CN109582040A - Unmanned aerial vehicle cluster formation and performance vulnerability assessment method and system - Google Patents
Unmanned aerial vehicle cluster formation and performance vulnerability assessment method and system Download PDFInfo
- Publication number
- CN109582040A CN109582040A CN201910077285.1A CN201910077285A CN109582040A CN 109582040 A CN109582040 A CN 109582040A CN 201910077285 A CN201910077285 A CN 201910077285A CN 109582040 A CN109582040 A CN 109582040A
- Authority
- CN
- China
- Prior art keywords
- cluster
- unmanned plane
- formation
- performance
- fragility
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000015572 biosynthetic process Effects 0.000 title claims abstract description 135
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000011156 evaluation Methods 0.000 claims abstract description 17
- 238000004891 communication Methods 0.000 claims description 50
- 230000003993 interaction Effects 0.000 claims description 8
- 238000012038 vulnerability analysis Methods 0.000 claims description 8
- 239000002245 particle Substances 0.000 claims description 4
- 230000001419 dependent effect Effects 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 3
- 238000013461 design Methods 0.000 abstract description 5
- 238000006243 chemical reaction Methods 0.000 abstract description 3
- 238000005755 formation reaction Methods 0.000 description 95
- 230000001276 controlling effect Effects 0.000 description 9
- 230000006872 improvement Effects 0.000 description 3
- 230000007423 decrease Effects 0.000 description 2
- 238000009795 derivation Methods 0.000 description 2
- 239000006185 dispersion Substances 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000000988 bone and bone Anatomy 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000005314 correlation function Methods 0.000 description 1
- 244000144992 flock Species 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Debugging And Monitoring (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention provides an unmanned aerial vehicle cluster formation and performance vulnerability assessment method and system. Comprises the following steps of 1: acquiring information of an unmanned aerial vehicle cluster; step 2: constructing an improved control model of the unmanned aerial vehicle cluster; and step 3: combining the improved control model of the unmanned aerial vehicle cluster in the step 2, and constructing a formation evaluation function of the cluster according to the formation driving force; and 4, step 4: calculating the vulnerability of the cluster formation to interference; according to the method, the reaction degree of the cluster to different interferences is reflected by quantifying the vulnerability degree of the formation and the vulnerability degree of the cluster performance, so that the cluster nodes are improved in a targeted manner during cluster design, and the task completion efficiency of the unmanned aerial vehicle cluster is improved.
Description
Technical field
The invention belongs to unmanned plane cluster overall performance evaluation areas more particularly to a kind of unmanned plane cluster formations and performance
Vulnerability assessment method and system.
Background technique
In recent years, small-sized, inexpensive unmanned plane cluster receives significant attention, and compares with single unmanned plane, multiple no-manned plane
Cluster all has a clear superiority in terms of flexibility, fault-tolerance, collaborative, task, so the application of unmanned plane cluster
It is more and more extensive, such as there are the show of unmanned plane light, express delivery dispatching, Fight Fire in Forest, personnel to search and rescue in civil field, in military field
Carried out investigations, interfered with unmanned plane cluster, rounded up and hunt, bombed, dash forward it is anti-etc..Currently, how to analyze nobody for unmanned plane cluster
Survival ability, the communication technology, clustered control, overall performance of machine cluster etc. are unmanned air vehicle technique forward position focus of attention.
In unmanned plane cluster overall performance analytical technology, the focus such as stability, reliability, survivability, toughness have
Difference.As stability study system has preferable anti-interference ability to the fluctuation of itself；Reliability consideration under the defined conditions,
System completes the ability of predetermined function, more to consider that faults itself influences；What survivability was studied is the anti-strike capability of system；And
Tough Journal of Sex Research is the recovery capability of system after disturbed components of system as directed, attack or damage.These performances are mostly from complexity at present
The angle of network is explored, such as document [1] of the complex network survivability model under the conditions of imperfect information " Wu J, Deng H-
Z,J TanY,et al.Vulnerability of complex networks under intentional attack
With incomplete information [M] .40.2007:2665. " and document [2] " Wu J, Deng H-Z, TanY-J,
et al.Attack Vulnerability of Complex Networks Based on Local Information[M]
.21.2007:1007-1014. described in ", classical random failure and deliberately attack are two special cases of the model.Document [1] and
Document [2] has gone out any degree distribution broad sense random network under the conditions of imperfect information using probability generating function method analytic derivation
Survivability metric parameter, proposition establish the optimal challenge model of complex network under the conditions of imperfect information, utilize probability mother's letter
Counting method analytic derivation has gone out the optimal attack strategies of scales-free network under the conditions of imperfect information.Document [3] " Tran H T.A
complex networks approach to designing resilient system-of-systems[D].Georgia
Institute of Technology, 2015. " have studied and how rationally to be weighed with indexs such as system recovery time and system performances
Measure the toughness of complex networks system.Although unmanned plane cluster can be regarded as a complex network, the spy of the two plus communication
It puts or distinguishing, there are also formation and distinctive kinetics models in addition to having the characteristics that network for unmanned plane cluster, therefore directly
Unmanned plane cluster is assessed with the correlated performance of complex network can not be fully described its most crucial feature.At present directly to nothing
In contrast it is the field for comparing blank that man-machine cluster integrally carries out comprehensive vulnerability analysis, and then the present invention comprehensively considers
Complex network, formation, the kinetic character of unmanned plane cluster give a kind of vulnerability assessment method of unmanned plane cluster and are
System.
