CN109547136B - Distributed cooperative spectrum sensing method based on maximum and minimum distance clustering - Google Patents

Distributed cooperative spectrum sensing method based on maximum and minimum distance clustering Download PDF

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CN109547136B
CN109547136B CN201910079086.4A CN201910079086A CN109547136B CN 109547136 B CN109547136 B CN 109547136B CN 201910079086 A CN201910079086 A CN 201910079086A CN 109547136 B CN109547136 B CN 109547136B
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许文俊
聂汝柳
张治�
冯志勇
张平
林家儒
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Beijing University of Posts and Telecommunications
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Abstract

The invention discloses a distributed cooperative spectrum sensing method based on maximum and minimum distance clustering in a cognitive unmanned aerial vehicle network, wherein a maximum and minimum distance clustering algorithm is utilized to divide a cognitive unmanned aerial vehicle into a plurality of clusters, and cluster heads are selected from the clusters; each cognitive unmanned aerial vehicle uses energy detection to obtain perception information of a master user; performing two-step fusion according to the clustering condition and the perception information of each cognitive unmanned aerial vehicle, so that each cognitive unmanned aerial vehicle obtains a uniformly converged global induction information; judging whether a master user occupies a frequency spectrum or not according to the global sensing information; according to the invention, the cognitive unmanned aerial vehicles with similar positions and mobility are divided into a cluster, so that the communication between members in the cluster and the cluster head is ensured, the clustering algorithm reduces the information exchange times during distributed fusion, and the detection efficiency is improved; the fusion of the global sensing information can be realized under the condition of no fusion center through the two-step fusion, the frequency of sensing information exchange is reduced, and the fast and efficient spectrum detection is realized.

Description

Distributed cooperative spectrum sensing method based on maximum and minimum distance clustering
Technical Field
The invention relates to a distributed cooperative spectrum sensing technology, in particular to a distributed cooperative spectrum sensing method based on maximum and minimum distance clustering.
Background
The spectrum sensing means that a cognitive user acquires spectrum use information in a wireless network through various signal detection and processing means, and a spectrum sensing technology can detect current spectrum information, discover idle spectrum resources to reuse the idle spectrum resources, improve the spectrum utilization rate and relieve spectrum supply and demand contradictions caused by spectrum shortage. Since the detection performance is poor when a single cognitive user performs spectrum sensing, a plurality of cognitive users are used for cooperative spectrum sensing. For cognitive users with mobility, it is difficult to send all perception information to a fusion center for data fusion, and distributed frequency cooperative spectrum perception is cooperative perception realized by a plurality of cognitive users only through information exchange with adjacent neighbor nodes of the cognitive users without an information fusion center. Therefore, distributed cooperative spectrum sensing has better generality and robustness for mobile secondary users. Because the fusion center does not exist, the addition and the exit of any node can not influence the operation of the perception network. In addition, the problem of low spectrum detection performance caused by shadow, fading and hidden terminals in single-user sensing can be effectively solved by using distributed cooperative spectrum sensing.
At present, the cooperative perception modes of multiple cognitive users are mainly divided into a centralized mode and a distributed mode. For example, in Cluster-based cooperative Spectrum Sensing in Cognitive Radio Systems, Cognitive users are divided into a plurality of clusters, a Cluster head node collects Sensing information of member nodes in the clusters and then makes a binary system in-Cluster judgment result, then each Cluster head node sends the judgment result to a fusion center for decision fusion, and finally makes a judgment whether a main user exists or not. The Chinese patent with the application patent number '201310745504.1' divides cognitive users into a plurality of clusters by using a fuzzy mean clustering method according to the receiving signal-to-noise ratio of the cognitive users, collects member nodes in the clusters for soft fusion by cluster heads, and then sends a fusion result to a fusion center for inter-cluster fuzzy decision soft fusion to obtain a decision result.
However, in the cognitive unmanned aerial vehicle network, because the fast movement of the unmanned aerial vehicle and the continuous change of the network topology structure of the cognitive unmanned aerial vehicle are difficult to transmit perception information to the same receiving terminal, the scheme is not suitable for the cognitive unmanned aerial vehicle network. The "handed distributed responses-Based collaborative Spectrum-Sensing Scheme in cognitive radios" proposes a Scheme in which a plurality of cognitive users achieve a convergence result by exchanging information with their neighboring nodes for a plurality of times without a fusion center, and then make a decision according to the convergence result. However, when the number of secondary users is large, multiple information exchanges are needed to reach the convergence value, the fusion time delay is increased, and the timeliness and the accuracy of frequency spectrum detection are reduced.
