CN103428747A - Aviation self-organizing network wireless link stability prediction method - Google Patents

Abstract

The invention discloses an aviation self-organizing network wireless link stability prediction method. The method includes the steps of firstly, establishing an aviation self-organizing network node mobile model; secondly, determining a node movement rate probability density function in each state and a probability density function of a relative speed between every two nodes according to the movement features of the nodes in the mobile model in each state; thirdly, determining a distribution function of the duration time of a wireless link through combination of the distance between every two nodes and node relative movement direction information on the basis; finally, obtaining stability factors of the multi-hop wireless link according to the distribution function of the duration time of the wireless link, wherein the mobile model is composed of seven states, namely, the acceleration taking off state, the constant-speed rising state, the smooth and steady flying state, the steering state, the constant-speed descending speed, the deceleration stopping state and the static state. The stability factors of the link can be used as the importation foundation for route selection through the airplane nodes, and therefore the wireless transmission link with the high stability is set up for an aviation self-organizing network. The validity of the method is proved through a simulation experiment in the QualNet simulation environment.

Description

A kind of aviation self-organizing network wireless link stability prediction method

Technical field

The invention belongs to field of wireless, particularly aviation self-organizing network wireless link stability prediction method.

Background technology

Wireless self-organization network (Wireless Ad hoc Networks) is a kind of novel wireless communication network that does not rely on static infrastructure, and it is comprised of one group of mobile node with wireless transmitter, and networking fast and flexible, reliability are high.Each node in network is terminal and router, can forward the packet of other node in automatic network.Can be under the procotol of layering be controlled after node start with the mode quickly networking of multi-hop ad hoc, when part of nodes breaks down or destroyed and after quitting work, the operation of whole network can not be affected, thereby network has very strong survivability and self-healing ability.

In recent years, wireless self-organization network has become a kind of important wireless networking mode, in the civilian and military field, all is widely used.Aviation self-organizing network (Aeronautical Ad hoc Networks, AANET) is the product that radio self organizing network technology is applied in the multi-platform networking of aviation.At civil area, the replacement of aviation self-organizing network is expensive, the broadband connections satellite of long time delay, becomes the new selection of following wideband multimedia air communications.The researcher proposes, and between the passenger plane of flight, builds self-organizing network, and the mode that can forward with multi-hop be set up the communication link between aircraft and ground base station, for the passenger provides the aerial broadband the Internet access service of cheap, low time delay on the road.

Aircraft node high-speed mobile (cruising speed is generally between 700km/h to 1000km/h) in the aviation self-organizing network, network topology changes fast, thereby the internodal wireless transmission link of aircraft frequently ruptures.When any hop link in the multi-hop transmission path ruptures, the aircraft node must start route finding process, finds other available transmission paths.A large amount of route finding process has consumed network bandwidth resources greatly, causes that network congestion and data-message transmission time delay increase, and finally causes the degradation of network in general performance.Therefore, in the design of aviation self-organizing network Routing Protocol, must consider the duration of wireless link between node, thereby find by route discovery the wireless transmission link that stability is high, alleviate the impact of network topology change on the network in general performance.

As the above analysis, between the aircraft node, the prediction of wireless link stability is of great significance the overall performance tool that promotes the aviation self-organizing network.Existing wireless link stability prediction method roughly can be divided three classes: the link stability Forecasting Methodology based on received signal power, based on node location, move the link stability Forecasting Methodology of (geometric properties) and the link stability Forecasting Methodology of Based on Probability estimation theory.The basic principle of the link stability Forecasting Methodology based on received signal power is: when node sends packet, radio signal power is the constantly decay with the increase of transmission range in transmitting procedure.After neighbor node receives this signal, according to the distance between the estimation of received signal power and path loss model and sending node, and according to this distance upgrade and sending node between link-state information.The method simple, intuitive, but in actual environment, the propagation of wireless signal may be subject to the impact of the many factors such as atmosphere, sexual intercourse, complex-terrain, deviation inevitably appears in the distance between the sending node obtained by said method estimation and receiving node, and then has influence on final routing strategy.

The basic principle of the link stability prediction of moving based on node location is: according to movable informations such as existing node location, speed, directions, derive and obtain the information of link-state change.The link stability prediction of moving based on node location mainly is divided into again two kinds.The first is based on link Duration Prediction (Prediction based, PBR), and this agreement is as follows to the Forecasting Methodology of link existent time: given two mobile node i and j, define between them the distance be | d _Ij|, corresponding translational speed is respectively v _iAnd v _j, communication distance is R, and definition s is used for distinguishing motion or counter motion in the same way, two internodal vertical width are w, derive thus and obtain the predicted value of link duration between two mobile node i and j.What this Forecasting Methodology was only considered is the situation that movement direction of nodes is identical or contrary, can't solve the problem of two movement direction of nodes not on the same straight line time.Therefore and what this algorithm was only considered is the linear uniform motion of node, can't solve the problems such as acceleration, deceleration, turning of node in the aviation self-organizing network.The second is based on the prediction (Multipath Doppler Routing Algorithm, MUDOR) of many Pood frequency displacement, and Doppler frequency shift is the key factor that the MUDOR agreement is considered.Pass between relative velocity and Doppler frequency shift is v=c (flf ₀-1), wherein c is the light velocity, and f is expected frequency, f ₀It is observed frequency.When the expectation frequency is less than observing frequency, for be defined as-c of doppler values (Doppler Value, the DV) (flf that reflects link load ₀-1), otherwise be defined as+2c (flf ₀-1).Doppler values is less, and link stability is better.Whether the MUDOR algorithm neither needs the support of GPS equipment also reliable without the attenuation model of considering the reception signal.But, due to the impact of only considering relative speed, and ignored link establishment internodal initial distance constantly fully.Thereby it can only carry out qualitative forecasting to link property, and can't obtain the predicted value of link duration accurately.