The dynamic fundamental models of self-organizing unmanned plane cluster dynamic performance very widely used at present are Cuker-
Smale (CS) model, such as document [4] " Cucker F, Smale S.Emergent Behavior in Flocks [J] .IEEE
Transactions on Automatic Control, 2007,52 (5): 852-862. " and document [5] " Cucker F,
Smale S.On the mathematics of emergence [M] .2.2007:197-227. " is described.Since CS is because having
It is good explanatory, become most practical one of mathematical theory model.Assuming that have the cluster of N frame unmanned plane, CS basic theories control
Simulation are as follows:
Wherein xi(t) and viIt (t) is respectively position and speed of i-th of unmanned plane in t moment, constant α is used to measure group
Interaction size between middle particle.aijIt (x) is information interchange weight between unmanned plane individual, also referred to as correlation function relies on
In the relative position of individual i and individual j, for describing the interaction strength between two unmanned planes.Due in classical CS model
Formation, repulsive force and destination driving force are not accounted for, therefore unmanned plane cluster can't be fully described using classics CS model
Kinetic model.For the unmanned plane cluster of design, using which kind of communication pattern, which kind of level is hardware technological level reach,
Can guarantee unmanned plane cluster have good anti-interference ability, in the cluster in the case where a part of unmanned plane disabler whether
Task can also be completed, the most concerned problem of unmanned plane cluster user is become.Therefore the self-organizing power from unmanned plane cluster is needed
Angle is learned, overall performance analysis such as vulnerability analysis is carried out to the unmanned plane cluster for forming the execution task formed into columns, so as to
Ensure efficiency and success rate that task is completed.
Summary of the invention
The technical problem to be solved by the present invention is to how carry out fragility to the unmanned plane cluster formation and performance that form formation
Property analysis, it is ensured that task completes efficiency and success rate, provides the assessment of a kind of unmanned plane cluster formation and performance fragility
Method and system.
To solve this problem, the technical scheme adopted by the invention is that:
A kind of appraisal procedure of unmanned plane cluster formation fragility, comprising the following steps:
Step 1: obtain unmanned plane cluster information, including unmanned plane cluster initial number, formation mode, spatial position with
And Communication topology；
Step 2: according to the information of acquired unmanned plane cluster, constructing the Controlling model of improved unmanned plane cluster；
Wherein:
Wherein: xi(t) and viIt (t) is respectively position and speed of i-th of unmanned plane in t moment, constant α is used to measure group
Interaction size in body between particle, aij(x) be unmanned plane individual between information interchange weight, dependent on unmanned plane individual
Relative position between i and individual j, for describing the interaction strength between two unmanned planes.bijIndicate cluster evolution process
Repulsion force coefficient between middle unmanned plane, C indicate a distance constant, when the distance between unmanned plane be less than the value, then they
Between there are repulsive forces.N indicates unmanned plane number, | | | | indicate 2 norms of vector, ε is a normal number, and value is usually small
In 0.5, F (xi) it is formation driving force, β and γ are respectively repulsive force and destination driving force, and θ is unmanned plane individual speed direction
With the direction of destination angular separation angular bisector, value is [0, π], and β and γ are that constant indicates system to this two dependences
Degree usually takes respectivelyWith 1.
Step 3: in conjunction with the improvement Controlling model of the unmanned plane cluster of step 2, according to formation driving force F (xi) specific shape
The formation valuation functions of formula building cluster:
Wherein Exp indicates that natural number e is the power function at bottom,Indicate the ideal position of the i-th frame unmanned plane t moment, it is ideal
Position is determined according to the formation mode of cluster by model (2)-(4)；The formula is only applicable to unmanned plane cluster, and only one is logical
The scene of letter connection subset, this communication connection subset is exactly cluster entirety at this time, if cluster has multiple communication connection subsets,
Then the formation assessment f of cluster entirety is calculated by following (6) formula:
Wherein n* indicates the number of connection subset, kiIndicate the ratio of i-th of connection total unmanned plane number of subset unmanned plane number Zhan
Example, fiIt indicates i-th of connection subset formation assessment of cluster, is calculated by formula (5), the N in formula (5) is equal to the connection at this time
Unmanned plane number in subset.The set that the communication connection subset is made of the unmanned plane that can be in communication with each other,
Step 4: the fragility of following formula computing cluster formation reply interference is passed through according to the information of acquisition；
Rf=(fh-fmin)(fh-fl) (7)
Wherein fhCluster formation assessed value before indicating interference, fminIndicate cluster be interfered after formation assessed value most
Small value, flIndicate cluster be interfered stablize after formation assessed value, fh、fmin、flIt is calculated by formula (5) and (6).
Consideration unmanned plane cluster formation mode is circular case, at this point, the formation in step 2 drives force function are as follows:
Wherein R is round formation convergence radius,In geometry for the unmanned plane individual in the same communication connection subset
The heart, the set that the communication connection subset is made of the unmanned plane that can be in communication with each other.
Similarly, when unmanned plane cluster formation mode is round, valuation functions are as follows:
Formation vulnerability assessment is carried out according to unmanned plane cluster formation vulnerability assessment method the present invention also provides a kind of
System, comprise the following modules:
Cluster information obtain module: by radar obtain unmanned plane cluster information, including unmanned plane cluster initial number,
Formation mode, spatial position and Communication topology；
Clustered control module: unmanned plane cluster information acquired in module is obtained by cluster information, constructs unmanned plane
The Controlling model of cluster；
Cluster formation evaluation module: the unmanned plane clustered control model exported according to clustered control module, to cluster formation
Assessment, and export assessed value；
Formation fragility computing module: module is obtained by cluster information for different formation interference and obtains cluster
Spatial positional information and Communication topology calculate the formation assessed value under disturbance, then calculate the fragile journey of cluster
It spends and exports.
