CN108833049B - Deception anti-interference method and device based on cognition in unmanned aerial vehicle network - Google Patents

Deception anti-interference method and device based on cognition in unmanned aerial vehicle network Download PDF

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CN108833049B
CN108833049B CN201810599086.2A CN201810599086A CN108833049B CN 108833049 B CN108833049 B CN 108833049B CN 201810599086 A CN201810599086 A CN 201810599086A CN 108833049 B CN108833049 B CN 108833049B
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deception
channel
interference
unmanned aerial
aerial vehicle
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CN108833049A (en
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许文俊
韩晓
冯志勇
尚晋
袁昕
林家儒
张平
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Beijing University of Posts and Telecommunications
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Beijing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
    • H04K3/00—Jamming of communication; Counter-measures
    • H04K3/80—Jamming or countermeasure characterized by its function
    • H04K3/84—Jamming or countermeasure characterized by its function related to preventing electromagnetic interference in petrol station, hospital, plane or cinema
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04K—SECRET COMMUNICATION; JAMMING OF COMMUNICATION
    • H04K3/00—Jamming of communication; Counter-measures
    • H04K3/20—Countermeasures against jamming
    • H04K3/22—Countermeasures against jamming including jamming detection and monitoring
    • H04K3/224—Countermeasures against jamming including jamming detection and monitoring with countermeasures at transmission and/or reception of the jammed signal, e.g. stopping operation of transmitter or receiver, nulling or enhancing transmitted power in direction of or at frequency of jammer

Abstract

The embodiment of the application provides a deception jamming prevention method and device based on cognition in an unmanned aerial vehicle network, and belongs to the technical field of wireless communication. The method comprises the following steps: judging whether data transmission in progress of a first unmanned aerial vehicle exists in the unmanned aerial vehicle network or not and whether the data transmission is interfered by a first interference signal or not, if yes, obtaining network parameters of the unmanned aerial vehicle network, determining an optimal deception channel according to the network parameters and a prestored deception channel selection algorithm, determining optimal deception income power according to the network parameters and a prestored deception power distribution strategy, and if the optimal deception income power is larger than a preset income threshold value, sending a prestored deception signal through the optimal deception channel based on the optimal deception income power to a second unmanned aerial vehicle in the unmanned aerial vehicle network, wherein the second unmanned aerial vehicle receives collected data corresponding to the data transmission. The invention can improve the anti-interference effect of the unmanned aerial vehicle.

Description

Deception anti-interference method and device based on cognition in unmanned aerial vehicle network
Technical Field
The application relates to the technical field of wireless communication, in particular to a deception anti-interference method and device based on cognition in an unmanned aerial vehicle network.
Background
Unmanned aerial vehicle has extensive application prospect in civilian and military fields owing to have mobility height, with low costs, a great deal of advantages such as no casualties risk, has not had the threat of external electromagnetic interference easily, produces communication stability not good, the not high scheduling problem of communication efficiency that is beautiful not enough is the unmanned aerial vehicle network. Therefore, the unmanned aerial vehicle anti-interference technology is a research hotspot and is widely concerned by scholars at home and abroad.
The traditional unmanned aerial vehicle anti-interference method comprises a frequency hopping method and a transmission power distribution method. The specific process for resisting interference by using the transmission power distribution method comprises the following steps: a first drone in the drone network transmits acquisition data using a certain transmission channel. When the first unmanned machine detects that the interference signal of the corresponding interference channel exists in the current unmanned machine network through the prestored frequency spectrum sensing module, the first unmanned machine calculates the signal transmitting power and the transmission benefit required by continuously transmitting the acquired data by using the current transmission channel through the prestored analysis judgment module and the game algorithm. According to the transmission gain, determining to increase the signal transmitting power to the required signal transmitting power and continuously transmitting the acquired data; or temporarily stopping transmission until the interference signal to the first unmanned machine is weakened or the network environment in which the first unmanned machine is located becomes better.
However, when the maximum available signal transmitting power of the first drone is not enough to meet the required signal transmitting power requirement, or the interference signal received by the first drone is not weakened all the time, so that the network environment where the first drone is located is unchanged all the time, effective information transmission cannot be achieved by using the transmission power distribution method, and the anti-interference effect of the drone is poor.
Disclosure of Invention
An object of the embodiment of the application is to provide a deception jamming prevention method and device based on cognition in an unmanned aerial vehicle network, so that the jamming prevention effect of the unmanned aerial vehicle is improved. The specific technical scheme is as follows:
in a first aspect, a cognition-based deception jamming prevention method in a drone network is provided, where the method is applied to a deception node in the drone network, and the drone network further includes a plurality of other drones, and the method includes:
judging whether data transmission in progress of a first unmanned machine exists in the unmanned aerial vehicle network and whether the data transmission is interfered by a first interference signal;
if the data transmission exists and is interfered by the first interference signal, acquiring network parameters of the unmanned aerial vehicle network, wherein the network parameters at least comprise deception channel coefficients of all deception channels in the deception nodes and interference channel coefficients of all interference channels corresponding to all the deception channels;
determining an optimal deception channel according to the network parameters and a prestored deception channel selection algorithm;
determining the optimal deception income power of the optimal deception channel according to the network parameters and a prestored deception power distribution strategy;
and if the optimal deception income power is larger than a preset income threshold value, based on the optimal deception income power, sending a prestored deception signal to a second unmanned aerial vehicle in the unmanned aerial vehicle network through the optimal deception channel, wherein the second unmanned aerial vehicle is an unmanned aerial vehicle for receiving the collected data corresponding to the data transmission.
Optionally, the determining an optimal spoofed channel according to the network parameter and a spoofed channel selection algorithm stored in advance includes:
obtaining a deception channel coefficient of each deception channel and the interference channel coefficient of each interference channel corresponding to each deception channel;
aiming at each deception channel, determining a channel coefficient ratio of the deception channel according to a deception channel coefficient of the deception channel and the interference channel coefficient corresponding to the deception channel;
comparing the channel coefficient ratios of the deception channels to determine the maximum channel coefficient ratio;
if the spoofed channel with the maximum channel coefficient ratio is unique, taking the spoofed channel with the maximum channel coefficient ratio as a best spoofed channel;
and if the deception channel with the maximum channel coefficient ratio is not unique, determining an interference channel corresponding to each deception channel with the maximum channel coefficient ratio, determining a target interference channel with the maximum interference channel coefficient from the determined interference channels, and taking the deception channel corresponding to the target interference channel as the optimal deception channel.
Optionally, the determining the optimal spoofing revenue power of the optimal spoofing channel according to the network parameter and a spoofing power allocation policy stored in advance includes:
establishing a deception income function of the deception node and an interference income function of an interference source according to the network parameters, wherein the deception income function is used for expressing the income obtained by deception of the deception node, and the interference income function is used for expressing the income obtained by interference of the interference source on the deception node;
and calculating the optimal deception income power according to the deception income function, the interference income function and a prestored Stenberg game algorithm.