Disclosure of Invention
In view of the above, the present invention is directed to a distributed cooperative spectrum sensing method based on maximum and minimum distance clustering, which can solve one or all of the above problems.
A distributed cooperative spectrum sensing method based on maximum and minimum distance clustering is suitable for a cognitive unmanned aerial vehicle network, wherein the cognitive unmanned aerial vehicle network comprises a master user and N cognitive unmanned aerial vehicles, and the method comprises the following steps:
dividing the cognitive unmanned aerial vehicle into a plurality of clusters by using a maximum and minimum distance clustering algorithm, and selecting cluster heads in the clusters;
each cognitive unmanned aerial vehicle uses energy detection to obtain perception information of a master user;
performing two-step fusion according to clustering conditions and perception information of each cognitive unmanned aerial vehicle to enable each cognitive unmanned aerial vehicle to
Obtaining a uniformly converged global induction information;
and judging whether the master user occupies the frequency spectrum or not according to the global sensing information.
Further, the algorithm for clustering the maximum and minimum distances includes:
step one, randomly selecting a cognitive unmanned aerial vehicle as a first clustering center Z1；
Step two, calculating other cognitive unmanned aerial vehicles and Z1The Euclidean distance between, the distance Z is selected1The farthest cognitive unmanned aerial vehicle is the second clustering center Z2And D is1,2＝||Z1-Z2I, let D1,2As a discrimination parameter;
step three, calculating the rest cognitive unmanned aerial vehicles and Z1And a second clustering center Z2And selects the minimum distance, i.e., min (D)i,1,Di,2,..), i ═ 1,2, 3.. N, the set of all minimum distances is denoted { min (D)11,D12,...),min(D21,D22,...),min(Di1,Di2,...)...}，i＝1,2,3,...N；
Step four, selecting the maximum distance D in all the minimum distance sets as max (min (D)i,1,Di,2,..)), wherein i ═ 1,2, 3.;
step five, the maximum distance D and the discrimination parameter D are compared1,2Making a judgment if D > theta.D1，2If so, the cognitive unmanned aerial vehicle generating the maximum distance is the newly added clustering center, and the third step and the fourth step are returned to be executed until no new clustering center is generated; if D < theta.D1,2Executing the next step, wherein theta is an initialization parameter;
step six, dividing the cognitive unmanned aerial vehicle outside the clustering center into clusters represented by the clustering center closest to the cognitive unmanned aerial vehicle;
step seven, judging the number of the cognitive unmanned aerial vehicles in the cluster, if only one cognitive unmanned aerial vehicle exists in one cluster, repeatedly executing the step six, and if a plurality of cognitive unmanned aerial vehicles exist in one cluster, executing the next step;
and step eight, calculating the trust value of each cognitive unmanned aerial vehicle, and taking the cognitive unmanned aerial vehicle with the largest trust value in one cluster as a cluster head.
Further, the euclidean distance is calculated in the following manner:
Figure GDA0002129734090000031
wherein Di,jIs Euclidean distance, x, of unmanned aerial vehicle i and unmanned aerial vehicle ji、xjRepresenting the positions, the moving speed and the moving direction, omega, of the cognitive unmanned aerial vehicle i and the cognitive unmanned aerial vehicle j in the three-dimensional space1As a weight factor, ω, of the first cluster center2Is the weight factor for the second cluster center,
Figure GDA0002129734090000032
the distances of the cognitive unmanned aerial vehicle i and the cognitive unmanned aerial vehicle j at the beginning and the end of the time slot are respectively.
Further, the calculation method of the trust value of the cognitive unmanned aerial vehicle is as follows: the trust value of cognitive drone i is
Figure GDA0002129734090000033
ωiIs a weight factor, i ═ 1,2,3, Δ t is a time slot, d0Distance, d, from the master user to the position of the cognitive drone at the start of the time slot1The distance from the master user to the position of the cognitive unmanned aerial vehicle at the end of the time slot is V, and the moving speed of the cognitive unmanned aerial vehicle is v.