Performance vulnerability assessment is carried out using unmanned plane cluster formation vulnerability assessment method the present invention also provides a kind of
Method, comprising the following steps:
S1: the vulnerability analysis factor of a communication connection subset is constructed according to formation valuation functions (5) formula:
wi=ckifi (10)
Wherein k is that the quantity of unmanned plane in communication network connection subset accounts for the ratio of unmanned plane quantity in unmanned plane cluster, c
For constant, indicate that the cluster of analysis factor influences intensity；
S2: the performance function of cluster one connection subset is constructed according to the vulnerability analysis factor；
Wherein η is the Performance Strength of cluster, controls the overall variation of clustering performance；The performance function of cluster entirety then can table
It is shown as:
Wherein n*Indicate the quantity of connection subset.
S3: unmanned plane cluster is calculated to the performance fragility of interference by following (13) formula:
Rp=(ph-pmin)(ph-pl) (13)
Wherein: phClustering performance value before indicating interference, pminAfter indicating that cluster is interfered, the minimum of clustering performance value
Value, plIndicate cluster be interfered stablize after performance number.ph、pmin、plIt is calculated by formula (11), (12).
Performance vulnerability assessment is carried out using unmanned plane clustering performance vulnerability assessment method the present invention also provides a kind of
System, comprise the following modules:
Cluster information obtain module: by radar obtain unmanned plane cluster information, including unmanned plane cluster initial number,
Formation mode, spatial position and Communication topology；
Clustered control module: unmanned plane cluster information acquired in module is obtained by cluster information, constructs unmanned plane
The Controlling model of cluster；
Cluster formation evaluation module: the unmanned plane clustered control model exported according to clustered control module, to cluster formation
Assessment, and export assessed value；
Clustering performance computing module: the formation assessed value computing cluster performance exported according to cluster formation evaluation module is crisp
Weak property analysis factor, then according to clustering performance function computing cluster performance number；
Clustering performance fragility computing module: different interference is inputted by cluster information and obtains module acquisition cluster
Spatial position and Communication topology, then by the calculated clustering performance value of above-mentioned each module institute, according to fragility
Function is calculated to calculate the performance fragility under disturbance and export.
Compared with prior art, beneficial effect obtained by the present invention is:
The present invention provides a kind of unmanned plane cluster formation and performance vulnerability assessment method and system, by obtaining nobody
The formation information and Communication topology of machine cluster are connected to subset structure, based on classics CS model constructions it is improved nobody
Machine group system kinetic model, and then formation valuation functions and performance are constructed according to the concrete form of formation driving force
Then valuation functions calculate unmanned plane cluster formation and performance fragility by providing different interference, and then obtain collection
The vulnerability assessment of group, the present invention keep degree and cluster property retention degree to embody cluster to different dry by quantization formation
The extent of reaction disturbed, to targetedly be improved to clustered node in clustering design, to improve unmanned plane cluster
Task completes efficiency.
Detailed description of the invention
Fig. 1 is cluster formation Vulnerability Evaluation System figure；
Fig. 2 is clustering performance Vulnerability Evaluation System figure；
Fig. 3 is the communication topology network of unmanned plane cluster in example；
Fig. 4 is unmanned plane cluster formation assessment under the backbone node failure conditions of different number；
Fig. 5 is unmanned plane clustering performance assessment under the backbone node failure conditions of different number；
Fig. 6 is unmanned plane cluster formation assessment under the backbone node failure conditions of different location；
Fig. 7 is unmanned plane clustering performance assessment under the backbone node failure conditions of different location.
Specific embodiment
Fig. 1 to Fig. 7 shows the specific of the appraisal procedure of a kind of unmanned plane cluster formation of the present invention and performance fragility
Embodiment.The present embodiment is illustrated using unmanned plane cluster formation mode as circle, specifically includes the following steps:
A kind of appraisal procedure of unmanned plane cluster formation fragility, comprising the following steps:
Step 1: obtain unmanned plane cluster information, including unmanned plane cluster initial number, formation mode, spatial position with
And Communication topology；
Step 2: according to the information of acquired unmanned plane cluster, constructing the Controlling model of unmanned plane cluster；
Wherein:
Wherein: xi(t) and viIt (t) is respectively position and speed of i-th of unmanned plane in t moment, constant α is used to measure group
Interaction size in body between particle, aij(x) be unmanned plane individual between information interchange weight, dependent on unmanned plane individual
Relative position between i and individual j, for describing the interaction strength between two unmanned planes.bijIndicate cluster evolution process
Repulsion force coefficient between middle unmanned plane, C indicate a distance constant, when the distance between unmanned plane be less than the value, then they
Between there are repulsive forces.N indicates unmanned plane number, | | | | indicate 2 norms of vector, ε is a normal number, and value is usually small
In 0.5, F (xi) it is formation driving force, β and γ are respectively repulsive force and destination driving force, and θ is unmanned plane individual speed direction
With the direction of destination angular separation angular bisector, value is [0, π], and β and γ are that constant indicates system to this two dependences
Degree usually takes respectivelyWith 1,It is described logical for the geometric center of the unmanned plane individual in the same communication connection subset
The set that letter connection subset is made of the unmanned plane that can be in communication with each other, when only one communication connection subset in cluster
It waits, entire cluster one formation；There are when multiple disconnected communication connection subsets after cluster is interfered, every height
Collect one formation of each self-forming to form into columns, therefore hereIt is described as in the geometry of unmanned plane individual of the same communication connection subset
The heart because the individual in cluster could cooperate with when only there is information interchange, be only connected to according to place the geometric center of subset come
Determine driving force.