Optionally, the determining whether there is data transmission in progress by a first drone in the drone network, and whether the data transmission is interfered by a first interference signal includes:
detecting first spectrum detection information corresponding to the cheating node, wherein the first spectrum detection information comprises spectrum information and power distribution information of a network environment in a preset range of the cheating node;
sending the first spectrum detection information to other unmanned aerial vehicles in the unmanned aerial vehicle network;
receiving second spectrum detection information sent by the other unmanned aerial vehicles, wherein the second spectrum detection information comprises spectrum information and power distribution information of a network environment in a predetermined range of the other unmanned aerial vehicles;
and judging whether the first unmanned aerial vehicle is carrying out data transmission and whether the data transmission is interfered by the first interference signal in the unmanned aerial vehicle network according to the first frequency spectrum detection information, the second frequency spectrum detection information and a pre-stored frequency spectrum detection information processing algorithm.
Optionally, the determining whether there is data transmission in progress by the first drone in the drone network, and whether the data transmission is interfered by the first interfering signal further includes:
receiving an identity setting instruction sent by a console in the unmanned aerial vehicle network, wherein the identity setting instruction is used for setting a cheating node in the unmanned aerial vehicle network;
and setting the identity information of the node as a deception node according to the identity setting instruction.
In a second aspect, a deception jamming prevention device based on cognition in a drone network is provided, the device is applied to a deception node in the drone network, the drone network further includes a plurality of other drones, and the device includes:
the judging module is used for judging whether data transmission in progress of a first unmanned machine exists in the unmanned aerial vehicle network and whether the data transmission is interfered by a first interference signal;
an obtaining module, configured to obtain a network parameter of the drone network when the data transmission exists and is interfered by the first interference signal, where the network parameter at least includes a spoofed channel coefficient of each spoofed channel in the spoofed node and an interference channel coefficient of each interference channel corresponding to each spoofed channel;
the first determining module is used for determining the optimal deception channel according to the network parameters and a prestored deception channel selection algorithm;
a second determining module, configured to determine an optimal spoofing revenue power of the optimal spoofing channel according to the network parameter and a spoofing power allocation policy stored in advance;
and the sending module is used for sending a prestored deception signal to a second unmanned aerial vehicle in the unmanned aerial vehicle network through the optimal deception channel based on the optimal deception income power when the optimal deception income power is greater than a preset income threshold value, wherein the second unmanned aerial vehicle is an unmanned aerial vehicle for receiving the collected data corresponding to the data transmission.
Optionally, the first determining module includes:
an obtaining submodule, configured to obtain a deception channel coefficient of each deception channel and the interference channel coefficient of each interference channel corresponding to each deception channel;
the determining submodule is used for determining the channel coefficient ratio of each deception channel according to the deception channel coefficient of the deception channel and the interference channel coefficient corresponding to the deception channel;
the comparison submodule is used for comparing the channel coefficient ratios of the deceptive channels and determining the maximum channel coefficient ratio;
a selecting sub-module for taking the spoofed channel having the maximum channel coefficient ratio as a best spoofed channel when the spoofed channel having the maximum channel coefficient ratio is unique;
the selecting sub-module is further configured to determine, when the spoofed channels with the largest channel coefficient ratio are not unique, interference channels corresponding to the spoofed channels with the largest channel coefficient ratio, determine, among the determined interference channels, a target interference channel with a largest interference channel coefficient, and use the spoofed channel corresponding to the target interference channel as an optimal spoofed channel.
Optionally, the second determining module includes:
the establishing submodule is used for establishing a deception income function of the deception node and an interference income function of an interference source according to the network parameters, wherein the deception income function is used for expressing the income obtained by deception of the deception node, and the interference income function is used for expressing the income obtained by interference of the interference source on the deception node;
and the calculation submodule is used for calculating the optimal cheating income power according to the cheating income function, the interference income function and a prestored Stenberg game algorithm.
Optionally, the determining module includes:
the detection submodule is used for detecting first spectrum detection information corresponding to the deception node, wherein the first spectrum detection information comprises spectrum information and power distribution information of a network environment in a predetermined range of the deception node;
a sending submodule, configured to send the first spectrum detection information to other drones in the drone network;
the receiving submodule is used for receiving second spectrum detection information sent by other unmanned aerial vehicles, wherein the second spectrum detection information comprises spectrum information and power distribution information of a network environment in a preset range of the other unmanned aerial vehicles;
and the judging submodule is used for judging whether the first unmanned aerial vehicle is carrying out data transmission and whether the data transmission is interfered by the first interference signal in the unmanned aerial vehicle network according to the first frequency spectrum detection information, the second frequency spectrum detection information and a prestored frequency spectrum detection information processing algorithm.
Optionally, the apparatus further comprises:
the system comprises a receiving module, a configuration module and a configuration module, wherein the receiving module is used for receiving an identity setting instruction sent by a console in the unmanned aerial vehicle network, and the identity setting instruction is used for setting a cheating node in the unmanned aerial vehicle network;
and the setting module is used for setting the identity information of the user as a deception node according to the identity setting instruction.
In a third aspect, there is provided a communications device comprising a processor and a machine-readable storage medium storing machine-executable instructions executable by the processor, the processor being caused by the machine-executable instructions to: the method steps of any deception anti-interference method based on cognition in the unmanned aerial vehicle network are realized.
In a fourth aspect, there is provided a machine-readable storage medium storing machine-executable instructions that, when invoked and executed by a processor, cause the processor to: the method steps of any deception anti-interference method based on cognition in the unmanned aerial vehicle network are realized.
The embodiment of the invention provides a deception jamming prevention method and a deception jamming prevention device based on cognition in an unmanned aerial vehicle network. And when the optimal deception income power is larger than a preset income threshold value, the deception node sends deception signals to a second unmanned aerial vehicle which receives the collected data corresponding to the data transmission based on the optimal deception channel according to the optimal deception income power. Because the total transmission power of the interference source is fixed and unchanged, the deception node reduces the transmission power of the first interference signal and improves the first unmanned network environment by inducing the interference source to send a second interference signal which interferes the deception signal. Based on this scheme, can improve unmanned aerial vehicle's anti-interference effect.