Further, the two-step fusion includes intra-cluster centralized fusion and inter-cluster distributed fusion, and the specific steps are as follows:
detecting the energy used by each cognitive unmanned aerial vehicle in the cluster to obtain the perception information of the master user;
each cognitive unmanned aerial vehicle in the cluster sends sensing information to a cluster head;
the cluster head performs soft fusion on perception information of each cognitive unmanned aerial vehicle in the cluster to obtain fusion perception information;
each cluster head and the adjacent cluster head exchange fusion sensing information, distributed fusion is carried out, the fusion result is judged, if the fusion result is not convergent, the fusion is continued, and if the fusion result with consistent convergence is obtained, the next step is executed;
and obtaining global induction information from the fusion result, wherein the global induction information is a convergence value x, and comparing the convergence value x with a decision threshold lambda to make a final decision to obtain a result of whether the master user occupies the frequency spectrum.
Further, the manner of obtaining the fusion perception information is as follows:
each cognitive unmanned aerial vehicle receives a main user signal in an energy detection mode, and obtains perception information Y after sampling for a plurality of timesj；
Sensing information Y of cognitive unmanned aerial vehicle in cluster head fusion clusterjTo obtain the fusion perception information
Figure GDA0002129734090000034
Wherein
Figure GDA0002129734090000041
Wherein M is the number of clusters, N represents the number of cognitive drones, hiThe cluster heads are shown as being in the cluster,
Figure GDA0002129734090000042
indicates cluster head hiSet of internal cognitive drones, j being index value of cognitive drones, ωjIs a weight factor and satisfies
Figure GDA0002129734090000043
After centralized fusion is carried out in each cluster, distributed fusion is carried out on the in-cluster fusion perception information among cluster heads.
Further, the perception information YjComprises the following steps:
Figure GDA0002129734090000044
where m is the number of samples, yj(t) is a master user signal received by the cognitive unmanned aerial vehicle j:
Figure GDA0002129734090000045
hjrepresenting the channel correlation coefficient, sjFor a hypothetical primary user signal, nj(t) is a mean of 0 and a variance of
Figure GDA0002129734090000046
Additive white Gaussian noise, H1Spectrum occupation on behalf of primary users, H0The representative primary user does not occupy the spectrum.
Further, the distributed fusion mode is as follows:
each cluster head and the adjacent cluster head exchange sensing information, fuse the sensing information and update data to obtain a fusion iterative formula of each cluster head as
Figure GDA0002129734090000047
Wherein
Figure GDA0002129734090000048
Delta is the maximum node degree of the unmanned aerial vehicle network,
Figure GDA0002129734090000049
is a cluster head hiK is iteration times, the node degree of the unmanned aerial vehicle is the number of the adjacent unmanned aerial vehicles, and the maximum node degree is the node degree corresponding to the unmanned aerial vehicle with the largest neighbor node in the unmanned aerial vehicle network;
and calculating the global perception information according to the fusion detection value of each cluster head.
Further, the global perception information is obtained in the following manner:
carrying out centralized fusion in a cluster, after carrying out distributed fusion between clusters, diffusing perception information of each cognitive unmanned aerial vehicle to other cognitive unmanned aerial vehicles through an unmanned aerial vehicle network, and finally obtaining consistently converged global perception information by each cognitive unmanned aerial vehicle, wherein the global perception information is a convergence value x:
Figure GDA0002129734090000051
order to
Figure GDA0002129734090000052
And will be
Figure GDA0002129734090000053
Substituting the above equation to obtain a convergence value:
Figure GDA0002129734090000054
and comparing the convergence value x with a decision threshold lambda to make a final decision:
Figure GDA0002129734090000055
wherein H1Spectrum occupation on behalf of primary users, H0The representative primary user does not occupy the spectrum.
According to the distributed cooperative spectrum sensing method based on the maximum and minimum distance clustering, the cognitive unmanned aerial vehicles with similar positions and mobility are divided into the same cluster, so that communication between member nodes and cluster heads in the cluster is guaranteed, in addition, the clustering algorithm guarantees that at least two cognitive unmanned aerial vehicles are contained in one cluster, the information exchange times during distributed fusion are reduced, and the detection efficiency is improved;
in the sensing data fusion stage, the centralized fusion is carried out in the clusters, the distributed fusion is carried out among the clusters, the fusion of the overall sensing information can be realized under the condition without a fusion center, the frequency of sensing information exchange is reduced, and the rapid and efficient frequency spectrum detection is realized.