Step 3: in conjunction with the improvement Controlling model of the unmanned plane cluster of step 2, according to F (xi) concrete form construct cluster
Formation valuation functions:
Wherein Exp is indicated using natural number e as the power function at bottom,It indicates the ideal position of the i-th frame unmanned plane t moment, manages
Think that position is determined according to the formation mode of cluster by clustered control model (2)-(4)；
In the present embodiment, as shown in figure 3, unmanned plane cluster formation mode is circle, then using round formation valuation functions:
Wherein Abs indicates absolute value；It can certainly be assessed using formula (5), only use formula (9) specific aim
By force, accuracy is some higher.
When cluster has multiple connection subsets, the formation assessment f of cluster entirety is calculated by following (6) formula:
Wherein n* indicates connection subset quantity, kiIndicate connection subset ratio, fiIndicate that the formation of a connection subset is commented
Estimate, calculated by above-mentioned (9) formula, the N in (9) indicates the unmanned plane number in connection subset at this time.
Step 4: passing through the fragility of following formula computing cluster formation reply interference；
Rf=(fh-fmin)(fh-fl) (7)
Wherein fhCluster formation assessed value before indicating interference, fminIndicate cluster be interfered after formation assessed value most
Small value, flIndicate cluster be interfered stablize after formation assessed value, fmin、flValue be after cluster is interfered, according to collection
The spatial position of group and Communication topology information use formula (5) or formula (6) assessed value calculated, because the present embodiment uses
Round formation, so f in the present embodimentmin、flIt is calculated using formula (9) and formula (6).
Assessment of this example also directed to unmanned plane clustering performance fragility, comprising the following steps:
S1: the vulnerability analysis factor of one communication connection subset of cluster is constructed according to formation valuation functions:
wi=ckifi (10)
Wherein kiThe ratio of unmanned plane quantity in unmanned plane cluster is accounted for for the quantity that communication network is connected to unmanned plane in subset,
K when only one connection subseti=1, c are that constant indicates that cluster influences intensity, and c takes 1 in the present embodiment, but can be according to data
The reasonability of statistics or final vulnerability assessment result is adjusted；
S2: the performance function of cluster one connection subset is constructed according to the vulnerability analysis factor；
Wherein η is the Performance Strength of cluster, controls the overall variation of clustering performance；The performance of cluster entirety passes through following formula meter
It calculates:
S3: according to formation valuation functions building interference to the fragility function of unmanned plane clustering performance；
Rp=(ph-pmin)(ph-pl) (13)
Wherein: phClustering performance value before indicating interference, pminAfter indicating that cluster is interfered, the minimum of clustering performance value
Value, plIndicate cluster be interfered stablize after performance number.pmin、plCluster information after being interfered according to cluster uses formula
(11), (12) are calculated.
Formation vulnerability assessment is carried out according to unmanned plane cluster formation vulnerability assessment method the present invention also provides a kind of
System, as shown in Figure 1, comprising the following modules:
Cluster information obtain module: by radar obtain unmanned plane cluster information, including unmanned plane cluster initial number,
Formation mode, spatial position and Communication topology；
Clustered control module: unmanned plane cluster information acquired in module is obtained by cluster information, constructs unmanned plane
The Controlling model of cluster；
Cluster formation evaluation module: the unmanned plane clustered control model exported according to clustered control module, to cluster formation
Assessment, and export assessed value；
Formation fragility computing module: module is obtained by cluster information for different formation interference and obtains cluster
Spatial positional information and Communication topology calculate the formation assessed value under disturbance, then calculate the fragile journey of cluster
It spends and exports.
Performance vulnerability assessment is carried out using unmanned plane clustering performance vulnerability assessment method the present invention also provides a kind of
System, as shown in Fig. 2, comprising the following modules:
Cluster information obtain module: by radar obtain unmanned plane cluster information, including unmanned plane cluster initial number,
Formation mode, spatial position and Communication topology；
Clustered control module: unmanned plane cluster information acquired in module is obtained by cluster information, constructs unmanned plane
The Controlling model of cluster；
Cluster formation evaluation module: the unmanned plane clustered control model exported according to clustered control module, to cluster formation
Assessment, and export assessed value；
Clustering performance computing module: the formation assessed value computing cluster performance exported according to cluster formation evaluation module is crisp
Weak property analysis factor, then according to clustering performance function computing cluster performance number；
Clustering performance fragility computing module: different interference is inputted by cluster information and obtains module acquisition cluster
Spatial position and Communication topology, then by the calculated clustering performance value of above-mentioned each module institute, according to fragility
Function is calculated to calculate the performance fragility under disturbance and export.
The interference that 2 backbone, 4 backbone, 6 backbone nodes fail is implemented to unmanned plane cluster, passes through the equipment such as radar
Obtain the spatial position after cluster is interfered, communication network is connected to number of subsets information.Then pass through the above method and be
Statistics calculates the formation fragility value and performance fragility value in the case of disturbance, and carries out with interference i.e. 0 backbone failure is not carried out
Compare, the results are shown in Table 1.Fig. 4, Fig. 5 give under the backbone node failure conditions of different number, and unmanned plane cluster formation is commented
Estimate and Performance Evaluation curve.
Cluster formation and performance fragility value after the backbone node failure of 1. different number of table
RfAnd RpIt is more fragile to the interference to be worth bigger expression cluster.From calculated result it can be seen that backbone node failure number is got over
More, unmanned plane cluster formation and performance are poorer.A possibility that completion task, is smaller.
Below by unmanned plane cluster implement 2 be connected, 2 dispersion, 4 be connected, 4 dispersion etc. different locations bone
The interference of dry node failure calculates unmanned plane cluster formation and Performance Evaluation situation, by the equipment such as radar obtain cluster by
Spatial position, communication network after interference are connected to number of subsets information.Then disturbance situation is calculated by the above method
Under formation fragility value and performance fragility value, and be compared with interference i.e. 0 backbone being not carried out failing, the results are shown in Table 2.