The link stability Forecasting Methodology of Based on Probability estimation theory is: for any two mobile nodes 1 and 2, the communication distance of node 1 is divided into to the n section, every section represents respectively a state S _i, writ state S _N+1Mean absorbing state, the zone beyond representation node 1 communication range, p _IjMean that node 2 is by state S _iMove to state S after the unit interval _jProbability, define a step transition probability matrix P=|p _Ij|, i, j ∈ [1, n+1].

Mean that communication initial time node 2 is in state S _iProbability, the initial distance distribution matrix of defined node 2

I ∈ [1, n+1], can draw linkage availability and the probability density function of link duration thus, analyzes accordingly link stability.Because a step transition probability matrix in the method is changeless, therefore, the link stability that the method analysis obtains is only relevant with the distribution of node initial distance, and irrelevant with the initial relative speed of node.

Summary of the invention

The objective of the invention is to propose a kind of wireless link stability prediction method that is applicable to the aviation self-organizing network, for Design of Routing Protocol provides important evidence, alleviate the impact of network topology change on the network in general performance.In order to realize this purpose, step of the present invention is:

Step 1: build aviation self-organized network nodes mobility model, mobility model is comprised of seven states: accelerate to take off, constant speed rising, smooth flight, turning, constant speed descend, slowing down stops with static;

Step 2: according to node in above-mentioned mobility model, at the motion feature of each state, determine the node movement rate probability density function in each state;

Step 3: the speed probability density function according to node in each motion state, determine the probability density function of relative speed between node;

Step 4: according to the probability density function of relative speed between node, the distance between node, and node direction of relative movement, determine the distribution function of link duration;

Step 5: according to link duration distribution function, calculate the multi-hop link stability factor.

The wireless link stability prediction method of the aviation self-organizing network that the present invention proposes is realized in the QualNet4.5 network simulation environment.Simulating area is 10000 * 10000 * 1000m ³, network node adds up to 100, in the simulating area random distribution.Physical layer adopts the DSSS model, and simulation time is 300s, and all the other simulation parameters are as shown in table 1.Relevant parameter value in the node motion model is as shown in table 2

Table 1 simulation parameter

Relevant parameter value in table 2 node motion model

Accompanying drawing 4 has provided the theoretical value of the link duration distribution function that the present invention calculates and the contrast of simulation value.The consistency of simulation value and theoretical value has illustrated that the present invention determines the validity of link duration distribution function method between node.Accompanying drawing 5 and accompanying drawing 6 have provided the network performance simulation result contrast before and after the link stability Forecasting Methodology of introducing the present invention's proposition in AODV (ad hoc on-demand distance vector) Routing Protocol.Simulation results show, the link stability Forecasting Methodology of introducing the present invention's proposition in Routing Protocol can effectively provide the saturation throughput performance of aviation self-organizing network, and reduces Packet Delay simultaneously.

The accompanying drawing explanation

Fig. 1 is aviation self-organized network nodes mobility model state transition graph;

Fig. 2 is node relative velocity schematic diagram;

Fig. 3 is that node link duration distribution function calculates schematic diagram;

Fig. 4 is theoretical value and the simulation value comparison diagram distributed link duration of calculating of the present invention;

Fig. 5 is the network throughput performance simulation comparison diagram of introducing the link stability Forecasting Methodology front and back of the present invention's proposition in the AODV Routing Protocol;

Fig. 6 is the network delay performance simulation comparison diagram of introducing the link stability Forecasting Methodology front and back of the present invention's proposition in the AODV Routing Protocol.

Embodiment

Below in conjunction with drawings and Examples, the present invention is described in further detail.

The aviation self-organizing network link stability Forecasting Methodology that the present invention proposes realizes in Wireless Network Simulation environment QualNet4.5.

Below provide the specific embodiment of the invention step:

Step 1: build aviation self-organized network nodes mobility model.

As shown in Figure 1, to be divided into be seven states to this mobility model: accelerate to take off, constant speed rising, smooth flight, turning, constant speed descend, slowing down stops with static.The aircraft node, from accelerating takeoff phase, after speed accelerates to set-point, enters the constant speed ascent stage.After rising to the regulation flying height, aircraft will start cruising flight (smooth flight)., if meet with special circumstances, as prominent, meet thunder and lightning weather awing, aircraft need to be revised course line, and now, aircraft will enter the turning stage.Turn after end, aircraft is got back to regular shipping lines, i.e. the smooth flight state.When being about to arrive destination, aircraft enters the constant speed decline stage.When dropping to ground proximity, aircraft enters the deceleration stop phase, slows down gradually until finally stop, and enters quiescent phase.

The design parameter of each motion state is described as follows:

1, accelerate takeoff phase (α stage)

Targeted rate v _α∈ U[v _{α, min}, v _{α, max}], continue timeslot number α ∈ U[α _min, α _max], the horizontal movement direction

Vertical motion direction θ _α=pi/2.Wherein U means to be uniformly distributed.So acceleration a in this stage _αFor

$a_{\mathrm{\&alpha;}}=\frac{v_{\mathrm{\&alpha;}}-v_{t_0}}{t_{\mathrm{\&alpha;}}-{\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">t</figure-callout>}}_0}=\frac{v_{\mathrm{\&alpha;}}}{\mathrm{\&alpha;\&Delta;t}}---\left(1\right)$

Δ t means time step, and the whole period of motion take Δ t and divided as time slot.

2, constant speed ascent stage (β stage)

After accelerating to finish takeoff phase, aircraft starts to climb at a certain angle, speed v _β=v _α, continue timeslot number β ∈ U[β _min, β _max], the horizontal movement direction

Vertical motion direction θ _β∈ U[0, pi/2].