Of course, it is not necessary for any product or method of the present application to achieve all of the above-described advantages at the same time.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a deception jamming prevention method based on cognition in an unmanned aerial vehicle network according to an embodiment of the present invention;
fig. 2 is a flowchart of a deception jamming prevention method based on cognition in an unmanned aerial vehicle network according to an embodiment of the present invention;
fig. 3 is a flowchart of a deception jamming prevention method based on cognition in an unmanned aerial vehicle network according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a deception jamming-resistant device based on cognition in an unmanned aerial vehicle network according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a deception-based anti-interference device in an unmanned aerial vehicle network according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a deception-based anti-interference device in an unmanned aerial vehicle network according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a deception jamming resistant apparatus based on cognition in an unmanned aerial vehicle network according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a deception jamming resistant apparatus based on cognition in an unmanned aerial vehicle network according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a communication device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the invention provides a deception anti-interference method based on cognition in an unmanned aerial vehicle network. The unmanned aerial vehicle can acquire the spectrum state of the current network environment of the unmanned aerial vehicle network, and judges whether an interference signal exists in the current unmanned aerial vehicle network, such as an unmanned aerial vehicle for battlefield terrain detection, an unmanned aerial vehicle for communication relay, and the like.
In the embodiment of the invention, any unmanned aerial vehicle in the unmanned aerial vehicle network can be used as a cheating node, and a detection sensing system, a frequency spectrum information exchange system, an analysis judgment system, a cheating selection system, a sending system and a receiving system are prestored in the cheating node.
As shown in fig. 1, the specific process of the method is as follows:
step 101, judging whether data transmission in progress of a first unmanned aerial vehicle exists in the unmanned aerial vehicle network, and whether the data transmission is interfered by a first interference signal.
In implementation, the drone network comprises a drone group consisting of a plurality of drones, including, at each mission, one drone selected as a rogue node, one drone selected as a console, and other drones for performing the mission. Tasks executed by the unmanned aerial vehicle include terrain investigation, military force deployment investigation and the like. In the process of executing the task, after the first unmanned aerial vehicle receives the transmission information instruction of the console, the stored acquisition data is sent to the second unmanned aerial vehicle in the unmanned aerial vehicle group, and data transmission is carried out. It should be noted that the first drone is a drone performing a task in the drone group, and the second drone is a drone or a console performing another task in the drone group. The collected data comprises a photo or a video shot by the first unmanned machine and the like.
An interference source may exist in the unmanned aerial vehicle network, and the interference source detects whether data transmission in progress by the unmanned aerial vehicle exists in the current network environment within a preset range in real time. The purpose that the interference source disturbed is, under the prerequisite of considering both the power restriction of self and sustainable effective interference condition, through destroying the network environment of unmanned aerial vehicle network, reduces unmanned aerial vehicle and carries out data transmission's transmission quality, for example the accuracy that second unmanned aerial vehicle received the data collection. When the interference source detects data transmission in progress of the first unmanned aerial vehicle, the information of the collected data is analyzed, and a first interference signal corresponding to the collected data is generated and sent to perform interference.
The cheating node can start to execute a task when receiving a working instruction of the console, namely regularly judging whether data transmission in progress of a first unmanned aerial vehicle exists in the unmanned aerial vehicle network and whether the data transmission is interfered by a first interference signal according to the detection sensing system, the frequency spectrum information exchange system and the analysis judgment system. It should be noted that, the spoofing node may preset a detection period through its own timer, and periodically issue a detection sensing instruction to implement timing detection.
In the embodiment of the invention, the purpose of deceiving the interference source by the deceptive node is to improve the transmission quality of the first unmanned machine transmission acquisition data on the premise of considering the power limit and the endurance condition of the deceptive node.
Step 102, if the data transmission exists and is interfered by the first interference signal, acquiring network parameters of the unmanned aerial vehicle network, wherein the network parameters at least comprise deception channel coefficients of each deception channel in a deception node and interference channel coefficients of each interference channel corresponding to each deception channel.
In implementation, after the deception node determines that data transmission in progress by a first unmanned machine exists in the unmanned aerial vehicle network and a first interference signal sent by an interference source aiming at the data transmission, the network parameters of the unmanned aerial vehicle network are calculated through an analysis and judgment system according to a pre-stored channel estimation algorithm, first spectrum detection information and second spectrum detection information, and the network parameters are stored. And the cheating node acquires the network parameters through a cheating selection system. The cheating node is stored with an interference information experience statistical table in advance, and the interference information experience statistical table comprises a corresponding interference source and relevant information of corresponding interference signal network parameters when the unmanned aerial vehicle is interfered for the next time. And the cheating node obtains an estimated value of the network parameter of the first interference signal according to a channel estimation algorithm through an analysis judgment system, and then corrects the estimated value according to an interference information experience statistical table to obtain the more accurate network parameter of the first interference signal.
It should be noted that the network parameter includes the spoofed channel coefficient h of n available spoofed channels in the spoofed nodep，hpN value sets of { h }1,h2,h3...hnH, and interference channel coefficients h for n interference channels corresponding to n spoofed channelsjp，hjpN value sets of { h }j1,hj2,hj3...hjnH, transmission channel coefficient h corresponding to the first unmanned transmission channeltInterference channel coefficient h of a first interference channel corresponding to a transmission channeljtAmbient noise power N, emission power P of the first drone transmission acquisition datatTotal transmission power P of interference sourceJActual transmission power P of the first interfering signaljtSpoofing the maximum power P of the node, spoofingTransmitting power P of cheating nodepSpoofed node power loss factor C, power loss factor C of interference sourceJAnd the like. It should be noted that n is a natural number, and represents the number of spoofed channels available to the spoofed node.
And 103, determining the optimal deception channel according to the network parameters and a prestored deception channel selection algorithm.
In implementation, the spoofing node inputs the acquired network parameters as input quantity into a spoofing channel selection algorithm stored in advance through a spoofing selection system. And the deception node calculates the channel coefficient ratio of each deception channel available for the deception node through a deception channel selection algorithm, compares the channel coefficient ratios and determines the maximum channel coefficient ratio. And if the maximum channel coefficient ratio is only one than the corresponding deception channel, the deception node takes the deception channel corresponding to the maximum channel coefficient ratio as the optimal deception channel through a deception channel selection algorithm. And if the maximum channel coefficient ratio is more than the corresponding deception channels, the deception node compares the channel coefficients of the interference channels corresponding to the deception channels with the maximum channel coefficient ratio through a deception channel selection algorithm, takes the interference channel with the maximum interference channel coefficient as a target interference channel, and selects the deception channel corresponding to the target interference channel as the optimal deception channel.
Wherein the network parameters comprise the deception channel coefficients { h) of n deception channels available for the deception node1,h2,h3...hnH, and interference channel coefficients { h) for n interference channels corresponding to the n spoofed channelsj1,hj2,hj3...hjn}. Channel coefficient ratio is determined by
Figure BDA0001692744110000091
And (4) showing.
In the embodiment of the invention, the cheating node cheats the interference source through the best cheating channel to obtain the highest cheating benefit in the n available cheating channels of the cheating node. The cheating benefit means that the cheating node reduces the transmitting power of the first interference signal and improves the first unmanned network environment by inducing the interference source to send out a second interference signal which interferes the cheating signal based on the characteristic that the total transmitting power of the interference source is limited.