Fig. 6, Fig. 7 give under the backbone node failure conditions of different location, the curve of the assessment of unmanned plane cluster formation and Performance Evaluation
Figure.
The backbone node failure interference cluster formation of 2 different location of table and the fragile value of performance
It can be seen that from Tables 1 and 2, as the backbone node of failure increases and more dispersed, cluster formation and performance
Fragility value it is bigger, indicate cluster it is stronger to this disturbing reaction, interfere the influence of generation also bigger.Also illustrate simultaneously
, backbone node is exactly the tender spots of cluster, therefore should consider that backbone node increases anti-interference ability in the design of cluster,
It is just more conducive to cluster in this way and completes set task.
It can be seen that from Fig. 4 and Fig. 5, as the unmanned plane backbone number of failure increases, the formation assessment of cluster and performance
Sharply decline, illustrates that communication backbone executes task to unmanned plane cluster and has great influence.It can be seen that from Fig. 6 and Fig. 7, work as failure
UAV Communication backbone it is more dispersed when, the formation assessment and performance decline of cluster are more, illustrate nobody under this mode
The anti-ability interfered in many ways of a group of planes is weaker.Generally speaking, this communication pattern is more fragile, and the success of task is to communication backbone
Anti-interference ability have very strong dependence.
The appraisal procedure and system of unmanned plane cluster formation and performance fragility provided by the present invention, by obtaining nothing
The formation information and Communication topology of man-machine cluster communication connection subset structure in other words, according to improved unmanned plane cluster
Controlling model calculates out unmanned plane cluster team under different disturbed conditions using formation valuation functions and Performance Evaluation function
Shape and performance fragility embody cluster by quantifying to the response situation of disturbance, thus in clustering design Shi Youzhen
To being improved to clustered node for property, improves unmanned plane cluster task and complete efficiency.
The above is only the preferred embodiment of the present invention, protection scope of the present invention is not limited merely to above-described embodiment,
All technical solutions belonged under thinking of the present invention all belong to the scope of protection of the present invention.It should be pointed out that for the art
For those of ordinary skill, several improvements and modifications without departing from the principles of the present invention should be regarded as protection of the invention
Range.
Claims (6)
1. a kind of appraisal procedure of unmanned plane cluster formation fragility, it is characterised in that: the following steps are included:
Step 1: obtaining the information of unmanned plane cluster, including unmanned plane cluster initial number, formation mode, spatial position and logical
Believe topological structure；
Step 2: according to the information of acquired unmanned plane cluster, constructing the Controlling model of improved unmanned plane cluster；
Wherein
Wherein: xi(t) and viIt (t) is respectively position and speed of i-th of unmanned plane in t moment, constant α is used to measure in group
Interaction size between particle, aij(x) be information interchange weight between unmanned plane individual, dependent on unmanned plane individual i and
Relative position between individual j, for describing the interaction strength between two unmanned planes, N indicates unmanned plane number, bijIt indicates
Repulsion force coefficient during cluster evolution between unmanned plane, C indicates a distance constant, when the distance between unmanned plane is less than
The value, then between them there are repulsive force, | | | | indicate 2 norms of vector, ε is a positive constant, and value is usually less than
0.5, F (xi) be formation driving force, β and γ are respectively repulsive force and destination driving force, θ be unmanned plane individual speed direction with
The direction of destination angular separation angular bisector, value are [0, π], and β and γ are that constant indicates system to this two dependence journeys
Degree, usually takes respectivelyWith 1.
Step 3: according to the Controlling model of unmanned plane cluster improved in step 2 and formation driving force F (xi), construct cluster
Formation valuation functions:
Wherein Exp is indicated using natural number e as the power function at bottom,Indicate the ideal position of the i-th frame unmanned plane t moment, ideal bit
It sets and is determined according to the formation mode of cluster by model (2)-(4)；
When cluster has multiple communication connection subsets, the collection that the communication connection subset is made of the unmanned plane that can be in communication with each other
It closes, then the formation valuation functions f of cluster entirety is calculated by formula (6):
Wherein n* indicates the number of connection subset, kiIndicate the ratio of i-th of connection total unmanned plane number of subset unmanned plane number Zhan, fi
It indicates i-th of connection subset formation assessment of cluster, is calculated by (5), the N in (5) is equal to nobody in the connection subset at this time
Machine number；
Step 4: the fragility of unmanned plane cluster formation reply interference is calculated according to formation valuation functions；
Rf=(fh-fmin)(fh-fl) (7)
fhCluster formation assessed value before indicating interference, fminIndicate the minimum value of formation assessed value after cluster is interfered, flTable
Show cluster be interfered stablize after formation assessed value, fh、fmin、flBy in step 3 formula (5) and formula (6) be calculated.
2. the appraisal procedure of unmanned plane cluster formation fragility according to claim 1, it is characterised in that: when unmanned plane collection
When group's formation mode is round, formation driving force function in step 2 are as follows:
Wherein R is round formation convergence radius,For the geometric center of the unmanned plane individual in the same communication connection subset.
3. the appraisal procedure of unmanned plane cluster formation fragility according to claim 2, it is characterised in that: when unmanned plane collection
When group's formation mode is round, valuation functions:
4. a kind of carry out formation fragility using the described in any item unmanned plane cluster formation vulnerability assessment methods of claim 1-3
Property assessment system, it is characterised in that: comprise the following modules:
Cluster information obtains module: the information of unmanned plane cluster, including unmanned plane cluster initial number, formation are obtained by radar
Mode, spatial position and Communication topology；
Clustered control module: unmanned plane cluster information acquired in module is obtained by cluster information, constructs unmanned plane cluster
Controlling model；
Cluster formation evaluation module: the unmanned plane clustered control model exported according to clustered control module assesses cluster formation,
And export assessed value；
Formation fragility computing module: module is obtained by cluster information for different formation interference and obtains the space of cluster
Location information and Communication topology calculate the formation assessed value under disturbance, then calculate the fragility of cluster simultaneously
Output.
