“思考时间”是独立同分布的随机变量。(d) When the node continues to the next segment and reaches the next pause point, there will be a short thinking time, where the "thinking time" is an independent and identically distributed random variable. 3.根据权利要求1所述的紫外光协作无人机通信的网络连通性分部积分计算方法,其特征在于,所述步骤S1中概率密度函数如下:3. the network connectivity fractional integral calculation method of ultraviolet light cooperative unmanned aerial vehicle communication according to claim 1, is characterized in that, in described step S1, probability density function is as follows:
Figure RE-FDA0003133304650000011
Figure RE-FDA0003133304650000011
其中,C为一个固定常数并且等于
Figure RE-FDA0003133304650000012
where C is a fixed constant and equal to
Figure RE-FDA0003133304650000012
4.根据权利要求1所述的紫外光协作无人机通信的网络连通性分部积分计算方法,其特征在于,所述步骤S2具体如下:4. the network connectivity fractional integral calculation method of ultraviolet light cooperative unmanned aerial vehicle communication according to claim 1, is characterized in that, described step S2 is specifically as follows: 在无人机通信过程中,引入紫外光非直视通信方式,采用PPM调制方式,得到无线紫外光通信方式下的覆盖范围;其中,覆盖范围是以无人机为中心,有效通信距离为半径的圆域,通信半径可以表示为:In the process of unmanned aerial vehicle communication, the UV non-direct line of sight communication method is introduced, and the PPM modulation method is used to obtain the coverage under the wireless ultraviolet communication method; among which, the coverage area is centered on the unmanned aerial vehicle, and the effective communication distance is the radius The circular domain of , the communication radius can be expressed as:
Figure RE-FDA0003133304650000021
Figure RE-FDA0003133304650000021
其中,η是滤波器效率和光电倍增管量子效率的乘积,Rb为信息调制速率,M为PPM调制码长,ξ为路径损耗因子,α为路径损耗指数。Among them, η is the product of filter efficiency and photomultiplier tube quantum efficiency, R b is the information modulation rate, M is the PPM modulation code length, ξ is the path loss factor, and α is the path loss index.
5.根据权利要求1所述的紫外光协作无人机通信的网络连通性分部积分计算方法,其特征在于,所述步骤S3具体如下:5. the network connectivity fractional integral calculation method of ultraviolet light cooperative unmanned aerial vehicle communication according to claim 1, is characterized in that, described step S3 is as follows: 针对无人机呈离散型概率分布时,设网络中有n个无人机是离散的泊松静态分布,无人机i的最小邻居数记作:dmin,并满足dmin≥k,网络中k连通的概率等于网络中任意无人机的最小邻居数不小于k的概率,并满足:For the discrete probability distribution of UAVs, it is assumed that there are n UAVs in the network with discrete Poisson static distribution, the minimum number of neighbors of UAV i is recorded as: d min , and if d min ≥ k, the network The probability that k is connected is equal to the probability that the minimum number of neighbors of any UAV in the network is not less than k, and satisfies:
Figure RE-FDA0003133304650000022
Figure RE-FDA0003133304650000022
当无人机呈连续型的概率分布时转换为积分的形式,公式(3)应该转换为公式(4)以适应移动自组网的场景:When the UAV is in the form of continuous probability distribution, the formula (3) should be converted into the formula (4) to adapt to the mobile ad hoc network scenario:
Figure RE-FDA0003133304650000023
Figure RE-FDA0003133304650000023
其中,r为无人机i距离分布区域中心的距离,f(r)为无人机分布的概率密度函数,A为分布区域的面积。Among them, r is the distance of UAV i from the center of the distribution area, f(r) is the probability density function of UAV distribution, and A is the area of the distribution area.
6.根据权利要求4所述的紫外光协作无人机通信的网络连通性分部积分计算方法,其特征在于,所述步骤S4具体如下:6. The network connectivity fractional integral calculation method of ultraviolet light cooperative unmanned aerial vehicle communication according to claim 4, is characterized in that, described step S4 is specifically as follows: 设无人机i的通信半径为r0,将无人机i覆盖范围内存在邻居无人机的概率记作p(ri,r0),覆盖范围内不存在无人机的概率为1-p(ri,r0),那么,UVAi覆盖范围内邻居无人机数量服从二项分布,表示为:Nn,k~Bin(n,p(ri,r0));无人机i的覆盖范围内至少有k个邻居无人机的概率为Let the communication radius of UAV i be r 0 , the probability of the existence of neighboring UAVs in the coverage area of UAV i is denoted as p(r i ,r 0 ), and the probability of no UAV in the coverage area is 1 -p(r i ,r 0 ), then, the number of neighboring drones within the coverage of UVA i obeys a binomial distribution, expressed as: N n,k ~Bin(n,p(r i ,r 0 )); no The probability that there are at least k neighbor drones within the coverage of human-machine i is
Figure RE-FDA0003133304650000031
Figure RE-FDA0003133304650000031
将公式(5)代入公式(4)中可以得到无人机网络k-连通概率;其中,p(ri,r0)表示为Substituting formula (5) into formula (4) can obtain the k-connectivity probability of UAV network; among them, p(r i ,r 0 ) is expressed as
Figure RE-FDA0003133304650000032
Figure RE-FDA0003133304650000032
其中,S为无人机网络分布区域与无人机i的覆盖范围的交集表示为:
Figure RE-FDA0003133304650000033
Among them, S is the intersection of the UAV network distribution area and the coverage area of UAV i, which is expressed as:
Figure RE-FDA0003133304650000033
无人机网络在极坐标下分为两种情况,当ri>r0时,计算方法如公式(6),当ri≤r0时,f(r)在S上的积分转为分部积分,为概率密度函数在S1和S2两部分上的积分之和The UAV network is divided into two cases in polar coordinates. When ri > r 0 , the calculation method is as in formula (6). When ri i r 0 , the integral of f(r) on S is converted into Partial integral, which is the sum of the integrals of the probability density function over the two parts S 1 and S 2
Figure RE-FDA0003133304650000034
Figure RE-FDA0003133304650000034
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