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FIG. 1 is a diagram of an unmanned aerial vehicle scenario in an embodiment of the invention
Fig. 2 is a flowchart of a distributed cooperative spectrum sensing method based on maximum and minimum distance clustering according to an embodiment of the present invention;
FIG. 3 is a flow chart of a Max Min Cluster Algorithm according to an embodiment of the present invention;
FIG. 4 is a flow chart of a two-step fusion algorithm of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
The distributed cooperative spectrum sensing method based on the maximum and minimum distance clustering is suitable for a cognitive unmanned aerial vehicle network, wherein the cognitive unmanned aerial vehicle network comprises a master user P and N cognitive unmanned aerial vehicles, and the method comprises the following steps:
dividing the cognitive unmanned aerial vehicle into a plurality of clusters by using a maximum and minimum distance clustering algorithm, and selecting cluster heads from the clusters;
each cognitive unmanned aerial vehicle uses energy detection to obtain perception information of a master user P;
performing two-step fusion according to clustering conditions and perception information of each cognitive unmanned aerial vehicle to enable each cognitive unmanned aerial vehicle to
Obtaining a uniformly converged global induction information;
and judging whether the master user P occupies the frequency spectrum or not according to the global perception information.
For ease of discussion, we make the following assumptions. As shown in the scene diagram of the unmanned aerial vehicle in fig. 1, in the cognitive unmanned aerial vehicle network, there are a master user P and N cognitive unmanned aerial vehicles. Suppose that the cognitive drone moves randomly within one time slot Δ t, and its position at the beginning of the time slot is a (x)A,yA,zA) The position at the end of the slot is B (x)B,yB,zB) (ii) a The distances from P to A and B of the primary user are d0，d1(ii) a The distance between two cognitive unmanned planes i, j at the beginning and the end of a time slot is respectively
Figure GDA0002129734090000061
Specific steps of the distributed cooperative spectrum sensing method may be as shown in fig. 2:
step 101, a cognitive unmanned aerial vehicle is divided into a plurality of clusters by using a maximum and minimum distance clustering algorithm, and the clusters are selected
Clustering heads;
102, detecting the energy used by each cognitive unmanned aerial vehicle to obtain perception information of a master user P;
step 103, the cluster head carries out centralized fusion on perception information of all cognitive unmanned aerial vehicles in a cluster to obtain fusion
Synthesizing the perception information;
step 104, each cluster head exchanges fusion perception information with the adjacent cluster head to perform distributed fusion among clusters,
until each cognitive unmanned aerial vehicle obtains a uniformly converged global induction information;
and 105, judging whether the master user P occupies the frequency spectrum or not according to the global sensing information.
Specifically, the algorithm for maximum and minimum distance clustering is shown in fig. 2, and includes:
step 111, randomly selecting a cognitive unmanned aerial vehicle as a first clustering center Z1；
Step 112, calculating the remaining cognitive unmanned aerial vehicles and Z1The Euclidean distance between, the distance Z is selected1The farthest cognitive unmanned aerial vehicle is the second clustering center Z2And D is1,2＝||Z1-Z2I, let D1,2As a discrimination parameter;
step 113, calculating the remaining cognitive unmanned aerial vehicles and Z1And the other cognitive unmanned aerial vehicles Z2And selects the minimum distance, i.e., min (D)i,1,Di,2,..), i ═ 1, 2.. N, the set of all minimum distances is denoted { min (D)11,D12,...),min(D21,D22,...),min(Di1,Di2,..) }, the Euclidean distance is calculated in the following way:
Figure GDA0002129734090000062
wherein Di,jIs Euclidean distance, x, of unmanned aerial vehicle i and unmanned aerial vehicle ji、xjRepresenting the positions, the moving speed and the moving direction, omega, of the cognitive unmanned aerial vehicle i and the cognitive unmanned aerial vehicle j in the three-dimensional space1As a weight factor, ω, of the first cluster center2Is the weight factor for the second cluster center,
Figure GDA0002129734090000071
the distances of the cognitive unmanned aerial vehicle i and the cognitive unmanned aerial vehicle j at the beginning and the end of the time slot are respectively;
step 114, select the maximum distance D of all the minimum distance sets as max (min (D)i,1,Di,2,...))；
Step 115, the maximum distance D and the discrimination parameter D1,2Making a judgment if D > theta.D1，2If so, the cognitive unmanned aerial vehicle with the largest distance is the newly added clustering center, and the step 113 and the step 114 are returned to be executed until no new clustering center is generated; if D < theta.D1,2Executing the next step, wherein theta is an initialization parameter;
step 116, dividing the cognitive unmanned aerial vehicle outside the clustering center into clusters represented by the clustering center closest to the cognitive unmanned aerial vehicle;
step 117, judging the number of the cognitive unmanned aerial vehicles in the cluster, if only one cognitive unmanned aerial vehicle exists in one cluster, repeatedly executing the step 116, and if a plurality of cognitive unmanned aerial vehicles exist in one cluster, executing the next step;
step 118, calculating the trust value of each cognitive unmanned aerial vehicle, and taking the cognitive unmanned aerial vehicle with the largest trust value in a cluster as a cluster head, wherein the trust value calculation mode of the cognitive unmanned aerial vehicle is as follows: the trust value of cognitive drone i is
Figure GDA0002129734090000072
ωiIs a weight factor, i ═ 1,2,3, Δ t is a time slot, d0Distance, d, from the master user P to the position of the cognitive UAV at the beginning of the time slot1The distance from the master user P to the position of the cognitive unmanned aerial vehicle at the end of the time slot is v, and v is the moving speed of the cognitive unmanned aerial vehicle.
In the invention, the maximum and minimum distance clustering algorithm is applied to the distributed cooperative sensing algorithm, the maximum and minimum distance clustering algorithm tends to divide the cognitive unmanned aerial vehicles with similar positions and mobility into the same cluster, so that the communication between member nodes and cluster heads in the cluster is ensured, in addition, the clustering algorithm ensures that at least two cognitive unmanned aerial vehicles are contained in one cluster, the information exchange times in the distributed fusion are reduced, and the detection efficiency is improved.
Further, the two-step fusion includes intra-cluster centralized fusion and inter-cluster distributed fusion, and the specific steps are shown in fig. 3 and include:
step 121, detecting the energy used by each cognitive unmanned aerial vehicle in the cluster to obtain perception information of a master user P;
step 122, each cognitive unmanned aerial vehicle sends sensing information to the cluster head;
step 123, soft fusion is carried out on the perception information of each cognitive unmanned aerial vehicle in the cluster by the cluster head to obtain fusion perception information;
step 124, exchanging fusion sensing information between each cluster head and the adjacent cluster head, fusing, judging a fusion result, continuing fusing if the fusion result is not convergent, and executing the next step if a consistent convergent fusion result is obtained;
and step 125, obtaining global induction information from the fusion result, wherein the global induction information is a convergence value x, and comparing the convergence value x with a decision threshold lambda to make a final decision to obtain a result of whether the primary user P occupies the frequency spectrum.
Further, the manner of obtaining the fusion perception information is as follows:
each cognitive unmanned aerial vehicle receives a master user P signal in an energy detection mode, and obtains perception information Y after sampling for a plurality of timesj；
Sensing information Y of cognitive unmanned aerial vehicle fused with cluster headjTo obtain the fusion perception information
Figure GDA0002129734090000081
Wherein
Figure GDA0002129734090000082
Wherein M is the number of clusters, N represents the number of cognitive drones, hiThe cluster heads are shown as being in the cluster,
Figure GDA0002129734090000083
indicates cluster head hiSet of internal cognitive drones, j being index value of cognitive drones, ωiIs a weight factor and satisfies
Figure GDA0002129734090000084
After centralized fusion is carried out in each cluster, distributed fusion is carried out on the in-cluster fusion perception information among cluster heads.
Further, the perception information YjComprises the following steps:
Figure GDA0002129734090000085
where m is the number of samples, yj(t) is a master user P signal received by the cognitive unmanned aerial vehicle j:
Figure GDA0002129734090000086
hjrepresenting the channel correlation coefficient, sjFor a hypothetical primary user P signal, nj(t) is a mean of 0 and a variance of
Figure GDA0002129734090000087
Additive white Gaussian noise, H1The representative primary user P occupies the frequency spectrum, H0The primary user P does not occupy the spectrum.
Further, the distributed fusion mode is as follows:
each cluster head and the adjacent cluster head perform frequency spectrum sensing, fusion sensing information is exchanged, data updating is performed, and a fusion iterative formula of each cluster head is obtained
Figure GDA0002129734090000091
Wherein
Figure GDA0002129734090000092
Delta is the maximum node degree of the unmanned aerial vehicle network,
Figure GDA0002129734090000093
is a cluster head hiK is iteration times, the node degree of the unmanned aerial vehicle is the number of the adjacent unmanned aerial vehicles, and the maximum node degree is the node degree corresponding to the unmanned aerial vehicle with the largest neighbor node in the unmanned aerial vehicle network;
and calculating the global perception information according to the fusion detection value of each cluster head.
Further, the global perception information is obtained in the following manner:
the perception information of each cognitive unmanned aerial vehicle is diffused to other cognitive unmanned aerial vehicles through an unmanned aerial vehicle network until each cognitive unmanned aerial vehicle obtains a consistently convergent global perception information, wherein the global perception information is a convergence value x:
Figure GDA0002129734090000094
order to
Figure GDA0002129734090000095
And will be
Figure GDA0002129734090000096
Substituting the above equation to obtain a convergence value:
Figure GDA0002129734090000097
rule of integrating the above formula with centralized soft
Figure GDA0002129734090000098
In contrast, in the case where there is no fusion center, the present embodiment is clearThe scheme finally obtains the global perception information which is the same as the centralized fusion.
And comparing the convergence value x with a decision threshold lambda to make a final decision:
Figure GDA0002129734090000099
wherein H1The representative primary user P occupies the frequency spectrum, H0The primary user P does not occupy the spectrum.
The invention applies a two-step fusion method to data fusion, the cluster head nodes collect member nodes in the cluster to perform centralized soft fusion, and the cluster head nodes exchange sensing information mutually to realize distributed data fusion. By the two-step fusion method, the fusion of the global sensing information is realized on the premise of no fusion center, the frequency of sensing information exchange is reduced, the fast and efficient spectrum detection is realized, and the fast and efficient spectrum sensing under the unmanned aerial vehicle scene can be realized.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the invention, also features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements and the like that may be made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (5)

1. Distributed cooperative spectrum sensing method based on maximum and minimum distance clustering is suitable for a cognitive unmanned aerial vehicle network, wherein the cognitive unmanned aerial vehicle network is provided with a master user and N cognitive unmanned aerial vehicles, and the distributed cooperative spectrum sensing method is characterized by comprising the following steps:
dividing the cognitive unmanned aerial vehicle into a plurality of clusters by using a maximum and minimum distance clustering algorithm, and selecting cluster heads in the clusters;
each cognitive unmanned aerial vehicle uses energy detection to obtain perception information of a master user;
performing two-step fusion according to the clustering condition and the perception information of each cognitive unmanned aerial vehicle, so that each cognitive unmanned aerial vehicle obtains a uniformly converged global induction information;
judging whether a master user occupies a frequency spectrum or not according to the global sensing information;
the algorithm for maximum and minimum distance clustering comprises the following steps:
step one, randomly selecting a cognitive unmanned aerial vehicle as a first clustering center Z1；
Step two, calculating other cognitive unmanned aerial vehicles and Z1The Euclidean distance between, the distance Z is selected1The farthest cognitive unmanned aerial vehicle is the second clustering center Z2And D is1,2＝||Z1-Z2I, let D1,2As a discrimination parameter;
step three, calculating the rest cognitive unmanned aerial vehicles and Z1And a second clustering center Z2And selects the minimum distance, i.e., min (D)i,1,Di,2,..), i ═ 1,2, 3.. N, the set of all minimum distances is denoted { min (D)11,D12,...),min(D21,D22,...),min(Di1,Di2,...)...}，i＝1,2,3,...N；
Step four, selecting the maximum distance D in all the minimum distance sets as max (min (D)i,1,Di,2,..)), wherein i ═ 1,2, 3.;
step five, the maximum distance D and the discrimination parameter D are compared1,2Make a judgment if D>θ·D1,2If so, the cognitive unmanned aerial vehicle generating the maximum distance is the newly added clustering center, and the third step and the fourth step are returned to be executed until no new clustering center is generated; if D < theta.D1,2Executing the next step, wherein theta is an initialization parameter;