5. a kind of carry out performance fragility using the described in any item unmanned plane cluster formation vulnerability assessment methods of claim 1-3
Property assessment method, it is characterised in that: the following steps are included:
S1: the performance vulnerability analysis factor of one communication connection subset of cluster is constructed according to formation valuation functions:
wi=ckifi (10)
Wherein k is that the quantity of unmanned plane in communication network connection subset accounts for the ratio of unmanned plane quantity in unmanned plane cluster, and c is normal
Number indicates that the cluster of analysis factor influences intensity；
S2: the performance function of a connection subset is constructed according to the performance vulnerability analysis factor；
Wherein η is the Performance Strength of cluster, controls the overall variation of clustering performance；And the performance of cluster entirety then passes through following formula meter
It calculates:
S3: unmanned plane cluster is calculated to the performance fragility of interference by formula (13)；
Rp=(ph-pmin)(ph-pl) (13)
Wherein: phClustering performance value before indicating interference, pminAfter indicating that cluster is interfered, the minimum value of clustering performance value, pl
Indicate cluster be interfered stablize after performance number, ph、pmin、plIt is calculated by (11) and (12) formula.
6. a kind of unmanned plane clustering performance vulnerability assessment method using described in claim 5 carries out performance vulnerability assessment
System comprises the following modules:
Cluster information obtains module: the information of unmanned plane cluster, including unmanned plane cluster initial number, formation are obtained by radar
Mode, spatial position and Communication topology；
Clustered control module: unmanned plane cluster information acquired in module is obtained by cluster information, constructs unmanned plane cluster
Controlling model；
Cluster formation evaluation module: the unmanned plane clustered control model exported according to clustered control module assesses cluster formation,
And export assessed value；
Clustering performance computing module: the formation assessed value computing cluster performance fragility exported according to cluster formation evaluation module
Analysis factor, then according to clustering performance function computing cluster performance number；
Clustering performance fragility computing module: it inputs different interference and the space that module obtains cluster is obtained by cluster information
Position and Communication topology calculate then by the calculated clustering performance value of above-mentioned each module institute according to fragility
Function calculates the performance fragility under disturbance and exports.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910077285.1A CN109582040B (en) | 2019-01-25 | 2019-01-25 | Unmanned aerial vehicle cluster formation and performance vulnerability assessment method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910077285.1A CN109582040B (en) | 2019-01-25 | 2019-01-25 | Unmanned aerial vehicle cluster formation and performance vulnerability assessment method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109582040A true CN109582040A (en) | 2019-04-05 |
| CN109582040B CN109582040B (en) | 2021-08-06 |
Family
ID=65918198
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910077285.1A Active CN109582040B (en) | 2019-01-25 | 2019-01-25 | Unmanned aerial vehicle cluster formation and performance vulnerability assessment method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109582040B (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110162093A (en) * | 2019-06-13 | 2019-08-23 | 中国人民解放军军事科学院国防科技创新研究院 | A kind of high-adaptability formation control method based on geometric similarity design |
| CN110661667A (en) * | 2019-09-29 | 2020-01-07 | 北京航空航天大学 | Task reliability analysis method and system for unmanned aerial vehicle swarm |
| CN110673651A (en) * | 2019-12-05 | 2020-01-10 | 北京航空航天大学 | Robust formation method for unmanned aerial vehicle cluster under limited communication condition |
| CN110969724A (en) * | 2019-12-23 | 2020-04-07 | 拓攻（南京）机器人有限公司 | Method, device and equipment for analyzing flight performance of unmanned aerial vehicle and storage medium |
| CN111190433A (en) * | 2019-12-16 | 2020-05-22 | 北京泊松技术有限公司 | Unmanned aerial vehicle cluster confrontation competition judging method |
| CN112154346A (en) * | 2019-10-09 | 2020-12-29 | 深圳市大疆创新科技有限公司 | Radar anti-interference method, equipment, system and storage medium |
| CN112527014A (en) * | 2020-12-02 | 2021-03-19 | 电子科技大学 | Unmanned aerial vehicle cluster grazing method based on packing algorithm |
| CN112801160A (en) * | 2021-01-22 | 2021-05-14 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle formation vulnerability analysis method and system based on honeycomb graph model |
| CN113625697A (en) * | 2021-09-15 | 2021-11-09 | 北京航空航天大学 | Unmanned aerial vehicle cluster reliability assessment method and system considering task capability change |
| WO2023010712A1 (en) * | 2021-08-06 | 2023-02-09 | 浙江大学 | Optimization method and device for communication network of aerial swarm |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2161297A1 (en) * | 2008-09-08 | 2010-03-10 | Shin-Etsu Chemical Co., Ltd. | Method for preparing a primer composition and coated product |
| US20100163621A1 (en) * | 2006-01-11 | 2010-07-01 | Carmel-Haifa University Economic Corporation Ltd. | Uav decision and control system |