And step 104, determining the optimal deception income power of the optimal deception channel according to the network parameters and a prestored deception power distribution strategy.
In implementation, the spoofing node obtains the network parameters calculated by the analysis and decision system through the spoofing selection system, and inputs the network parameters into a spoofing power distribution strategy stored in advance as input quantity, and takes the output of the spoofing power distribution strategy as the optimal spoofing profit power. The optimal deception income power refers to the transmitting power of deception signals when the deception nodes obtain the optimal deception income under the condition that the deception nodes take power limit and endurance conditions into consideration. The best spoofing benefit means that the spoofing node obtains the maximum value of the transmission power reduction corresponding to the first interference signal by inducing the interference source to send a second interference signal which interferes with the spoofing signal.
And 105, if the optimal deception income power is larger than a preset income threshold value, sending a prestored deception signal to a second unmanned aerial vehicle in the unmanned aerial vehicle network through an optimal deception channel based on the optimal deception income power, wherein the second unmanned aerial vehicle is an unmanned aerial vehicle for receiving the collected data corresponding to the data transmission.
In implementation, the cheating node compares the calculated optimal cheating income power with a preset income threshold value through a cheating selection system, and when the optimal cheating income power is larger than the income threshold value, the cheating node transmits a prestored cheating signal to a second unmanned aerial vehicle in the unmanned aerial vehicle network through a sending system and an optimal cheating channel according to the optimal cheating income power. It should be noted that the profit threshold is set by the spoofing node, and the spoofing node can be changed according to the available transmitting power and the endurance condition of the spoofing node; the spoof signal includes encoded information that is not meaningful; the second unmanned aerial vehicle in the unmanned aerial vehicle network is the unmanned aerial vehicle which receives the collected data corresponding to the data transmission of the first unmanned aerial vehicle.
Optionally, as shown in fig. 2, the specific process of step 101 is:
step 201, detecting first spectrum detection information corresponding to a spoofed node, wherein the first spectrum detection information includes spectrum information and power distribution information of a network environment within a predetermined range of the spoofed node.
In implementation, the cheating node detects the network environment within the preset range of the cheating node regularly through the detection sensing system according to a preset detection time interval to obtain the spectrum state of the network environment within the preset range, and then extracts the time domain and frequency domain characteristics of the spectrum state of the network environment within the preset range according to the detection sensing system to serve as first spectrum detection information. It should be noted that the first spectrum sensing information includes spectrum information and spectrum power distribution information corresponding to a network environment within a predetermined range of the spoofed node. The predetermined range may be determined by a maximum detectable sensing radius of the detection sensing system or by a detection sensing radius preset by the spoof node.
Step 202, sending the first spectrum sensing information to other drones in the drone network.
In implementation, the cheating node sends the first spectrum detection information to the spectrum information exchange system through the detection sensing system, and the cheating node sends the first spectrum detection information to other unmanned aerial vehicles in the unmanned aerial vehicle network through the spectrum information exchange system and the sending system.
Step 203, receiving second spectrum detection information sent by other unmanned aerial vehicles, wherein the second spectrum detection information includes spectrum information and power distribution information of the network environment in a predetermined range of other unmanned aerial vehicles.
In implementation, the spoofing node receives second spectrum detection information sent by other unmanned aerial vehicles through the receiving system, and sends the received second spectrum detection information to the spectrum information exchange system. It should be noted that the second spectrum sensing information is extracted by the other unmanned aerial vehicles through their own sensing systems, and includes spectrum information and power distribution information corresponding to network environments within a predetermined range of the other unmanned aerial vehicles. The predetermined range of the spoofed node may be the same as or different from the predetermined range of the other drones.
And 204, judging whether the first unmanned machine is performing data transmission and whether the data transmission is interfered by the first interference signal in the unmanned machine network according to the first spectrum detection information, the second spectrum detection information and a prestored spectrum detection information processing algorithm.
In implementation, the cheating node sends the extracted first spectrum detection information to the analysis and judgment system through the detection sensing system, and sends the received second spectrum detection information to the analysis and judgment system through the spectrum information exchange system.
And the cheating node processes the first spectrum detection information and the second spectrum detection information according to a spectrum detection information processing algorithm pre-stored in the analysis and judgment system to obtain spectrum power distribution information of the whole unmanned aerial vehicle network. And screening target signals exceeding a preset transmission power threshold value in the spectrum power distribution information by the cheating node according to a spectrum detection information processing algorithm prestored in the analysis and judgment system. And if the target signal exceeding the preset transmission power threshold exists, the cheating node judges that the first unmanned machine is transmitting data in the unmanned machine network. And if the target signal exceeding the preset transmission power threshold value does not exist, the cheating node judges that no unmanned aerial vehicle transmits data in the unmanned aerial vehicle network. When the first unmanned machine is performing data transmission, the deception node analyzes whether the power of the target signal is covered by strong power noise in the spectrum power distribution information according to a spectrum detection information processing algorithm, and judges whether the data transmission is interfered by a first interference signal sent by an interference source. The spectrum sensing information processing algorithm is a common algorithm in the art and is not specifically described here.
Optionally, as shown in fig. 3, the specific processing procedure of step 103 includes:
step 301, obtaining the deception channel coefficient of each deception channel and the interference channel coefficient of each interference channel corresponding to each deception channel.
In implementation, the spoofing node acquires the spoofing channel coefficient of each spoofing channel and the interference channel coefficient of each interference channel corresponding to the spoofing channel through the spoofing selection system. It should be noted that the channel available to the spoofing node is a spoofing channel, the channel used by the interference source corresponding to the spoofing channel is an interference channel, the spoofing channel and the corresponding spoofing channel parameter are determined by the spoofing node when the network parameter is calculated by the analysis and decision system.
Step 302, aiming at each deception channel, determining a channel coefficient ratio of the deception channel according to a deception channel coefficient of the deception channel and an interference channel coefficient corresponding to the deception channel.
In implementation, the deception node has n usable deception channels, and the deception node uses a deception selection system for each deception channel to obtain the deception channel coefficient h of the deception channelpInterference channel coefficient h corresponding to the deceptive channeljpCalculating to determine the channel coefficient ratio of the deceptive channel
Figure BDA0001692744110000121
The spoofed node stores a channel coefficient ratio corresponding to each spoofed channel.
Step 303, comparing the channel coefficient ratios of the deceptive channels to determine the maximum channel coefficient ratio.
In implementation, the spoofing node compares the channel coefficient ratios of the spoofing channels through the spoofing selection system, determines the maximum value, and takes the maximum value as the maximum channel coefficient ratio.
And step 304, if the deception channel with the largest channel coefficient ratio is unique, taking the deception channel with the largest channel coefficient ratio as the best deception channel.
In implementation, if there is only one spoofed channel with the largest channel coefficient ratio among the spoofed channels, the spoofed node regards the spoofed channel as the best spoofed channel.
And 305, if the deception channel with the maximum channel coefficient ratio is not unique, determining interference channels corresponding to the deception channels with the maximum channel coefficient ratio, determining a target interference channel with the maximum interference channel coefficient from the determined interference channels, and taking the deception channel corresponding to the target interference channel as the optimal deception channel.
In the implementation, if a plurality of deception channels with the largest channel coefficient ratio exist in each deception channel, the deception node firstly determines the interference channel corresponding to each deception channel with the largest channel coefficient ratio, then compares the interference channel coefficients of the interference channels in the determined interference channels, takes the interference channel with the largest interference channel coefficient as a target interference channel, and finally takes the deception channel corresponding to the target interference channel as the optimal deception channel.
Optionally, the specific processing procedure of step 104 includes:
step one, establishing a deception income function of a deception node and an interference income function of an interference source according to network parameters, wherein the deception income function is used for representing earnings obtained by deception of the deception node, and the interference income function is used for representing earnings obtained by interference of the interference source on the deception node.
In implementation, the deception node obtains the network parameters calculated by the analysis and judgment system through a deception selection system, and establishes a deception gain function of the deception node and an interference gain function of an interference source based on the network parameters. The cheating revenue function is used for representing the revenue obtained by cheating the cheating node, and the interference revenue function is used for representing the revenue obtained by the interference source for the cheating node.
The embodiment of the invention provides a calculation formula of a deception income function of a deception node, which comprises the following steps:
Figure BDA0001692744110000131
wherein, UpRepresenting the deception income of the deception node, wherein theta is a transmission state parameter of the first unmanned machine, and the value of theta has two conditions of 0 and 1 and respectively corresponds to two conditions of non-transmission and ongoing data transmission of the first unmanned machine;
n represents the ambient noise power, C represents the power loss factor of the rogue node, htRepresenting the channel coefficient, P, of the first dronetRepresenting the actual transmission power, P, of the first unmanned transmission acquisition datapSignalling fraud on behalf of rogue nodesActual transmission power, PjtRepresents the actual transmit power of the first interfering signal, and hjtA first interference channel coefficient representing an interference source to the first unmanned machine's transmission channel.
The embodiment of the invention also provides a calculation formula of the interference gain function of the interference source:
Figure BDA0001692744110000132
wherein, UJInterference yield, h, representing the interference sourcepSpoofing channel coefficient, h, representing the selected available spoofing channel by the spoofing nodep＝hi,1≤i≤n，hjpSelecting available spoofed channel h for spoofed nodes on behalf of an interfererpAnd a second interference channel coefficient, h, of the selected available second interference channeljp＝hji,1≤i≤n，PjpRepresenting the actual transmission power, C, of the interferer to the second interfering signal sent by the rogue nodeJRepresenting the power loss factor of the interferer.
And step two, calculating the optimal deception income power according to the deception income function, the interference income function and a prestored Stenberg game algorithm.
In implementation, the cheating node inputs a cheating profit function, an interference profit function and related network parameters as input quantities into a prestored Stenberg game algorithm for calculation through a cheating selection system. The cheating node predicts the optimal actual transmitting power of the interference source according to the transmitting power of the cheating node, the transmitting power of the first unmanned aerial vehicle transmission acquisition data and the interference gain function, and then calculates the maximum cheating gain of the cheating node and the optimal cheating gain power corresponding to the maximum cheating gain according to the optimal actual transmitting power of the interference source and the cheating gain function. The optimal actual transmitting power of the interference source is calculated by the deception node according to the interference gain function and the network parameters. The interference benefit means that the interference source destroys the network environment by sending the first interference signal and the second interference signal, and reduces the transmission quality of data transmission performed by the first unmanned aerial vehicle in the unmanned aerial vehicle network.
In the embodiment of the invention, the cheating node selects a Stenberg game algorithm, and the process of calculating the optimal cheating income power comprises the following steps:
and in the step of the Spoofer game algorithm, the cheating node is used as a leader of the game, and the interference source is used as a follower of the game.
In the game, the cheating node assumes that the workflow of the interference source is as follows: after detecting and sensing the transmitting power of the first unmanned transmission acquisition data and the transmitting power of the deception node, calculating the maximum interference benefit through an interference benefit function, and determining the actual transmitting power of the first interference signal and the second interference signal corresponding to the maximum interference benefit as the optimal actual transmitting power.
And the cheating node calculates the maximum cheating income through a cheating income function according to the transmitting power of the first unmanned aerial vehicle transmission acquisition data and the predicted optimal actual transmitting power of the interference source, and determines the transmitting power of the cheating signal corresponding to the maximum cheating income as the optimal cheating income power.
In the game process, the interference source always changes the actual transmitting power of the interference source along with the transmitting power of the deception node, and as a result of the game, the deception node determines the optimal deception income power of the deception node through calculation according to the estimated optimal actual transmitting power of the interference source.
The embodiment of the invention provides a mathematical model of a Steinberg game algorithm, which comprises the following steps:
Figure BDA0001692744110000151
wherein the content of the first and second substances,
Figure BDA0001692744110000152
represents the best spoofed revenue power for the spoofed node, P represents the maximum power for the spoofed node,
Figure BDA0001692744110000153
representing an expected value of the transmit power of the first jamming signal by the rogue node,
Figure BDA0001692744110000154
an expected value representing the transmit power of the second interfering signal,
Figure BDA0001692744110000155
representing the transmit power of the spoofed signal detected by the interferer,
Figure BDA0001692744110000156
Figure BDA0001692744110000157
respectively representing the transmission power of the corresponding first interference signal and the second interference signal when the interference source obtains the overall best interference gain.
The calculation result of the cheating node through the SteinBog game algorithm is that the optimal cheating income power and the optimal cheating income power of the cheating node are obtained according to the predicted optimal actual transmitting power of the interference source
Figure BDA0001692744110000158
The specific expression of (A) is as follows:
Figure BDA0001692744110000159
wherein, Λ1、Λ2、Λ3、Λ4Four conditions for obtaining the best deception income power respectively.
Four conditions are specifically described below:
1. when the cheating node judges that the network parameter meets the lambda1When the requirements are met, the system can be used,
Figure BDA00016927441100001510
is 0;
2. when the cheating node judges that the network parameter meets the lambda2When the requirements are met, the system can be used,
Figure BDA00016927441100001511
is composed of
Figure BDA00016927441100001512
3. When the cheating node judges that the network parameter meets the lambda3When the requirements are met, the system can be used,
Figure BDA00016927441100001513
is PM；
4. When the cheating node judges that the network parameter meets the lambda4When the requirements are met, the system can be used,
Figure BDA00016927441100001514
is Pupb。
Wherein, Λ1、Λ2、Λ3、Λ4Specifically, the following are shown:
Λ1:Π1orΠ2,others
Figure BDA0001692744110000161
Figure BDA0001692744110000162
Figure BDA0001692744110000163
Figure BDA0001692744110000164
Figure BDA0001692744110000165
therein, II1Representing a condition in which the rogue node cannot effectively attract interfering power, Π2And representing the condition that the cheating node can effectively attract the interference power, wherein the effective attraction of the interference power refers to the condition that the interference source interferes with the cheating node to cause the reduction of the transmission power of the first interference signal. other stands for Π1Or Π2Middle removing lambda2、Λ3、Λ4Conditions other than those listed. II type1And pi2The specific expression mode is as follows:
Figure BDA0001692744110000166
wherein, αres、αlow、αupThe method is an intermediate quantity calculated by a Stenberg game algorithm and has no specific meaning; result1, result2 and result3 represent the fraud nodes at Λ, respectively2、Λ3、Λ4Maximum fraud gain, P, obtainable by performing fraud under conditionsupb、PlowbRespectively corresponding to the maximum value and the minimum value, P, of the deceptive power which can effectively attract the interference powerMMaximum power of spoofed node αres、αlow、αupResults 1, results 2, results 3 and Pupb、PlowbThe expression of (A) is as follows:
Figure BDA0001692744110000171
Figure BDA0001692744110000172
Figure BDA0001692744110000173
Figure BDA0001692744110000174
Figure BDA0001692744110000175
Figure BDA0001692744110000176
optionally, before implementing the method, the spoofing node performs the following operations:
step one, receiving an identity setting instruction sent by a console in an unmanned aerial vehicle network, wherein the identity setting instruction is used for setting a deception node in the unmanned aerial vehicle network.
In implementation, a certain drone in the drone network may receive an identity setting instruction sent by a console in the drone network before starting to work, or during work. It should be noted that the identity setting instruction includes an identifier of identity information of the spoofed node, and is used to set the spoofed node in the drone network.
And step two, setting the identity information of the node as a deception node according to the identity setting instruction.
In implementation, a certain unmanned aerial vehicle sets up the instruction according to the identity received, through reading the identity information identification of the deception node in the instruction of setting up the identity, in the identity information table of prestoring, change the identity information of self into the mode of deception node identity information, set up this unmanned aerial vehicle as the deception node.
The embodiment of the invention provides a deception jamming prevention method and a deception jamming prevention device based on cognition in an unmanned aerial vehicle network. And when the optimal deception income power is larger than a preset income threshold value, the deception node sends deception signals to a second unmanned aerial vehicle which receives the collected data corresponding to the data transmission based on the optimal deception channel according to the optimal deception income power. Because the total transmission power of the interference source is fixed and unchanged, the deception node reduces the transmission power of the first interference signal and improves the first unmanned network environment by inducing the interference source to send a second interference signal which interferes the deception signal. Based on this scheme, can improve unmanned aerial vehicle's anti-interference effect.
Based on the same technical concept, as shown in fig. 4, an embodiment of the present invention further provides a deception jamming prevention apparatus based on cognition in an unmanned aerial vehicle network, where the apparatus is applied to a deception node in the unmanned aerial vehicle network, the unmanned aerial vehicle network further includes a plurality of other unmanned aerial vehicles, and the apparatus includes:
a determining module 410, configured to determine whether there is data transmission in progress by a first drone in the drone network, and whether the data transmission is interfered by a first interference signal;
an obtaining module 420, configured to obtain network parameters of the drone network when the data transmission exists and is interfered by the first interference signal, where the network parameters at least include a spoofed channel coefficient of each spoofed channel in the spoofed node and an interference channel coefficient of each interference channel corresponding to each spoofed channel;
a first determining module 430, configured to determine an optimal spoofed channel according to the network parameter and a pre-stored spoofed channel selection algorithm;
a second determining module 440, configured to determine an optimal spoofed revenue power of the optimal spoofed channel according to the network parameter and a pre-stored spoofed power allocation policy;
a sending module 450, configured to send, based on the optimal spoofing revenue power, a pre-stored spoofing signal to a second unmanned aerial vehicle in the unmanned aerial vehicle network through the optimal spoofing channel when the optimal spoofing revenue power is greater than a preset revenue threshold, where the second unmanned aerial vehicle is an unmanned aerial vehicle that receives the collected data corresponding to the data transmission.
Optionally, as shown in fig. 5, the first determining module 430 includes:
an obtaining submodule 431, configured to obtain a spoofed channel coefficient of each spoofed channel and the interference channel coefficient of each interference channel corresponding to each spoofed channel;
a determining submodule 432, configured to determine, for each deceptive channel, a channel coefficient ratio of the deceptive channel according to a deceptive channel coefficient of the deceptive channel and the interference channel coefficient corresponding to the deceptive channel;
a comparison submodule 433, configured to compare channel coefficient ratios of the spoofed channels, and determine a maximum channel coefficient ratio;
a selecting sub-module 434, configured to, when the spoofed channel with the largest channel coefficient ratio is unique, take the spoofed channel with the largest channel coefficient ratio as a best spoofed channel;
the selecting sub-module 434 is further configured to, when the spoofed channels with the largest channel coefficient ratio are not unique, determine an interference channel corresponding to each of the spoofed channels with the largest channel coefficient ratio, determine a target interference channel with a largest interference channel coefficient among the determined interference channels, and take the spoofed channel corresponding to the target interference channel as an optimal spoofed channel.
Optionally, as shown in fig. 6, the second determining module 440 includes:
a creating submodule 441, configured to create, according to the network parameter, a spoofing revenue function of the spoofing node and an interference revenue function of an interference source, where the spoofing revenue function is used to indicate revenue that is available for spoofing by the spoofing node, and the interference revenue function is used to indicate revenue that is available for the interference source to interfere with the spoofing node;
the calculating sub-module 442 is configured to calculate an optimal fraud gain power according to the fraud gain function, the interference gain function, and a prestored steinbor game algorithm.
Optionally, as shown in fig. 7, the determining module 410 includes:
the detecting submodule 411 is configured to detect first spectrum detection information corresponding to the spoofed node, where the first spectrum detection information includes spectrum information and power distribution information of a network environment in a predetermined range of the spoofed node;
a sending submodule 412, configured to send the first spectrum sensing information to other drones in the drone network;
the receiving submodule 413 is configured to receive second spectrum sensing information sent by the other drones, where the second spectrum sensing information includes spectrum information and power distribution information of a network environment in a predetermined range of the other drones;
a determining submodule 414, configured to determine, according to the first spectrum detection information and the second spectrum detection information, and a prestored spectrum detection information processing algorithm, whether the first drone and the second drone perform data transmission in the drone network, and whether the data transmission is interfered by the first interference signal.
Optionally, as shown in fig. 8, the apparatus further includes:
a receiving module 460, configured to receive an identity setting instruction sent by a console in the drone network, where the identity setting instruction is used to set a spoofing node in the drone network;
a setting module 470, configured to set the identity information of the node as a spoofed node according to the identity setting instruction.
The embodiment of the invention provides a deception jamming prevention method and a deception jamming prevention device based on cognition in an unmanned aerial vehicle network. And when the optimal deception income power is larger than a preset income threshold value, the deception node sends deception signals to a second unmanned aerial vehicle which receives the collected data corresponding to the data transmission based on the optimal deception channel according to the optimal deception income power. Because the total transmission power of the interference source is fixed and unchanged, the deception node reduces the transmission power of the first interference signal and improves the first unmanned network environment by inducing the interference source to send a second interference signal which interferes the deception signal. Based on this scheme, can improve unmanned aerial vehicle's anti-interference effect.
The embodiment of the present invention further provides a communication device, as shown in fig. 9, which includes a processor 901, a communication interface 902, a memory 903, and a communication bus 904, where the processor 901, the communication interface 902, and the memory 903 complete mutual communication through the communication bus 904,
a memory 903 for storing computer programs;
a processor 901, configured to execute the program stored in the memory 903, so that the node apparatus executes the following steps, where the steps include:
judging whether data transmission in progress of a first unmanned machine exists in the unmanned aerial vehicle network and whether the data transmission is interfered by a first interference signal;
if the data transmission exists and is interfered by the first interference signal, acquiring network parameters of the unmanned aerial vehicle network, wherein the network parameters at least comprise deception channel coefficients of all deception channels in the deception nodes and interference channel coefficients of all interference channels corresponding to all the deception channels;
determining an optimal deception channel according to the network parameters and a prestored deception channel selection algorithm;
determining the optimal deception income power of the optimal deception channel according to the network parameters and a prestored deception power distribution strategy;
and if the optimal deception income power is larger than a preset income threshold value, based on the optimal deception income power, sending a prestored deception signal to a second unmanned aerial vehicle in the unmanned aerial vehicle network through the optimal deception channel, wherein the second unmanned aerial vehicle is an unmanned aerial vehicle for receiving the collected data corresponding to the data transmission.
Optionally, the determining an optimal spoofed channel according to the network parameter and a pre-stored spoofed channel selection algorithm includes:
obtaining a deception channel coefficient of each deception channel and the interference channel coefficient of each interference channel corresponding to each deception channel;
aiming at each deception channel, determining a channel coefficient ratio of the deception channel according to a deception channel coefficient of the deception channel and the interference channel coefficient corresponding to the deception channel;
comparing the channel coefficient ratios of the deception channels to determine the maximum channel coefficient ratio;
if the spoofed channel with the maximum channel coefficient ratio is unique, taking the spoofed channel with the maximum channel coefficient ratio as a best spoofed channel;
and if the deception channel with the maximum channel coefficient ratio is not unique, determining an interference channel corresponding to each deception channel with the maximum channel coefficient ratio, determining a target interference channel with the maximum interference channel coefficient from the determined interference channels, and taking the deception channel corresponding to the target interference channel as the optimal deception channel.
Optionally, the determining the optimal spoofing revenue power of the optimal spoofing channel according to the network parameter and a spoofing power allocation policy stored in advance includes:
establishing a deception income function of the deception node and an interference income function of an interference source according to the network parameters, wherein the deception income function is used for expressing the income obtained by deception of the deception node, and the interference income function is used for expressing the income obtained by interference of the interference source on the deception node;
and calculating the optimal deception income power according to the deception income function, the interference income function and a prestored Stenberg game algorithm.
Optionally, the determining whether there is data transmission in progress by a first drone in the drone network, and whether the data transmission is interfered by a first interference signal includes:
detecting first spectrum detection information corresponding to the cheating node, wherein the first spectrum detection information comprises spectrum information and power distribution information of a network environment in a preset range of the cheating node;
sending the first spectrum detection information to other unmanned aerial vehicles in the unmanned aerial vehicle network;
receiving second spectrum detection information sent by the other unmanned aerial vehicles, wherein the second spectrum detection information comprises spectrum information and power distribution information of a network environment in a predetermined range of the other unmanned aerial vehicles;
and judging whether the first unmanned aerial vehicle is carrying out data transmission and whether the data transmission is interfered by the first interference signal in the unmanned aerial vehicle network according to the first frequency spectrum detection information, the second frequency spectrum detection information and a pre-stored frequency spectrum detection information processing algorithm.
Optionally, the determining whether there is data transmission in progress by the first drone in the drone network, and whether the data transmission is interfered by the first interfering signal further includes:
receiving an identity setting instruction sent by a console in the unmanned aerial vehicle network, wherein the identity setting instruction is used for setting a cheating node in the unmanned aerial vehicle network;
and setting the identity information of the node as a deception node according to the identity setting instruction.
The machine-readable storage medium may include a RAM (Random Access Memory) and may also include a NVM (Non-Volatile Memory), such as at least one disk Memory. Additionally, the machine-readable storage medium may be at least one memory device located remotely from the aforementioned processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The embodiment of the invention provides a deception jamming prevention method and a deception jamming prevention device based on cognition in an unmanned aerial vehicle network. And when the optimal deception income power is larger than a preset income threshold value, the deception node sends deception signals to a second unmanned aerial vehicle which receives the collected data corresponding to the data transmission based on the optimal deception channel according to the optimal deception income power. Because the total transmission power of the interference source is fixed and unchanged, the deception node reduces the transmission power of the first interference signal and improves the first unmanned network environment by inducing the interference source to send a second interference signal which interferes the deception signal. Based on this scheme, can improve unmanned aerial vehicle's anti-interference effect.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.

Claims (6)

1. A cognition-based deception jamming prevention method in a drone network, the method being applied to a deception node in the drone network, the drone network further including a plurality of other drones, the method comprising:
judging whether data transmission in progress of a first unmanned machine exists in the unmanned aerial vehicle network and whether the data transmission is interfered by a first interference signal;
if the data transmission exists and is interfered by the first interference signal, acquiring network parameters of the unmanned aerial vehicle network, wherein the network parameters at least comprise deception channel coefficients of all deception channels in the deception nodes and interference channel coefficients of all interference channels corresponding to all the deception channels;
determining an optimal deception channel according to the network parameters and a prestored deception channel selection algorithm;
determining the optimal deception income power of the optimal deception channel according to the network parameters and a prestored deception power distribution strategy;
if the optimal deception income power is larger than a preset income threshold value, sending a prestored deception signal to a second unmanned aerial vehicle in the unmanned aerial vehicle network through the optimal deception channel based on the optimal deception income power, wherein the second unmanned aerial vehicle is an unmanned aerial vehicle for receiving the collected data corresponding to the data transmission;
wherein, the determining the best deception channel according to the network parameters and a prestored deception channel selection algorithm comprises:
obtaining a deception channel coefficient of each deception channel and the interference channel coefficient of each interference channel corresponding to each deception channel;
aiming at each deception channel, determining a channel coefficient ratio of the deception channel according to a deception channel coefficient of the deception channel and the interference channel coefficient corresponding to the deception channel;
comparing the channel coefficient ratios of the deception channels to determine the maximum channel coefficient ratio;
if the spoofed channel with the maximum channel coefficient ratio is unique, taking the spoofed channel with the maximum channel coefficient ratio as a best spoofed channel;
if the deception channel with the maximum channel coefficient ratio is not unique, determining an interference channel corresponding to each deception channel with the maximum channel coefficient ratio, determining a target interference channel with the maximum interference channel coefficient in the determined interference channels, and taking the deception channel corresponding to the target interference channel as an optimal deception channel;
the determining the optimal spoofing revenue power of the optimal spoofing channel according to the network parameters and a pre-stored spoofing power distribution strategy comprises the following steps:
establishing a deception income function of the deception node and an interference income function of an interference source according to the network parameters, wherein the deception income function is used for expressing the income obtained by deception of the deception node, and the interference income function is used for expressing the income obtained by interference of the interference source on the deception node;
and calculating the optimal deception income power according to the deception income function, the interference income function and a prestored Stenberg game algorithm.
2. The method of claim 1, wherein the determining whether there is data transmission in progress by a first drone in the drone network and whether the data transmission is interfered by a first interfering signal comprises:
detecting first spectrum detection information corresponding to the cheating node, wherein the first spectrum detection information comprises spectrum information and power distribution information of a network environment in a preset range of the cheating node;
sending the first spectrum detection information to other unmanned aerial vehicles in the unmanned aerial vehicle network;
receiving second spectrum detection information sent by the other unmanned aerial vehicles, wherein the second spectrum detection information comprises spectrum information and power distribution information of a network environment in a predetermined range of the other unmanned aerial vehicles;
and judging whether the first unmanned aerial vehicle is carrying out data transmission and whether the data transmission is interfered by the first interference signal in the unmanned aerial vehicle network according to the first frequency spectrum detection information, the second frequency spectrum detection information and a pre-stored frequency spectrum detection information processing algorithm.
3. The method of claim 1, wherein before determining whether there is data transmission in progress by a first drone in the drone network and whether the data transmission is interfered by a first interfering signal, further comprising:
receiving an identity setting instruction sent by a console in the unmanned aerial vehicle network, wherein the identity setting instruction is used for setting a cheating node in the unmanned aerial vehicle network;
and setting the identity information of the node as a deception node according to the identity setting instruction.
4. A cognition-based deception jamming prevention apparatus in a drone network, the apparatus being applied to a deception node in the drone network, the drone network further including a plurality of other drones, the apparatus comprising:
the judging module is used for judging whether data transmission in progress of a first unmanned machine exists in the unmanned aerial vehicle network and whether the data transmission is interfered by a first interference signal;
an obtaining module, configured to obtain a network parameter of the drone network when the data transmission exists and is interfered by the first interference signal, where the network parameter at least includes a spoofed channel coefficient of each spoofed channel in the spoofed node and an interference channel coefficient of each interference channel corresponding to each spoofed channel;
the first determining module is used for determining the optimal deception channel according to the network parameters and a prestored deception channel selection algorithm;
a second determining module, configured to determine an optimal spoofing revenue power of the optimal spoofing channel according to the network parameter and a spoofing power allocation policy stored in advance;
a sending module, configured to send, based on the optimal spoofing revenue power, a pre-stored spoofing signal to a second unmanned aerial vehicle in the unmanned aerial vehicle network through the optimal spoofing channel when the optimal spoofing revenue power is greater than a preset revenue threshold, where the second unmanned aerial vehicle is an unmanned aerial vehicle that receives the collected data corresponding to the data transmission;
wherein the first determining module comprises:
an obtaining submodule, configured to obtain a deception channel coefficient of each deception channel and the interference channel coefficient of each interference channel corresponding to each deception channel;
the determining submodule is used for determining the channel coefficient ratio of each deception channel according to the deception channel coefficient of the deception channel and the interference channel coefficient corresponding to the deception channel;
the comparison submodule is used for comparing the channel coefficient ratios of the deceptive channels and determining the maximum channel coefficient ratio;
a selecting sub-module for taking the spoofed channel having the maximum channel coefficient ratio as a best spoofed channel when the spoofed channel having the maximum channel coefficient ratio is unique;
the selecting submodule is further configured to determine, when the spoofed channels with the maximum channel coefficient ratio are not unique, interference channels corresponding to the spoofed channels with the maximum channel coefficient ratio, determine, among the determined interference channels, a target interference channel with a maximum interference channel coefficient, and use the spoofed channel corresponding to the target interference channel as an optimal spoofed channel;
the second determining module includes:
the establishing submodule is used for establishing a deception income function of the deception node and an interference income function of an interference source according to the network parameters, wherein the deception income function is used for expressing the income obtained by deception of the deception node, and the interference income function is used for expressing the income obtained by interference of the interference source on the deception node;
and the calculation submodule is used for calculating the optimal cheating income power according to the cheating income function, the interference income function and a prestored Stenberg game algorithm.
5. The apparatus of claim 4, wherein the determining module comprises:
the detection submodule is used for detecting first spectrum detection information corresponding to the deception node, wherein the first spectrum detection information comprises spectrum information and power distribution information of a network environment in a predetermined range of the deception node;
a sending submodule, configured to send the first spectrum detection information to other drones in the drone network;
the receiving submodule is used for receiving second spectrum detection information sent by other unmanned aerial vehicles, wherein the second spectrum detection information comprises spectrum information and power distribution information of a network environment in a preset range of the other unmanned aerial vehicles;
and the judging submodule is used for judging whether the first unmanned aerial vehicle is carrying out data transmission and whether the data transmission is interfered by the first interference signal in the unmanned aerial vehicle network according to the first frequency spectrum detection information, the second frequency spectrum detection information and a prestored frequency spectrum detection information processing algorithm.
6. The apparatus of claim 4, further comprising:
the system comprises a receiving module, a configuration module and a configuration module, wherein the receiving module is used for receiving an identity setting instruction sent by a console in the unmanned aerial vehicle network, and the identity setting instruction is used for setting a cheating node in the unmanned aerial vehicle network;
and the setting module is used for setting the identity information of the user as a deception node according to the identity setting instruction.
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