step six, dividing the cognitive unmanned aerial vehicle outside the clustering center into clusters represented by the clustering center closest to the cognitive unmanned aerial vehicle;
step seven, judging the number of the cognitive unmanned aerial vehicles in the cluster, if only one cognitive unmanned aerial vehicle exists in one cluster, repeatedly executing the step six, and if a plurality of cognitive unmanned aerial vehicles exist in one cluster, executing the next step;
step eight, calculating the trust value of each cognitive unmanned aerial vehicle, and taking the cognitive unmanned aerial vehicle with the largest trust value in a cluster as a cluster head;
the two-step fusion comprises cluster centralized fusion and cluster distributed fusion, and comprises the following specific steps:
detecting the energy used by each cognitive unmanned aerial vehicle in the cluster to obtain the perception information of the master user;
each cognitive unmanned aerial vehicle in the cluster sends sensing information to a cluster head;
the cluster head performs soft fusion on perception information of each cognitive unmanned aerial vehicle in the cluster to obtain fusion perception information;
each cluster head and the adjacent cluster head exchange fusion sensing information, distributed fusion is carried out, the fusion result is judged, if the fusion result is not convergent, the fusion is continued, and if the fusion result with consistent convergence is obtained, the next step is executed;
obtaining global induction information from the fusion result, wherein the global induction information is the convergence value x*Will converge to a value x*And comparing the result with a judgment threshold lambda to make final judgment to obtain the result of whether the master user occupies the frequency spectrum.
2. The distributed cooperative spectrum sensing method based on the maximum and minimum distance clustering according to claim 1, wherein the Euclidean distance is calculated in a manner that:
Figure FDA0002228098080000021
wherein Di,jIs Euclidean distance, x, of unmanned aerial vehicle i and unmanned aerial vehicle ji、xjRepresenting the positions, the moving speed and the moving direction, omega, of the cognitive unmanned aerial vehicle i and the cognitive unmanned aerial vehicle j in the three-dimensional space1As a weight factor, ω, of the first cluster center2Is the weight factor for the second cluster center,
Figure FDA0002228098080000022
the distances of the cognitive unmanned aerial vehicle i and the cognitive unmanned aerial vehicle j at the beginning and the end of the time slot are respectively.
3. The distributed cooperative spectrum sensing method based on the maximum and minimum distance clustering according to claim 1, wherein the trust value of the cognitive unmanned aerial vehicle is calculated in a manner that: the trust value of cognitive drone i is
Figure FDA0002228098080000023
ωiIs a weight factor, i is 1,2,3, △ t is a time slot, d is0Distance, d, from the master user to the position of the cognitive drone at the start of the time slot1The distance from the master user to the position of the cognitive unmanned aerial vehicle at the end of the time slot is V, and the moving speed of the cognitive unmanned aerial vehicle is v.
4. The distributed cooperative spectrum sensing method based on the maximum and minimum distance clustering according to claim 1, wherein the sensing information is fused in a manner that:
each cognitive unmanned aerial vehicle receives a main user signal in an energy detection mode, and obtains perception information Y after sampling for a plurality of timesj；
Sensing information Y of cognitive unmanned aerial vehicle fused with cluster headjTo obtain the fusion perception information
Figure FDA0002228098080000024
Wherein
Figure FDA0002228098080000025
Wherein M is the number of clusters, N represents the number of cognitive drones, hiThe cluster heads are shown as being in the cluster,
Figure FDA0002228098080000026
indicates cluster head hiInternal recognitionKnowing the set of drones, j is the index value, ω, of the cognitive dronesjIs a weight factor and satisfies
Figure FDA0002228098080000031
After centralized fusion is carried out in each cluster, distributed fusion is carried out on the in-cluster fusion perception information among cluster heads.
5. The distributed cooperative spectrum sensing method based on maximum and minimum distance clustering according to claim 4, wherein the sensing information Y isjComprises the following steps:
Figure FDA0002228098080000032
where m is the number of samples, yj(t) is a master user signal received by the cognitive unmanned aerial vehicle j:
Figure FDA0002228098080000033
hjrepresenting the channel correlation coefficient, sjFor a hypothetical primary user signal, nj(t) is a mean of 0 and a variance of σj 2Additive white Gaussian noise, H1Spectrum occupation on behalf of primary users, H0The representative primary user does not occupy the spectrum.
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