| CN103995539A (en) * | 2014-05-15 | 2014-08-20 | 北京航空航天大学 | Unmanned aerial vehicle autonomous formation evaluation index and MPC formation control method |
| RU2583234C1 (en) * | 2014-12-31 | 2016-05-10 | Открытое акционерное общество "Государственный научно-исследовательский навигационно-гидрографический институт" (ОАО "ГНИНГИ") | System for illuminating ice conditions and preventing effect of ice formations on sea objects of business operations |
| US20170039861A1 (en) * | 2015-08-04 | 2017-02-09 | Qualcomm Incorporated | Opportunistic Unmanned Autonomous Vehicle Energy Harvesting |
| CN106656300A (en) * | 2016-12-21 | 2017-05-10 | 中国航天时代电子公司 | UAV cluster combat system utilizing ad-hoc network data chain |
| CN106843269A (en) * | 2017-01-22 | 2017-06-13 | 北京航空航天大学 | A kind of unmanned plane formation method based on small birds cluster fly mechanics |
| US20170346679A1 (en) * | 2016-05-27 | 2017-11-30 | Hefei University Of Technology | Method and System for Reconstructing Unmanned Aerial Vehicle Formation Communication Topology Based on Minimum Cost Arborescence |
| CN107728643A (en) * | 2017-11-10 | 2018-02-23 | 西安电子科技大学 | A kind of unmanned aerial vehicle group distributed task dispatching method under dynamic environment |
| US9990854B1 (en) * | 2016-03-15 | 2018-06-05 | Rockwell Collins, Inc. | Unmanned aerial system mission flight representation conversion techniques and traffic management scheme |
| CN108170162A (en) * | 2017-12-30 | 2018-06-15 | 天津职业技术师范大学 | Multiple dimensioned wind disturbs analysis unmanned plane group of planes coordinated control system performance estimating method |
| GB2559291A (en) * | 2016-01-25 | 2018-08-01 | Above Surveying Ltd | UVAs for detecting defects in solar panel arrays |
| CN108680063A (en) * | 2018-05-23 | 2018-10-19 | 南京航空航天大学 | A kind of decision-making technique for the dynamic confrontation of extensive unmanned plane cluster |
| CN108768745A (en) * | 2018-06-14 | 2018-11-06 | 北京航空航天大学 | A kind of group system brittleness assessment method based on complex network |
| CN108959072A (en) * | 2018-06-14 | 2018-12-07 | 北京航空航天大学 | A kind of group system elasticity assessment method based on complex network |
| CN109104737A (en) * | 2018-07-31 | 2018-12-28 | 北京航空航天大学 | A kind of cluster antagonism appraisal procedure based on time-varying network |
| CN109254592A (en) * | 2018-09-17 | 2019-01-22 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle formation change control method and system |
-
2019
- 2019-01-25 CN CN201910077285.1A patent/CN109582040B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100163621A1 (en) * | 2006-01-11 | 2010-07-01 | Carmel-Haifa University Economic Corporation Ltd. | Uav decision and control system |
| EP2161297A1 (en) * | 2008-09-08 | 2010-03-10 | Shin-Etsu Chemical Co., Ltd. | Method for preparing a primer composition and coated product |
| CN103995539A (en) * | 2014-05-15 | 2014-08-20 | 北京航空航天大学 | Unmanned aerial vehicle autonomous formation evaluation index and MPC formation control method |
| RU2583234C1 (en) * | 2014-12-31 | 2016-05-10 | Открытое акционерное общество "Государственный научно-исследовательский навигационно-гидрографический институт" (ОАО "ГНИНГИ") | System for illuminating ice conditions and preventing effect of ice formations on sea objects of business operations |
| US20170039861A1 (en) * | 2015-08-04 | 2017-02-09 | Qualcomm Incorporated | Opportunistic Unmanned Autonomous Vehicle Energy Harvesting |
| GB2559291A (en) * | 2016-01-25 | 2018-08-01 | Above Surveying Ltd | UVAs for detecting defects in solar panel arrays |
| US9990854B1 (en) * | 2016-03-15 | 2018-06-05 | Rockwell Collins, Inc. | Unmanned aerial system mission flight representation conversion techniques and traffic management scheme |
| US20170346679A1 (en) * | 2016-05-27 | 2017-11-30 | Hefei University Of Technology | Method and System for Reconstructing Unmanned Aerial Vehicle Formation Communication Topology Based on Minimum Cost Arborescence |
| CN106656300A (en) * | 2016-12-21 | 2017-05-10 | 中国航天时代电子公司 | UAV cluster combat system utilizing ad-hoc network data chain |
| CN106843269A (en) * | 2017-01-22 | 2017-06-13 | 北京航空航天大学 | A kind of unmanned plane formation method based on small birds cluster fly mechanics |
| CN107728643A (en) * | 2017-11-10 | 2018-02-23 | 西安电子科技大学 | A kind of unmanned aerial vehicle group distributed task dispatching method under dynamic environment |
| CN108170162A (en) * | 2017-12-30 | 2018-06-15 | 天津职业技术师范大学 | Multiple dimensioned wind disturbs analysis unmanned plane group of planes coordinated control system performance estimating method |
| CN108680063A (en) * | 2018-05-23 | 2018-10-19 | 南京航空航天大学 | A kind of decision-making technique for the dynamic confrontation of extensive unmanned plane cluster |
| CN108768745A (en) * | 2018-06-14 | 2018-11-06 | 北京航空航天大学 | A kind of group system brittleness assessment method based on complex network |
| CN108959072A (en) * | 2018-06-14 | 2018-12-07 | 北京航空航天大学 | A kind of group system elasticity assessment method based on complex network |
| CN109104737A (en) * | 2018-07-31 | 2018-12-28 | 北京航空航天大学 | A kind of cluster antagonism appraisal procedure based on time-varying network |
| CN109254592A (en) * | 2018-09-17 | 2019-01-22 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle formation change control method and system |
Non-Patent Citations (4)
| Title |
|---|
| HUAXIN QIU 等: "Receding horizon control for multiple UAV formation flight based on modified brain storm optimization", 《NONLINEAR DYNAMICS》 * |
| WILSON O. CAUESADA 等: "Leader-Follower Formation for UAV Robot Swarm Based on Fuzzy Logic Theory", 《SPRINGER INTERNATIONAL PUBLISHING; AG, PART OF SPRINGER NATURE》 * |
