CN111050301A - Unmanned aerial vehicle network OLSR routing self-adaptive strategy based on dynamic topology - Google Patents

Unmanned aerial vehicle network OLSR routing self-adaptive strategy based on dynamic topology Download PDF

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CN111050301A
CN111050301A CN201911295378.8A CN201911295378A CN111050301A CN 111050301 A CN111050301 A CN 111050301A CN 201911295378 A CN201911295378 A CN 201911295378A CN 111050301 A CN111050301 A CN 111050301A
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CN111050301B (en
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米志超
姜雨卿
王海
于卫波
李艾静
赵宁
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Army Engineering University of PLA
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/18—Communication route or path selection, e.g. power-based or shortest path routing based on predicted events
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/34—Modification of an existing route
    • H04W40/38—Modification of an existing route adapting due to varying relative distances between nodes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

An unmanned aerial vehicle network OLSR routing self-adaption strategy based on dynamic topology relates to the technical field of unmanned aerial vehicle network OLSR routing self-adaption strategies. The method comprises the steps of obtaining a simulation result of the next state of a node by using a Kalman filtering algorithm; calculating the current distance N between nodes and the predicted distance M between nodes; and obtaining the broadcasting periods of the Hello messages of the two nodes, reducing the broadcasting periods of the two remaining Hello messages to an optimal value for the first time of receiving the Hello message of the new node, and simultaneously not receiving any new Hello message any more. When the unmanned aerial vehicle cluster frequently moves in the network, the node predicts the state of the linear mobile node through a Kalman filtering algorithm, and the routing protocol can dynamically detect whether the topology changes and adaptively adjust the broadcast period of the control message.

Description

Unmanned aerial vehicle network OLSR routing self-adaptive strategy based on dynamic topology
Technical Field
The invention relates to the technical field of unmanned aerial vehicle network OLSR routing self-adaption strategies based on dynamic topology.
Background
In a network of multi-drone nodes, movement of any node may cause changes in the network topology. Frequent changes in the topology of the mobile network can cause large fluctuations, and even severe degradation, in the overall network performance. The routing protocol applicable to the unmanned aerial vehicle self-organizing network can dynamically detect the topology change, which is one of the most important research targets in the field. Currently, the existing main dynamic routing protocols, such as a routing protocol OSPF based on a link state, a routing protocol RIP based on a distance vector, or a routing protocol OLSR based on an optimized link state, interact with each other through a periodic or triggered communication relationship to obtain a network topology structure, and when a network changes, a new topology table or routing table can be calculated through the updated communication relationship.
The traditional routing protocol uses a fixed broadcast period of Hello messages to detect network topology, obviously, the strategy can not self-adaptively adjust routing parameters according to the change of dynamic topology, and the strategy is too rigid in the military tactics of unmanned aerial vehicle cluster dynamic networking. The traditional routing protocol only initiates topology updating messages, such as Hello and TC messages, when the node link on-off relationship changes. Under the environment of rapid movement of nodes, a certain time is needed for the topology updating messages to propagate to the network, during the period, part of routes in the original routing table are old failed routes, and part of data is forwarded and lost due to errors of the routing table. In addition, in the case where the network is not fully connected, the data may not be sent out because there is no route. These conditions will reduce the throughput of the network and increase the transmission delay of the data.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle network OLSR routing self-adaptive strategy based on dynamic topology, when an unmanned aerial vehicle cluster frequently moves in a network, a node predicts the state of a linear mobile node through a Kalman filtering algorithm, and a routing protocol can dynamically detect whether the topology changes or not and adaptively adjust the broadcasting period of a control message of the topology. The time point of the task does not need to be scheduled in advance, the network topology can be predicted actively, and the corresponding routing parameters can be adjusted in a self-adaptive mode once the trend of the topology change is found.
An unmanned aerial vehicle network OLSR routing self-adaptive strategy based on dynamic topology comprises the following steps:
step 1) obtaining a simulation result of the next state of the node by using a Kalman filtering algorithm;
step 2) calculating the current distance N between nodes and the predicted distance M between nodes;
step 3) judging whether the current node-node distance N is greater than or equal to the maximum effective distance L and smaller than R, and meanwhile, the predicted node-node distance M is greater than R, wherein R is the communication range of the node, if so, executing the next step, otherwise, executing the seventh step;
step 4) obtaining
Figure BDA0002320360910000021
Wherein
Figure BDA0002320360910000022
In the formula, H is a broadcasting period of Hello messages of two nodes, T is the maximum effective communication time of the nodes, R is the communication range of the nodes, N is the current distance between the nodes, and v is the relative speed of a neighbor node;
step 5), the routing protocol sends third Hello information, if so, the next step is executed, and if not, the previous step is returned;
step 6) setting
Figure BDA0002320360910000023
And return toA first step; wherein
Figure BDA0002320360910000024
In the formula, H is a broadcasting period of Hello messages of two nodes, T is the maximum effective communication time of the nodes, R is the communication range of the nodes, N is the current distance between the nodes, and v is the relative speed of a neighbor node;
step 7) judging whether the node receives the Hello information of the new node for the first time, if so, executing the next step, and if not, returning to the first step;
step 8) reducing H of the remaining two pieces of Hello information to an optimal value, and not receiving any new Hello information any more;
step 9) judging the result of the step 8), if so, restoring H to a default value, and simultaneously returning to the first step; if not, returning to the step 7).
By adopting the technical scheme, when the unmanned aerial vehicle cluster frequently moves in the network, the state of the linear mobile node is predicted by the node through the Kalman filtering algorithm, and the routing protocol can dynamically detect whether the topology changes and adaptively adjust the broadcast period of the control message. The time point of the task does not need to be scheduled in advance, the network topology can be predicted actively, and the corresponding routing parameters can be adjusted in a self-adaptive mode once the trend of the topology change is found.
Drawings
Fig. 1 is a schematic diagram of drone node prediction in a drone network.
Fig. 2 is a schematic flow chart of a routing calculation method based on dynamic topology according to the present invention.
FIG. 3 is a flow diagram of the simulation platform of the present invention.
FIG. 4 is a graph illustrating the results of a simulation experiment on node throughput at different time periods of node flight.
Fig. 5 is a schematic diagram of a test result of a simulation experiment on a node packet loss rate at different time periods of node flight.
Fig. 6 is a result diagram of the number of Hello message transmissions in the Exata simulation process.
Fig. 7a is a diagram comparing the DT-OLSR protocol with the original OLSR protocol when the maximum effective communication time is 500 ms.
FIG. 7b is a diagram comparing the DT-OLSR protocol with the original OLSR protocol when the maximum effective communication time is 200 ms.
Fig. 7c is a diagram comparing the DT-OLSR protocol with the original OLSR protocol when the maximum effective communication time is 100 ms.
Fig. 8a is a graph comparing throughput using DT-OLSR protocol and using original OLSR protocol based on the same number of nodes of ping-pong ball movement model.
Fig. 8b is a graph comparing packet loss rate using DT-OLSR protocol and using original OLSR protocol when the number of nodes is the same based on the ping-pong ball movement model.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings:
as shown in fig. 2, an adaptive policy for routing in an OLSR network based on a dynamic topology includes the following steps:
step 1) obtaining a simulation result of the next state of the node by using a Kalman filtering algorithm;
step 2) calculating the current distance N between nodes and the predicted distance M between nodes;
step 3) judging whether the current node-node distance N is greater than or equal to the maximum effective distance L and smaller than R, and meanwhile, the predicted node-node distance M is greater than R, wherein R is the communication range of the node, if so, executing the next step, otherwise, executing the seventh step;
step 4) obtaining
Figure BDA0002320360910000041
Wherein
Figure BDA0002320360910000042
In the formula, H is a broadcasting period of Hello messages of two nodes, T is the maximum effective communication time of the nodes, R is the communication range of the nodes, N is the current distance between the nodes, and v is the relative speed of a neighbor node;
step 5), the routing protocol sends third Hello information, if so, the next step is executed, and if not, the previous step is returned;
step 6) setting
Figure BDA0002320360910000051
And returning to the first step; wherein
Figure BDA0002320360910000052
In the formula, H is a broadcasting period of Hello messages of two nodes, T is the maximum effective communication time of the nodes, R is the communication range of the nodes, N is the current distance between the nodes, and v is the relative speed of a neighbor node;
step 7) judging whether the node receives the Hello information of the new node for the first time, if so, executing the next step, and if not, returning to the first step;
step 8) reducing H of the remaining two pieces of Hello information to an optimal value, and not receiving any new Hello information any more;
step 9) judging the result of the step 8), if so, restoring H to a default value, and simultaneously returning to the first step; if not, returning to the step 7).
The specific process of the step 2) of the invention is as follows:
the node receives the Hello message, obtains the coordinates (x, y) and the relative speed v of the neighbor node, and calculates the distance N between the node and the neighbor node at the moment as
Figure BDA0002320360910000053
Calculating the coordinates (x ', y ') and the relative speed v ' of the next moment node by the Kalman filtering algorithm, and calculating the distance M between the next moment node and the adjacent node as
Figure BDA0002320360910000054
Figure BDA0002320360910000055
D in the formulaijIs the distance, x, between two nodesiIs the abscissa, x, of node ijIs the abscissa, y, of node jiIs longitudinal to node iCoordinate, yjIs the ordinate of node j.
The specific process of the step 3) of the invention is as follows:
when in use
Figure BDA0002320360910000061
And is
Figure BDA0002320360910000062
And if the second node B has a certain distance from the communication boundary of the first node A and topology disturbance in the range is ignored, keeping the Hello message broadcasting period H of the two nodes unchanged, wherein the distance is called as the maximum effective distance L and is preset through DT-OLSR.
The specific process of the step 4) of the invention is as follows:
when the second node B moves to the maximum effective distance L, according to the fact that the effective time 3H of sending the Hello message is less than or equal to the maximum effective communication time T of the node, namely 0 < 3H is less than or equal to T, H back-stepping is estimated to be
Figure BDA0002320360910000063
Wherein
Figure BDA0002320360910000064
The specific process of step 6) of the invention is as follows:
when the second node B is in
Figure BDA0002320360910000065
After the first two Hello message packets are sent in the broadcast period, the DT-OLSR protocol is according to the current effective time
Figure BDA0002320360910000066
A final refinement of H, i.e.
Figure BDA0002320360910000067
H maintains this high frequency until the first node a updates the routing table.
The specific processes of the step 7) and the step 8) of the invention are as follows:
if the third node C is to be added into the communication range of the first node a, the third node C usually needs to send three Hello messages to be added into the routing table; when the first node A receives the first strange Hello message once, the broadcast period of the two remaining Hello messages is reduced to an optimal value, and H maintains the frequency until the first node A finishes updating the routing table
Step S101: the node receives the Hello message, obtains the coordinates (x, y) and the relative speed v of the neighbor node, and calculates the distance between the node and the neighbor node at the moment
Figure BDA0002320360910000068
Calculating the coordinates (x ', y ') and the relative speed v ' of the next moment node calculated by the Kalman filtering algorithm, and calculating the distance between the next moment node and the adjacent node
Figure BDA0002320360910000069
Step S102: when in use
Figure BDA00023203609100000610
And is
Figure BDA00023203609100000611
And if the distance between the node B and the communication boundary of the node A is still a certain distance, and the topology disturbance in the range can be ignored, and the Hello message broadcasting period H of the node is kept unchanged. The distance is called as the maximum effective distance L and is preset through DT-OLSR;
step S103: when the second node B moves to the maximum effective distance L, the effective time 3H for sending the Hello message is not more than the maximum effective communication time T of the node, namely 0 < 3H ≦ T, so that the H back-stepping is estimated to be
Figure BDA0002320360910000071
Wherein
Figure BDA0002320360910000072
Step S104: when the second node B is in
Figure BDA0002320360910000073
After the first two Hello message packets are sent in the broadcast period, the DT-OLSR protocol is according to the current effective time
Figure BDA0002320360910000074
A final refinement of H, i.e.
Figure BDA0002320360910000075
H maintains this high frequency until the first node a updates the routing table.
Step S105: if the third node C were to join the communication range of the first node a, it would normally need to send three Hello messages to be added into the routing table. When the first node a receives the first strange Hello message once, the broadcast period of the two remaining Hello messages is reduced to an optimal value. H maintains this frequency until the first node a completes the update of the routing table.
Step S106: the period of the broadcast of the Hello message is recalled to a default value after the nodes have confirmed that the network topology remains relatively static.
In large-scale UANET, the unmanned aerial vehicle node can perform ideal prediction on the position of the next state of the node through a Kalman filtering algorithm. The Kalman filtering algorithm can realize the prediction of the next second topological change by using the transformation of a state matrix according to the conditions of the current states of the source node and the target node, such as coordinate position, speed, acceleration and the like. The Kalman filtering algorithm is as follows:
Figure BDA0002320360910000076
Figure BDA0002320360910000077
Figure BDA0002320360910000078
where k represents a discrete time instant at which the node sends the Hello message,
Figure BDA0002320360910000079
is a state vector of the movement of the node,
Figure BDA0002320360910000081
is the node movement prediction state vector at the next discrete time to send the Hello message. x and y are the position coordinates of the nodes, vxAnd vyIs the relative velocity of the nodes in different directions. Phi is the state transition matrix, B is the noise matrix, H is the observation matrix,
Figure BDA0002320360910000082
is the acceleration of the unmanned aerial vehicle, is regarded as white noise processing in a short time,
Figure BDA0002320360910000083
are observation vectors that can be obtained from the GPS system.
Figure BDA0002320360910000084
Is the observation noise, determined by the accuracy of the GPS device.
Fig. 1 is a schematic diagram illustrating the prediction of nodes of drones in the drone network according to the present invention.
Figure BDA0002320360910000085
Is the distance between the nodes and is,
Figure BDA0002320360910000086
the distance between nodes after prediction by the Kalman filtering algorithm is shown, and both are calculated by formula (4).
Figure BDA0002320360910000087
R is the communication range of the node. By comparison
Figure BDA0002320360910000088
And R, we can further design an adaptive routing policy based on dynamic topology.
In military wars, drones sometimes need to reconnaissance the surrounding environment to obtain important military spyware and related combat information. In order to collect more abundant and comprehensive data information, the information operation department needs to arrange unmanned aerial vehicles in a plurality of positions and collect information at fixed points. The operation headquarters passes through the whole military information network of unmanned aerial vehicle straight line crossing that a removal, communicates with every reconnaissance unmanned aerial vehicle, reaches the operation destination with the data that all fixed point unmanned aerial vehicles gathered. In order to be close to reality, the scenes are simulated in the whole course in the Exata simulation software, and nodes all adopt a waypoint moving model. A plurality of nodes remain static, simulating a reconnaissance unmanned aerial vehicle. In addition, an unmanned aerial vehicle with a flight track crossing the whole unmanned aerial vehicle network topology is arranged, and the unmanned aerial vehicle is simulated to collect information. In a simulation scene range of 1.5km by 1.5km, an unmanned aerial vehicle at the farthest end in the topology and an unmanned aerial vehicle in flight are selected for carrying out fixed Bit Rate (CBR) service in an experiment, and the flow Rate is 1.04 Mbps. Traffic flow was generated starting from the 10 th second for 33 seconds, and the simulation lasted 43 seconds. Each protocol with different parameters was simulated 100 times independently and then the network performance of the protocols before and after improvement was compared.
The DT-OLSR protocol was simulated and validated using Matlab and exata5.1 simulation software. In order to enable the Kalman filtering algorithm to obtain the relevant information of the nodes, the invention builds a Socket communication service between Matlab and the Exata simulation platform. And (3) introducing information such as node coordinates, relative speed and the like in the Exata software into Matlab software through UDP communication, then simulating in a Matlab simulation platform according to a Kalman filtering algorithm, predicting position information of nodes and neighbor nodes of the unmanned aerial vehicle, and calculating the distance between the nodes through a formula (4). And transmitting data obtained by Matlab simulation to an Exata simulation platform, wherein the Exata adjusts the parameters of the current routing protocol by using a dynamic topology-based adaptive routing algorithm. The simulation flow chart is shown in fig. 3.
The exata5.1 simulation software used the original OLSR protocol with Hello message broadcast period of constant 2s to perform comparative experiments with the modified DT-OLSR protocol. The unmanned aerial vehicle of simulation experiment uses omnidirectional antenna, and the channel bandwidth is 2Mbps, and communication range is about 340 m. The MAC layer adopts an IEEE802.11 protocol and is accessed in a CSMA/CA mode. Specific experimental parameters are shown in table 1.
TABLE 1
Item categories Value taking
Antenna type Omnidirectional antenna
Mobile model Fixed point/linear movement
Communication range 340m
Extent of simulation scenario 1.5km*1.5km
Type of service data CBR(255～1040Kb/s)
Channel bandwidth 2Mbps
Number of nodes 5～40
Speed of movement 15m/s
The method uses the Exata simulation software to randomly distribute a maximum of 40 nodes on a map to form a large-scale UANET. The nodes all adopt a ping-pong ball movement model to carry out linear movement, and the scene of large-scale reconnaissance of enemy and battlefield environments of unmanned aerial vehicle swarm is simulated. The simulation configuration is as above, and the maximum effective time is 200ms with the best performance improvement effect. We tested the network performance when the number of nodes was 5, 10, 20, 30, 40, respectively.
FIG. 4 is a graph illustrating the results of a simulation experiment on node throughput at different time periods of node flight. Fig. 5 is a schematic diagram of a test result of a simulation experiment on a node packet loss rate at different time periods of node flight. The time in the suffix brackets of the DT-OLSR in the figure is the maximum effective communication time of the nodes preset by the DT-OLSR protocol.
Fig. 6 is a result diagram of the number of Hello message transmissions in the Exata simulation process. The result shows that the number of the Hello messages sent reaches a peak value when the node flight time is 30s, and the shorter the maximum effective time is, the more Hello messages are sent by the DT-OLSR protocol. Therefore, the maximum effective time of the DT-OLSR protocol is not as small as possible. Although increasing the sending frequency of the Hello message may have certain benefits on the stability of the network performance, the excessive frequency may also increase the overhead of the node, increase the burden of the channel, and make the objective of the routing policy contradict.
Fig. 7 is a schematic diagram of the DT-OLSR protocol of the present invention respectively comparing with the original OLSR protocol in three scenarios, where the traffic sizes of the test-changed CBR service data are 1500Kbps, 1040Kbps and 510Kbps, respectively, fig. 7a shows that the maximum effective communication time is 500ms, fig. 7b shows that the maximum effective communication time is 200ms, and fig. 7c shows that the maximum effective communication time is 100 ms. As can be seen from fig. 7, is shown in fig. 7. In the same scene, with the increase of CBR service data flow, both the OLSR protocol and the DT-OLSR protocol improve the average throughput of the nodes. However, no matter how much the CBR is increased, the performance of the DT-OLSR protocol is better than that of the OLSR protocol.
The simulation results of the table tennis ball movement model are shown in fig. 8. FIG. 8a is a graph comparing throughput using the DT-OLSR protocol and using the original OLSR protocol when the number of nodes is the same; fig. 8b is a graph comparing packet loss rate using DT-OLSR protocol and using original OLSR protocol when the number of nodes is the same. It can be seen from the figure that the overall network performance after the DT-OLSR protocol is used is still improved compared with that when the original OLSR protocol is used, and when the number of nodes is the same, the throughput of the DT-OLSR protocol is greater than that of the OLSR protocol and the packet loss rate is smaller.

Claims (7)

1. An unmanned aerial vehicle network OLSR routing self-adaptive strategy based on dynamic topology is characterized by comprising the following steps:
step 1) obtaining a simulation result of the next state of the node by using a Kalman filtering algorithm;
step 2) calculating the current distance N between nodes and the predicted distance M between nodes;
step 3) judging whether the current node-node distance N is greater than or equal to the maximum effective distance L and smaller than R, and meanwhile, the predicted node-node distance M is greater than R, wherein R is the communication range of the node, if so, executing the next step, otherwise, executing the seventh step;
step 4) obtaining
Figure FDA0002320360900000011
Wherein
Figure FDA0002320360900000012
In the formula, H is a broadcasting period of Hello messages of two nodes, T is the maximum effective communication time of the nodes, R is the communication range of the nodes, N is the current distance between the nodes, and v is the relative speed of a neighbor node;
step 5), the routing protocol sends third Hello information, if so, the next step is executed, and if not, the previous step is returned;
step 6) setting
Figure FDA0002320360900000013
And returning to the first step; wherein
Figure FDA0002320360900000014
In the formula, H is two nodesThe broadcast period of the Hello message, T is the maximum effective communication time of the node, R is the communication range of the node, N is the current distance between the nodes, and v is the relative speed of the neighbor nodes;
step 7) judging whether the node receives the Hello information of the new node for the first time, if so, executing the next step, and if not, returning to the first step;
step 8) reducing H of the remaining two pieces of Hello information to an optimal value, and not receiving any new Hello information any more;
step 9) judging the result of the step 8), if so, restoring H to a default value, and simultaneously returning to the first step; if not, returning to the step 7).
2. The dynamic topology based adaptive strategy for routing in OLSR for unmanned aerial vehicle network according to claim 1, wherein the kalman filter algorithm is:
Figure FDA0002320360900000021
Figure FDA0002320360900000022
Figure FDA0002320360900000023
where k represents a discrete time instant at which the node sends the Hello message,
Figure FDA0002320360900000024
is a state vector of the movement of the node,
Figure FDA0002320360900000025
is the node movement prediction state vector at the next discrete time of sending the Hello message, x and y are the location coordinates of the node, vxAnd vyIs the relative velocity of the node in different directions, phi is the state transition matrix, B is the noise matrix, and H is the observation matrix，
Figure FDA0002320360900000026
Is the acceleration of the unmanned aerial vehicle, is regarded as white noise processing in a short time,
Figure FDA0002320360900000027
are observation vectors that can be obtained from the GPS system,
Figure FDA0002320360900000028
is the observation noise, determined by the accuracy of the GPS device.
3. The dynamic topology-based routing adaptive policy for unmanned aerial vehicle networks OLSR according to claim 1, wherein the specific process of the step 2) is as follows:
the node receives the Hello message, obtains the coordinates (x, y) and the relative speed v of the neighbor node, and calculates the distance N between the node and the neighbor node at the moment as
Figure FDA0002320360900000029
Calculating the coordinates (x ', y ') and the relative speed v ' of the next moment node by the Kalman filtering algorithm, and calculating the distance M between the next moment node and the adjacent node as
Figure FDA00023203609000000210
Figure FDA00023203609000000211
D in the formulaijIs the distance, x, between two nodesiIs the abscissa, x, of node ijIs the abscissa, y, of node jiIs the ordinate, y, of node ijIs the ordinate of node j.
4. The dynamic topology-based routing adaptive policy for unmanned aerial vehicle networks OLSR according to claim 1, wherein the specific process of the step 3) is as follows:
when in use
Figure FDA0002320360900000031
And is
Figure FDA0002320360900000032
And if the second node B has a certain distance from the communication boundary of the first node A and topology disturbance in the range is ignored, keeping the Hello message broadcasting period H of the two nodes unchanged, wherein the distance is called as the maximum effective distance L and is preset through DT-OLSR.
5. The dynamic topology-based routing adaptive policy for unmanned aerial vehicle networks OLSR according to claim 1, wherein the specific process of the step 4) is as follows:
when the second node B moves to the maximum effective distance L, according to the fact that the effective time 3H of sending the Hello message is less than or equal to the maximum effective communication time T of the node, namely 0 < 3H is less than or equal to T, H back-stepping is estimated to be
Figure FDA0002320360900000033
Wherein
Figure FDA0002320360900000034
6. The dynamic topology-based routing adaptive policy for unmanned aerial vehicle networks OLSR according to claim 1, wherein the specific process of the step 6) is as follows:
when the second node B is in
Figure FDA0002320360900000035
After the first two Hello message packets are sent in the broadcast period, the DT-OLSR protocol is according to the current effective time
Figure FDA0002320360900000036
A final refinement of H, i.e.
Figure FDA0002320360900000037
H maintains this high frequency until the first node a updates the routing table.
7. The dynamic topology-based routing adaptive policy for unmanned aerial vehicle networks OLSR according to claim 1, wherein the specific processes of step 7) and step 8) are as follows:
if the third node C is to be added into the communication range of the first node a, the third node C usually needs to send three Hello messages to be added into the routing table; when the first node a receives the first strange Hello message once, the broadcast period of the two remaining Hello messages is reduced to the optimal value, and H maintains the frequency until the first node a completes the updating of the routing table.
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