3, smooth flight (γ stage)

Cruising phase in this stage simulated aircraft motion, aircraft keeps flying speed constant, along the preferential direction level, flies at a constant speed.V _γ=v _β, continue timeslot number γ ∈ U[γ _min, γ _max], θ _γ=pi/2.

4, turn (ζ stage)

In flight course, may need to change original course line because of certain reason, at this moment aircraft will enter the turning stage, and its turning can be approximately uniform circular motion.Before aircraft is turned, according to random selected horizontal movement direction

With vertical motion direction θ _ζ, make uniform circular motion, in this stage, the speed v of aircraft _ζ=v _γ.The turning stage continues timeslot number ζ ∈ U[ζ _min, ζ _max].After the γ stage finishes, aircraft may enter the turning stage, also may enter the constant speed decline η stage because being about to arrive destination.After supposing that each smooth flight finishes, the probability that aircraft enters the turning stage is P _γ-ζ, the probability that enters the constant speed decline stage is P _γ-η, obvious P _γ-ζ+ P _γ-η=1.Suppose that aircraft this process before fall-retarding will carry out K time, K=0,1,2 ...In this case, the γ stage carries out K+1 time, and the ζ stage carries out K time.The desired value of K is as follows

$E\left[K\right]=\underset{K=0}{\overset{\&infin;}{\mathrm{\&Sigma;}}}K{\left(P_{\mathrm{\&gamma;}-\mathrm{\&xi;}}\right)}^K(1-P_{\mathrm{\&gamma;}-\mathrm{\&xi;}})=\frac{P_{\mathrm{\&gamma;}-\mathrm{\&xi;}}}{1-P_{\mathrm{\&gamma;}-\mathrm{\&xi;}}}---\left(2\right)$

5, constant speed descends (η stage)

When being about to arrive destination, enter this stage.In this stage, aircraft is with at the uniform velocity straight line decline of fixed speed, and initial velocity magnitude and direction are respectively: v _η=v _γ,

θ _η∈ U[pi/2, π].Continue timeslot number η ∈ U[η _min, η _max].

6, slow down and stop (d stage)

After arriving ground, aircraft will be decelerated to and stop, and can regard the motion in this stage as uniformly retarded motion.Initial velocity magnitude and direction are respectively: v _d=v _η, θ _d=pi/2.Duration d ∈ U[d _min, d _max], can obtain accordingly acceleration and be

$a_d=\frac{0-v_{\mathrm{\&eta;}}}{\mathrm{d\&Delta;t}}=-\frac{v_{\mathrm{\&eta;}}}{\mathrm{d\&Delta;t}}---\left(3\right)$

7, static (p stage)

The duration p ∈ U[p that aircraft remains static _min, p _max], then start flight next time.

Step 2: the probability density function of determining each stage speed of node.

For constant speed rising, smooth flight, turning and constant speed this four-stage that descends, because the node movement rate all remains unchanged, so the node movement rate distributes to meet and is uniformly distributed, as shown in the formula

$f\left(v\right)=\left\{\begin{array}{cc}\frac{1}{v_{\max }-v_{\min }}& v\&Element;(v_{\min },v_{\max })\\ 0& \mathrm{else}\end{array}\right.---\left(4\right)$

For accelerating takeoff phase, node is with initial velocity v ₀=0, accelerate to given target velocity v _α, v _αEvenly choose from [v _min, v _max].Simultaneously, due to v _α∈ [v _min, v _max], therefore no matter v _αValue is how many, v ∈ [0, v _min] value will occur with probability 1 takeoff phase accelerating, due to even acceleration, so their probability density is also identical, be made as k.And v ∈ [v _min, v _max] can occur in this stage will be by v _αValue determine.V _αValue is in v _minWith v _minProbability between+Δ v is Δ v/ (v _max-v _min), to accelerate in take-off process, speed v comprises v _minThe probability of+Δ v is 1-Δ v/ (v _max-v _min).According to above analysis, can obtain accelerating takeoff phase, the probability density function of node movement rate is as follows

$f_{\mathrm{\&alpha;}}\left(v\right)=\left\{\begin{array}{cc}k,& 0\&le;v\&le;v_{\min }\\ k(1-\frac{v-v_{\min }}{v_{\max }-v_{\min }})& v_{\min }<v\&le;v_{\max }\end{array}\right.---\left(5\right)$

Due to

${\&Integral;}_{-\&infin;}^{+\&infin;}{f}_{\mathrm{\&alpha;}}\left(v\right)\mathrm{dv}=1---\left(6\right)$

Can obtain

$k=\frac{2}{(v_{\max }+v_{\min })}---\left(7\right)$

Thereby formula (5) can be expressed as

$f_{\mathrm{\&alpha;}}\left(v\right)=\left\{\begin{array}{cc}\frac{2}{v_{\max }+v_{\min }},& 0\&le;v\&le;v_{\min }\\ \frac{2}{v_{\max }+v_{\min }}(1-\frac{v-v_{\min }}{v_{\max }-v_{\min }}),& v_{\min }<v\&le;v_{\max }\end{array}\right.---\left(8\right)$

Equally, for the deceleration stop phase, also can obtain the conclusion identical with formula (8) according to being similar to top analysis.

Step 3: the probability density function of determining relative speed between the aircraft node.

The speed of supposing optional two mobile nodes is respectively v ₁, v ₂, as shown in Figure 2, the relative velocity v ' of these two mobile nodes=v ₁-v ₂, angle ω ∈ [0, π], establish v ₁, v ₂, v ' mould be respectively v ₁, v ₂, v ', have

$\left\{\begin{array}{c}{v}^{\&prime;}=\sqrt{{{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1}^2+{{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">v</figure-callout>}}_2}^2-{2v}_1{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">v</figure-callout>}}_2\cos w}\\ w=\mathrm{<figure-callout\; id="1"\; label="arccos\; v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">arccos</figure-callout>}\frac{{v_{}}^{}}{}\frac{{{}_1}^2+{{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">v</figure-callout>}}_2}^2-v^{\&prime;2}}{{2v}_1{v}_2}\end{array}\right.---\left(9\right)$

Due to v ₁, v ₂, ω is separate, so, v ₁, v ₂, ω joint probability density function can be written as

$f_{w,{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1,v_2}(w,{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1,v_2)=\frac{1}{\mathrm{\&pi;}}{f}_{v_1}\left(v_1\right)f_{v_2}\left(v_2\right)---\left(10\right)$

Convert by Jacobi, can obtain v ₁, v ₂, v ' joint probability density function

$f_{v^{\&prime;},{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1,v_2}(v^{\&prime;},{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1,v_2)=f_{w,{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1,v_2}(w,{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1,v_2)\left|\frac{\&PartialD;w}{\&PartialD;v^{\&prime;}}\right|$

$=\frac{2v^{\&prime;}{f}_{w,{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1,v_2}(w,{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1,v_2)}{\sqrt{4{{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1}^2{{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">v</figure-callout>}}_2}^2-{({{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1}^2+{{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">v</figure-callout>}}_2}^2-v^{\&prime;2})}^2}}---\left(11\right)$

$=\frac{2v^{\&prime;}{f}_{v_1}\left(v_1\right)f_{v_2}\left(v_2\right)}{\mathrm{\&pi;}\sqrt{4{{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1}^2{{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">v</figure-callout>}}_2}^2-{({{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1}^2+{{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">v</figure-callout>}}_2}^2-v^{\&prime;2})}^2}}$

The probability density function that can be drawn relative speed v ' by formula (11) is

$f_{v^{\&prime;}}\left(v^{\&prime;}\right)={\&Integral;}_{{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">v</figure-callout>}}_{2,\mathrm{<figure-callout\; id="2"\; label="min\; v"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">min</figure-callout>}}}^{v_{}}{\&Integral;}_{{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="min\; v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">min</figure-callout>}}}^{v_{}}\frac{2v^{\&prime;}{f}_{v_1}\left(v_1\right)f_{v_2}\left(v_2\right)}{\mathrm{\&pi;}\sqrt{4{{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1}^2{{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">v</figure-callout>}}_2}^2-{({{\mathrm{<figure-callout\; id="1"\; label="v"\; filenames="HSA0000094734970000021.png,HSA0000094734970000022.png"\; state="\{\{state\}\}">v</figure-callout>}}_1}^2+{{\mathrm{<figure-callout\; id="2"\; label="v"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">v</figure-callout>}}_2}^2-v^{\&prime;2})}^2}}d{v}_{1}d{v}_2---\left(12\right)$

Definition status S set={ α, beta, gamma, ζ, η, d, p}.Then, the rate distribution of each state obtained according to formula (4) and (8), in conjunction with formula (12), the probability density function that can obtain the relative speed under any two states is

$f_{v^{\&prime;}}^{E,G}\left(v^{\&prime;}\right)={\&Integral;}_{v_{G\min }}^{v_{G\max }}{\&Integral;}_{v_{E\min }}^{v_{E\max }}\frac{2v^{\&prime;}f\left(v_E\right)f\left(v_G\right)}{\mathrm{\&pi;}\sqrt{4{v_E}^2{v_G}^2-{({{\mathrm{<figure-callout\; id="2"\; label="v\; E"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">v</figure-callout>}}_E}^2+{v_G}^2-v^{\&prime;2})}^2}}d{v}_{E}d{v}_G---\left(13\right)$

Wherein, E, G all belongs to S.

Definition p (E), p (G) is respectively node constantly in E, the probability in G stage, the relative speed under aviation self-organizing network nodes mobility model is distributed as

$f_{v^{\&prime;}}\left(v^{\&prime;}\right)=\underset{E\&Element;S}{\mathrm{\&Sigma;}}\underset{G\&Element;S}{\mathrm{\&Sigma;}}p\left(E\right)p\left(G\right)f_{v^{\&prime;}}^{E,G}\left(v^{\&prime;}\right)---\left(14\right)$

Step 4: the distribution function that calculates the link duration.

As shown in Figure 3, consider the wireless link between node 1 and node 2, using node 2 as with reference to node, node 1 and node 2 apart from being d ₀, direction and the d of two node speed of related movement v ' ₀Between angle theta obey and be uniformly distributed at interval [0, π], R is the node transmission range; Use geometric knowledge, obtain the distribution function of link duration

$F\left(t\right)=P(T\&le;t)=P(\frac{d_1}{v^{\&prime;}}\&le;t)=P(\frac{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0\cos \mathrm{\&theta;}+\sqrt{R^2-{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^2{\mathrm{<figure-callout\; id="2"\; label="sin"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">sin</figure-callout>}}^2\mathrm{\&theta;}}}{v^{\&prime;}}\&le;t)$

$\left(15\right)$

$=\underset{\frac{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0\cos \mathrm{\&theta;}+\sqrt{R^2-{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^2{\mathrm{<figure-callout\; id="2"\; label="sin"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">sin</figure-callout>}}^2\mathrm{\&theta;}}}{v^{\&prime;}}\&le;t}{\&Integral;\&Integral;\&Integral;}f(\mathrm{\&theta;},{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0,v^{\&prime;})\mathrm{d\&theta;}{\mathrm{<figure-callout\; id="0"\; label="dd"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">dd</figure-callout>}}_0{\mathrm{dv}}^{\&prime;}$

θ, d ₀, tri-variablees of v ' are separate, therefore formula (15) can be expressed as

$F\left(t\right)=\underset{\frac{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0\cos \mathrm{\&theta;}+\sqrt{R^2-{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^2{\mathrm{<figure-callout\; id="2"\; label="sin"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">sin</figure-callout>}}^2\mathrm{\&theta;}}}{v^{\&prime;}}\&le;t}{\&Integral;\&Integral;\&Integral;}f\left(\mathrm{\&theta;}\right)f\left(d_0\right)f_{v^{\&prime;}}\left(v^{\&prime;}\right)\mathrm{d\&theta;}{\mathrm{<figure-callout\; id="0"\; label="dd"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">dd</figure-callout>}}_0{\mathrm{dv}}^{\&prime;}---\left(16\right)$

The probability density function of θ is

$f\left(\mathrm{\&theta;}\right)=\frac{1}{\mathrm{\&pi;}}---\left(17\right)$

By

$F\left(d_0\right)=P(d\&le;d_0)=\frac{\frac{4}{3}{{\mathrm{\&pi;<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^3}{\frac{4}{3}{\mathrm{\&pi;R}}^3}=\frac{{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^3}{R^3}---\left(18\right)$

Can obtain

$f\left(d_0\right)=\frac{{{3\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^2}{R^3}---\left(19\right)$

By qualifications

$\frac{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0\cos \mathrm{\&theta;}+\sqrt{R^2-{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^2{\mathrm{<figure-callout\; id="2"\; label="sin"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">sin</figure-callout>}}^2\mathrm{\&theta;}}}{v^{\&prime;}}\&le;t---\left(20\right)$

Can obtain

$\cos \mathrm{\&theta;}\&le;\frac{v^{\&prime;2}{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">t</figure-callout>}}^2+{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^2-{\mathrm{<figure-callout\; id="2"\; label="R"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">R</figure-callout>}}^2}{{2v}^{\&prime;}{\mathrm{td}}_0}---\left(21\right)$

Order

k=∫f(θ)dθ (22)

{。##.##1},

$k=\left\{\begin{array}{cc}0& \frac{v^{\&prime;2}{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">t</figure-callout>}}^2+{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^2-{\mathrm{<figure-callout\; id="2"\; label="R"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">R</figure-callout>}}^2}{{2v}^{\&prime;}{\mathrm{<figure-callout\; id="0"\; label="td"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">td</figure-callout>}}_0}\&le;-1\\ \frac{\mathrm{\&pi;}-\arccos \frac{v^{\&prime;2}{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">t</figure-callout>}}^2+{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^2-{\mathrm{<figure-callout\; id="2"\; label="R"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">R</figure-callout>}}^2}{{2v}^{\&prime;}{\mathrm{<figure-callout\; id="0"\; label="td"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">td</figure-callout>}}_0}}{\mathrm{\&pi;}}& -1<\frac{v^{\&prime;2}{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">t</figure-callout>}}^2+{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^2-{\mathrm{<figure-callout\; id="2"\; label="R"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">R</figure-callout>}}^2}{{2v}^{\&prime;}{\mathrm{<figure-callout\; id="0"\; label="td"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">td</figure-callout>}}_0}<1\\ 1& \frac{v^{\&prime;2}{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">t</figure-callout>}}^2+{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^2-{\mathrm{<figure-callout\; id="2"\; label="R"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">R</figure-callout>}}^2}{{2v}^{\&prime;}{\mathrm{<figure-callout\; id="0"\; label="td"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">td</figure-callout>}}_0}\&GreaterEqual;1\end{array}\right.---\left(23\right)$

Therefore, formula (16) can be expressed as

$F\left(t\right)={\&Integral;}_0^{{{2v}^{\&prime;}}_{\max }}{\&Integral;}_0^R\mathrm{kf}\left(d_0\right)f_{v^{\&prime;}}\left(v^{\&prime;}\right){\mathrm{<figure-callout\; id="0"\; label="dd"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">dd</figure-callout>}}_0{\mathrm{dv}}^{\&prime;}---\left(24\right)$

By formula (14), (17), (19), (23) substitution formula (24), can try to achieve the expression formula of link duration distribution function F (t).

Step 5: calculate the link stability factor.

For the stability to wireless link between node, predicted, the present invention has introduced the link stability factor and computational methods thereof.As shown in Figure 3, the present invention adjusts the distance according to the initial phase between node 1 and node 2 and initial velocity is derived and to be drawn link duration T between two nodes ₁₂

$T_{12}=\frac{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0\cos \mathrm{\&theta;}+\sqrt{R^2-{{\mathrm{<figure-callout\; id="0"\; label="d"\; filenames="HSA0000094734970000022.png"\; state="\{\{state\}\}">d</figure-callout>}}_0}^2{\mathrm{<figure-callout\; id="2"\; label="sin"\; filenames="HSA0000094734970000012.png,HSA0000094734970000021.png"\; state="\{\{state\}\}">sin</figure-callout>}}^2\mathrm{\&theta;}}}{v^{\&prime;}}---\left(25\right)$

Then by T ₁₂Be to compare 0.9 o'clock corresponding link duration with link duration distribution function F (t) value, obtain

${\hat{T}}_{12}=\frac{T_{12}}{T_{F\left(t\right)=0.9}}---\left(26\right)$

The present invention is defined as the wireless link stability factor of 2 of node 1 and nodes

$S_{12}=\min ({\hat{T}}_{12},1)---\left(27\right)$

For the multi-hop wireless link be comprised of (N-1) bar one hop link, its wireless link stability factor is defined as the product of many one hop link stability factors,

S _1N=S ₁₂S ₂₃S ₃₄…S _(N-1)N (28)

The wireless link stability factor can be used for predicting the stability state of current wireless link.In actual applications, determine F (t) according to the actual value of each motion state parameters in the aircraft mobility model, then calculated the stability factor of wireless link between node by formula (28), and the important evidence using it as Route Selection, thereby set up the wireless transmission link that stability is high for the aviation self-organized network nodes.

The content be not described in detail in the present patent application book belongs to the known prior art of professional and technical personnel in the field.

Claims (6)

1. an aviation self-organizing network wireless link stability prediction method, the step adopted is:

Step 1: build aviation self-organized network nodes mobility model, mobility model is comprised of seven states: accelerate to take off, constant speed rising, smooth flight, turning, constant speed descend, slowing down stops with static;

Step 2: according to node in above-mentioned mobility model, at the motion feature of each state, determine the node movement rate probability density function in each state;

Step 3: the speed probability density function according to node in each motion state, determine the probability density function of relative speed between node;

Step 4: according to the probability density function of relative speed between node, the distance between node, and node direction of relative movement, determine the distribution function of link duration;

Step 5: according to link duration distribution function, calculate the multi-hop link stability factor.

2. a kind of aviation self-organizing network wireless link stability prediction method according to claim 1 is characterized in that the concrete construction method of aviation self-organized network nodes mobility model is:

The aircraft node, from accelerating takeoff phase, after speed accelerates to set-point, enters the constant speed ascent stage, and after rising to the regulation flying height, aircraft will start cruising flight (smooth flight); , if meet with special circumstances, as prominent, meet thunder and lightning weather awing, aircraft need to be revised course line, and now, aircraft will enter the turning stage; Turn after end, aircraft is got back to regular shipping lines, i.e. the smooth flight state; When being about to arrive destination, aircraft enters the constant speed decline stage, and when dropping to ground proximity, aircraft enters the deceleration stop phase, slows down gradually until finally stop, and enters quiescent phase;

The design parameter of each motion state is described as follows:

(1) accelerate takeoff phase (α stage)

Targeted rate v _α∈ U[v _{α, min}, v _{α, max}], continue timeslot number α ∈ U[α _min, α _max], the horizontal movement direction

Vertical motion direction θ _α=pi/2; Wherein U means to be uniformly distributed; So acceleration a in this stage _αFor:

$a_{\mathrm{\&alpha;}}=\frac{v_{\mathrm{\&alpha;}}-{v_t}_0}{t_{\mathrm{\&alpha;}}-t_0}=\frac{v_{\mathrm{\&alpha;}}}{\mathrm{\&alpha;\&Delta;t}}---\left(1\right)$

Δ t means time step, and the whole period of motion take Δ t and divided as time slot;

(2) constant speed ascent stage (β stage)

After accelerating to finish takeoff phase, aircraft starts to climb at a certain angle, speed v _β=v _α, continue timeslot number β ∈ U[β _min, β _max], the horizontal movement direction

Vertical motion direction θ _β∈ U[O, pi/2];

(3) smooth flight (γ stage)

Cruising phase in this stage simulated aircraft motion, aircraft keeps flying speed constant, along the preferential direction level, flies at a constant speed; v _γ=v _β, continue timeslot number γ ∈ U[γ _min, γ _max], θ _γ=pi/2;

(4) turn (ζ stage)

In flight course, may need to change original course line because of certain reason, at this moment aircraft will enter the turning stage, and its turning can be approximately uniform circular motion; Before aircraft is turned, according to random selected horizontal movement direction

With vertical motion direction θ _ζ, make uniform circular motion, in this stage, the speed v of aircraft _ζ=v _γ, the turning stage continues timeslot number ζ ∈ U[ζ _min, ζ _max]; After the γ stage finishes, aircraft may enter the turning stage, also may enter the constant speed decline η stage because being about to arrive destination; After supposing that each smooth flight finishes, the probability that aircraft enters the turning stage is P _γ-ζ, the probability that enters the constant speed decline stage is P _γ-η, obvious P _γ-ζ+ P _γ-η=1; Suppose that aircraft this process before fall-retarding will carry out K time, K=0,1,2 ..., in this case, the γ stage carries out K+1 time, and the ζ stage carries out K time; The desired value of K is as follows:

$E\left[K\right]=\underset{K=0}{\overset{\&infin;}{\mathrm{\&Sigma;}}}K{\left(P_{\mathrm{\&gamma;}-\mathrm{\&xi;}}\right)}^K(1-P_{\mathrm{\&gamma;}-\mathrm{\&xi;}})=\frac{P_{\mathrm{\&gamma;}-\mathrm{\&xi;}}}{1-P_{\mathrm{\&gamma;}-\mathrm{\&xi;}}}---\left(2\right)$

(5) constant speed descends (η stage)

When being about to arrive destination, enter this stage; In this stage, aircraft is with at the uniform velocity straight line decline of fixed speed, and initial velocity magnitude and direction are respectively: v _η: v _γ,

θ _η∈ U[pi/2, π]; Continue timeslot number η ∈ U[η _min, η _max];

(6) slow down and stop (d stage)

After arriving ground, aircraft will be decelerated to and stop, and can regard the motion in this stage as uniformly retarded motion; Initial velocity magnitude and direction are respectively: v _d=v _η,

θ _d=pi/2; Duration d ∈ U[d _min, d _max], can obtain accordingly acceleration and be

$a_d=\frac{0-v_{\mathrm{\&eta;}}}{\mathrm{d\&Delta;t}}=-\frac{v_{\mathrm{\&eta;}}}{\mathrm{d\&Delta;t}}---\left(3\right)$

(7) static (p stage)

The duration p ∈ U[p that aircraft remains static _min, p _max], then start flight next time.

3. a kind of aviation self-organizing network wireless link stability prediction method according to claim 1 is characterized in that in each state, the concrete of node movement rate probability density function determines that method is:

For constant speed rising, smooth flight, turning and constant speed this four-stage that descends, because the node movement rate all remains unchanged, so the node movement rate distributes to meet and is uniformly distributed, as shown in the formula:

$f\left(v\right)=\left\{\begin{array}{cc}\frac{1}{v_{\max }-v_{\min }}& v\&Element;(v_{\min },v_{\max })\\ 0& \mathrm{else}\end{array}\right.---\left(4\right)$

Accelerating takeoff phase, node is with initial velocity v ₀=O, evenly accelerate to given target velocity v _α, v _αEvenly choose from [v _min, v _max]; At the deceleration stop phase, node is with initial velocity v _α, evenly decelerate to O, v _αEvenly choose from [v _min, v _max]; Therefore, accelerating takeoff phase and deceleration stop phase, the probability density function of node movement rate is:

$f_{\mathrm{\&alpha;}}\left(v\right)=\left\{\begin{array}{cc}\frac{2}{v_{\max }+v_{\min }},& 0\&le;v\&le;v_{\min }\\ \frac{2}{v_{\max }+v_{\min }}(1-\frac{v-v_{\min }}{v_{\max }-v_{\min }}),& v_{\min }<v\&le;v_{\max }\end{array}\right.---\left(5\right)$

Can obtain node movement rate probability density function in each state by said process.

4. a kind of aviation self-organizing network wireless link stability prediction method according to claim 1 is characterized in that the concrete of probability density function of relative speed between node determines that method is:

The speed of supposing optional two mobile nodes is respectively v ₁, v ₂, the relative velocity v ' of these two mobile nodes=v ₁-v ₂, angle ω ∈ [0, π], establish v ₁, v ₂, v ' mould be respectively v ₁, v ₂, v ', have

$\left\{\begin{array}{c}{v}^{\&prime;}=\sqrt{{v_1}^2+{v_2}^2-2v_1{v}_2\cos w},\\ w=\arccos \frac{{v_1}^2+{v_2}^2-{v\&prime;}^2}{2v_1{v}_2}\&CenterDot;\end{array}\right.---\left(6\right)$

Due to v ₁, v ₂, ω is separate, so, v ₁, v ₂, ω joint probability density function can be written as

$f_{w,v_1,v_2}(w,v_1,v_2)=\frac{1}{\mathrm{\&pi;}}{f}_{v_1}\left(v_1\right)f_{v_2}\left(v_2\right)\&CenterDot;---\left(7\right)$

Convert by Jacobi, can obtain v ₁, v ₂, v ' joint probability density function

$f_{v^{\&prime;}{,v}_1,v_2}(v^{\&prime;},v_1,v_2)=f_{w,v_1,v_2}(w,v_1,v_2)\left|\frac{\&PartialD;w}{\&PartialD;v^{\&prime;}}\right|$

$=\frac{2v^{\&prime;}{f}_{w,v_1,v_2}(w,v_1,v_2)}{\sqrt{4{v_1}^2{v_2}^2-{({v_1}^2+{v_2}^2-v^{\&prime;2})}^2}}---\left(8\right)$

$=\frac{2v^{\&prime;}{f}_{v_1}\left(v_1\right)f_{v_2}\left(v_2\right)}{\mathrm{\&pi;}\sqrt{4{v_1}^2{v_2}^2-{({v_1}^2+{v_2}^2-v^{\&prime;2})}^2}}\&CenterDot;$

The probability density function that can be drawn relative speed v ' by formula (8) is

$f_{v^{\&prime;}}(v^{\&prime;})={\&Integral;}_{v_{2,\min }}^{v_{2,\max }}{\&Integral;}_{v_{1,\min }}^{v_{1,\max }}\frac{2v^{\&prime;}{f}_{v_1}\left(v_1\right)f_{v_2}\left(v_2\right)}{\mathrm{\&pi;}\sqrt{4{v_1}^2{v_2}^2-{({v_1}^2+{v_2}^2-v^{\&prime;2})}^2}}{\mathrm{dv}}_1{\mathrm{dv}}_2---\left(9\right)$

Definition status S set={ α, beta, gamma, ζ, η, d, p}; Then, the rate distribution of each state obtained according to formula (4) and (5), in conjunction with formula (9), the probability density function that can obtain the relative speed under any two states is

$f_{v^{\&prime;}}^{E,G}(v^{\&prime;})={\&Integral;}_{v_{G\min }}^{v_{G\max }}{\&Integral;}_{v_{E\min }}^{v_{E\max }}\frac{2v^{\&prime;}f\left(v_E\right)f\left(v_G\right)}{\mathrm{\&pi;}\sqrt{4{v_E}^2{v_G}^2-{({v_E}^2+{v_G}^2-v^{\&prime;2})}^2}}{\mathrm{dv}}_E{\mathrm{dv}}_G---\left(10\right)$

Wherein, E, G all belongs to S;

Definition p (E), p (G) is respectively node constantly in E, the probability in G stage, the node relative speed distributes and can be expressed as:

$f_{v^{\&prime;}}(v^{\&prime;})=\underset{E\&Element;S}{\mathrm{\&Sigma;}}\underset{G\&Element;S}{\mathrm{\&Sigma;}}p\left(E\right)p\left(G\right)f_{v^{\&prime;}}^{E,G}(v^{\&prime;})---\left(11\right)$

Can obtain the probability density function of node movement rate in each state by said process.

5. a kind of aviation self-organizing network wireless link stability prediction method according to claim 1 is characterized in that between node, the concrete of wireless link duration distribution function determines that method is:

Consider the wireless link between node 1 and node 2, using node 2 as with reference to node, node 1 and node 2 apart from being d ₀, direction and the d of two node speed of related movement v ' ₀Between angle be θ, R is the node transmission range; Use geometric knowledge, obtain the distribution function of link duration

$F\left(t\right)=P(T\&le;t)=P(\frac{d_1}{v^{\&prime;}}\&le;t)=P(\frac{d_0\cos \mathrm{\&theta;}+\sqrt{R^2+{d_0}^2{\sin }^2\mathrm{\&theta;}}}{v^{\&prime;}}\&le;t)$

$=\underset{\frac{d_0\cos \mathrm{\&theta;}+\sqrt{R^2-{d_0}^2{\sin }^2\mathrm{\&theta;}}}{v^{\&prime;}}\&le;t}{\&Integral;\&Integral;\&Integral;}f(\mathrm{\&theta;},d_0,v^{\&prime;})\mathrm{d\&theta;d}{d}_{0}d{v}^{\&prime;}---\left(12\right)$

θ, d ₀, tri-variablees of v ' are separate, therefore formula (12) can be expressed as

$F\left(t\right)=\underset{\frac{d_0\cos \mathrm{\&theta;}+\sqrt{R^2-{d_0}^2{\sin }^2\mathrm{\&theta;}}}{v^{\&prime;}}\&le;t}{\&Integral;\&Integral;\&Integral;}f\left(\mathrm{\&theta;}\right)f\left(d_0\right)f_{v^{\&prime;}}(v^{\&prime;})\mathrm{d\&theta;d}{d}_{0}d{v}^{\&prime;}---\left(13\right)$

θ obeys and is uniformly distributed at interval [0, π], and its probability density function is

$f\left(\mathrm{\&theta;}\right)=\frac{1}{\mathrm{\&pi;}}---\left(14\right)$

By

$F\left(d_0\right)=P(d\&le;d_0)=\frac{\frac{4}{3}\mathrm{\&pi;}{d_0}^3}{\frac{4}{3}\mathrm{\&pi;}{R}^3}=\frac{{d_0}^2}{R^3}---\left(15\right)$

Can obtain

$f\left(d_0\right)=\frac{3{d_0}^2}{R^3}---\left(16\right)$

By qualifications

$\frac{d_0\cos \mathrm{\&theta;}+\sqrt{R^2-{d_0}^2{\sin }^2\mathrm{\&theta;}}}{v^{\&prime;}}\&le;t---\left(17\right)$

Can obtain

$\cos \mathrm{\&theta;}\&le;\frac{v^{\&prime;2}{t}^2+{d_0}^2-R^2}{2v^{\&prime;}t{d}_0}---\left(18\right)$

Order

k＝∫f(θ)dθ (19)

{。##.##1},

$k=\left\{\begin{array}{cc}0& \frac{v^{\&prime;2}{t}^2+{d_0}^2-R^2}{2v^{\&prime;}t{d}_0}\&le;-1\\ \frac{\mathrm{\&pi;}-\arccos \frac{v^{\&prime;2}{t}^2+{d_0}^2-R^2}{2v^{\&prime;}t{d}_0}}{\mathrm{\&pi;}}& -1<\frac{v^{\&prime;2}{t}^2+{d_0}^2-R^2}{2v^{\&prime;}t{d}_0}<1\\ 1& \frac{v^{\&prime;2}+t^2{+d_0}^2-R^2}{2v^{\&prime;}t{d}_0}\&GreaterEqual;1\end{array}\right.---\left(20\right)$

Therefore, formula (13) can be expressed as

$F\left(t\right)={\&Integral;}_0^{{2v^{\&prime;}}_{\max }}{\&Integral;}_0^R\mathrm{kf}\left(d_0\right)f_{v^{\&prime;}}(v^{\&prime;})d{d}_{0}d{v}^{\&prime;}---\left(21\right)$

By formula (11), (14), (16), (20) substitution formula (21), can try to achieve the expression formula of link duration distribution function F (t).

6. a kind of aviation self-organizing network wireless link stability prediction method according to claim 1 is characterized in that between node, the concrete of wireless link duration distribution function determines that method is:

Consider the wireless link between node 1 and node 2, using node 2 as with reference to node, node 1 and node 2 apart from being d ₀, direction and the d of two node speed of related movement v ' ₀Between angle be θ, R is the node transmission range; The duration T of wireless link between node 1 and node 2 ₁₂For

$T_{12}=\frac{d_0\cos \mathrm{\&theta;}+\sqrt{R^2-{d_0}^2{\sin }^2\mathrm{\&theta;}}}{v^{\&prime;}}---\left(22\right)$

By T ₁₂Be to compare 0.9 o'clock corresponding link duration with link duration distribution function F (t) value, obtain

${\hat{T}}_{12}=\frac{T_{12}}{T_{F\left(t\right)=0.9}}---\left(23\right)$

The present invention is defined as the wireless link stability factor of 2 of node 1 and nodes

$S_{12}=\min ({\hat{T}}_{12},1)---\left(24\right)$

For the multi-hop wireless link be comprised of (N-1) bar one hop link, its wireless link stability factor is defined as the product of many one hop link stability factors,

S _1N=S ₁₂S ₂₃S ₃₄…S _(N-1)N (25)

The wireless link stability factor can be used for predicting the stability state of current wireless link, in actual applications, determine F (t) according to the actual value of each motion state parameters in the aircraft mobility model, then calculated the stability factor of wireless link between node by formula (25), and the important evidence using it as Route Selection, thereby set up the wireless transmission link that stability is high for the aviation self-organized network nodes.

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