| 吴静: "多无人机协同航迹规划及效能评估方法研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
| 韩博文 等: "基于QABC-IFMADM算法的有人/无人机编队作战威胁评估", 《电子学报》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110162093A (en) * | 2019-06-13 | 2019-08-23 | 中国人民解放军军事科学院国防科技创新研究院 | A kind of high-adaptability formation control method based on geometric similarity design |
| CN110162093B (en) * | 2019-06-13 | 2022-02-11 | 中国人民解放军军事科学院国防科技创新研究院 | High-adaptability formation control method based on geometric similarity design |
| CN110661667A (en) * | 2019-09-29 | 2020-01-07 | 北京航空航天大学 | Task reliability analysis method and system for unmanned aerial vehicle swarm |
| CN110661667B (en) * | 2019-09-29 | 2020-07-03 | 北京航空航天大学 | Task reliability analysis method and system for unmanned aerial vehicle swarm |
| WO2021068136A1 (en) * | 2019-10-09 | 2021-04-15 | 深圳市大疆创新科技有限公司 | Radar anti-jamming method, device, and system, and storage medium |
| CN112154346A (en) * | 2019-10-09 | 2020-12-29 | 深圳市大疆创新科技有限公司 | Radar anti-interference method, equipment, system and storage medium |
| CN110673651A (en) * | 2019-12-05 | 2020-01-10 | 北京航空航天大学 | Robust formation method for unmanned aerial vehicle cluster under limited communication condition |
| CN111190433A (en) * | 2019-12-16 | 2020-05-22 | 北京泊松技术有限公司 | Unmanned aerial vehicle cluster confrontation competition judging method |
| CN110969724A (en) * | 2019-12-23 | 2020-04-07 | 拓攻（南京）机器人有限公司 | Method, device and equipment for analyzing flight performance of unmanned aerial vehicle and storage medium |
| CN112527014A (en) * | 2020-12-02 | 2021-03-19 | 电子科技大学 | Unmanned aerial vehicle cluster grazing method based on packing algorithm |
| CN112527014B (en) * | 2020-12-02 | 2022-05-17 | 电子科技大学 | Unmanned aerial vehicle cluster grazing method based on packing algorithm |
| CN112801160A (en) * | 2021-01-22 | 2021-05-14 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle formation vulnerability analysis method and system based on honeycomb graph model |
| CN112801160B (en) * | 2021-01-22 | 2022-09-06 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle formation vulnerability analysis method and system based on honeycomb graph model |
| WO2023010712A1 (en) * | 2021-08-06 | 2023-02-09 | 浙江大学 | Optimization method and device for communication network of aerial swarm |
| CN113625697A (en) * | 2021-09-15 | 2021-11-09 | 北京航空航天大学 | Unmanned aerial vehicle cluster reliability assessment method and system considering task capability change |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109582040B (en) | 2021-08-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109582040A (en) | Unmanned aerial vehicle cluster formation and performance vulnerability assessment method and system | |
| Dong et al. | An Intrusion Detection Model for Wireless Sensor Network Based on Information Gain Ratio and Bagging Algorithm. | |
| Deng et al. | Collective behavior of a small-world recurrent neural system with scale-free distribution | |
| Zhao et al. | Local similarity imputation based on fast clustering for incomplete data in cyber-physical systems | |
| Wu et al. | Autonomous cooperative flocking for heterogeneous unmanned aerial vehicle group | |
| CN103077347A (en) | Combined type intrusion detecting method on basis of data fusion of improved core vector machine | |
| Du et al. | LTrust: An adaptive trust model based on LSTM for underwater acoustic sensor networks | |
| Mohammed et al. | Edge computing intelligence using robust feature selection for network traffic classification in internet-of-things | |
| Zhu et al. | Three Kinds of Network Security Situation Awareness Model Based on Big Data. | |
| Qiu et al. | Born this way: A self-organizing evolution scheme with motif for internet of things robustness | |
| CN103401626A (en) | Genetic algorithm based cooperative spectrum sensing optimization method | |
| Ruiwen et al. | Improved method for subsystems performance trade-off in system-of-systems oriented design of UAV swarms | |
| Luo | An Emergency Management System for Government Data Security Based on Artificial Intelligence. | |
| Li et al. | Few-shot IoT attack detection based on RFP-CNN and adversarial unsupervised domain-adaptive regularization | |
| Li et al. | Application of velocity adaptive shuffled frog leaping bat algorithm in ICS intrusion detection | |
| Wang et al. | [Retracted] An Extraction Method of Volleyball Spiking Trajectory and Teaching Based on Wireless Sensor Network | |
| Jin et al. | A Hierarchical clustering community algorithm which missed the signal in the process of transmission | |
| CN110224782A (en) | A kind of unmanned plane formation secure communication of network method and system based on active interference | |
| Yang et al. | Flocking shape analysis of multi-agent systems | |
| CN106033432A (en) | A decomposition strategy-based multi-class disequilibrium fictitious assets data classifying method | |
| Li et al. | [Retracted] Abnormal Data Detection in Sensor Networks Based on DNN Algorithm and Cluster Analysis | |
| Yan et al. | freeLoc: Wireless-Based Cross-Domain Device-Free Fingerprints Localization to Free User’s Motions | |
| Jiang et al. | Modeling and Resilience Analysis of Combat Systems-of-Systems Based on Kill Web | |
| Mitrentsis et al. | Unsupervised learning method for clustering dynamic behavior in the context of power systems | |
| Bai | Social public opinion communication and network legal management based on artificial intelligence cognitive wireless network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |