CN116972694A - Unmanned plane cluster attack-oriented countering method and system - Google Patents
Unmanned plane cluster attack-oriented countering method and system Download PDFInfo
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Abstract
The invention discloses a countering method and a countering system for unmanned aerial vehicle cluster attack, and belongs to the technical field of unmanned aerial vehicle countering. The method comprises the steps of preprocessing target information detected by radar detection, radio detection, optical detection and other equipment to unify a data structure, and fusing multi-source data by adopting a fusion algorithm to generate comprehensive information of unmanned aerial vehicle cluster targets; combining comprehensive information of unmanned aerial vehicle cluster targets, constructing an unmanned aerial vehicle cluster threat evaluation index system by analyzing factors such as unmanned aerial vehicle type, unmanned aerial vehicle relative speed, unmanned aerial vehicle flight height, distance between an unmanned aerial vehicle and a prevention and control area and the like, and realizing unmanned aerial vehicle cluster threat sequencing by adopting a TOPSIS sequencing method based on entropy weight; finally, according to the threat sequencing result of the unmanned aerial vehicle clusters, a unmanned aerial vehicle cluster countercheck decision method based on countercheck equipment performance is provided, and accurate unmanned aerial vehicle cluster countercheck decision is realized.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle countering, and particularly relates to a countering method and a countering system for unmanned aerial vehicle cluster attack.
Background
With the vigorous development of unmanned aerial vehicles in the directions of agriculture, military, business and the like, the unmanned aerial vehicle is used and is in explosive growth, and further the events of illegal invasion of the unmanned aerial vehicle into important areas for shooting, attack and the like are gradually increased. The current unmanned aerial vehicle attack means are more and more abundant, can adopt unmanned aerial vehicle cluster attack, cluster carry explosive attack, many means such as single frame stealthy shooting reconnaissance, attack to protection objects such as important point target, key area, core personage, especially important value targets such as government, harbour, water conservancy junction, military land etc. become unmanned aerial vehicle attack object more easily.
The traditional unmanned aerial vehicle countering system adopts radar detection to harden and kill and strike or adopts a mode of radio detection to add radio interference and the like aiming at a single illegal unmanned aerial vehicle, and because the clustered unmanned aerial vehicle has the characteristics of a large number, small volume, low flying height, slower speed relative to other aircrafts, strong cooperative capacity and the like, the traditional unmanned aerial vehicle countering method has the problems of blind spot detection, difficulty in reasonably distributing countering targets, overlong countering time and the like when facing the unmanned aerial vehicle cluster attack, and the problems of extremely easy realization of burst prevention of part of unmanned aerial vehicles in the unmanned aerial vehicle cluster, and further investigation or attack on personnel or facilities in a key protection area, and great harm is caused to the social public safety.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a countering method and a countering system for unmanned aerial vehicle cluster attack, and aims to effectively improve the interception treatment success rate and the treatment efficiency of unmanned aerial vehicle clusters.
To achieve the above object, in a first aspect, the present invention provides a countering method for an unmanned aerial vehicle cluster attack, where the method includes:
acquiring detection data of a multisource sensor, preprocessing the detection data, performing space-time alignment, data association, track tracking filtering and target fusion to obtain target state data, performing target recognition by using a standard database and a target recognition feature library, and combining the target state data to form final target state information; the multi-source sensor comprises a radar, a radio detection device and an optical detection device;
constructing an unmanned aerial vehicle cluster threat evaluation index system according to the type of the unmanned aerial vehicle, the relative speed of the unmanned aerial vehicle, the flight height of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and a prevention and control area and the criticality of the unmanned aerial vehicle in a cluster unmanned aerial vehicle communication network, and realizing the threat sequencing of the unmanned aerial vehicle by adopting a TOPSIS sequencing method based on entropy weight;
and performing countercheck decision by combining the threat sequencing result of the unmanned aerial vehicle and the performance parameters of countercheck equipment.
Preferably, the acquiring the detection data of the multi-source sensor specifically includes:
when the radar does not detect the target, the radio detection equipment acquires a radio detection track when detecting the target;
when the radar detects a target and the radio detection equipment does not detect the target, a radar track is acquired;
when the radar and the radio detection equipment find targets at the same time, the radar track is preferentially collected, and meanwhile, the model number and the frequency of the unmanned aerial vehicle identified by the radio detection equipment are collected;
and when the radar and the optical detection equipment detect the target at the same time, detection data of the radar and the optical detection equipment are collected at the same time.
Preferably, the method includes the steps of performing space-time alignment, data association, track tracking filtering and target fusion on the detected data after preprocessing to obtain target state data, and specifically includes the following steps:
performing protocol and format conversion on the detected data to form a unified data format;
judging and eliminating the wild value in the detection data;
compressing the detection data;
performing time calibration and space calibration on the detection data;
aiming at the multi-target detection data acquired by each sensor, carrying out data association according to the accuracy of the sensor and the data dimension based on a track association algorithm, and judging which target the acquired detection data belongs to;
carrying out track tracking filtering on the target track by adopting a unscented Kalman filtering algorithm;
and carrying out fusion processing on the multi-sensor track information of the same target so as to track the target.
Preferably, the unmanned aerial vehicle cluster threat evaluation index system specifically includes:
the method comprises the steps of acquiring the size of the unmanned aerial vehicle based on target state information, and classifying threat levels according to the size of the unmanned aerial vehicle, wherein the smaller the size of the unmanned aerial vehicle is, the higher the threat level is;
acquiring the speed v of the unmanned aerial vehicle relative to the prevention and control area based on the target state information by threat of the relative speed of the unmanned aerial vehicle ′ ：
Wherein v is the unmanned aerial vehicle speed; beta is the angle of the unmanned aerial vehicle relative to the central point of the prevention and control area; alpha is the flight heading of the unmanned aerial vehicle; velocity v ′ The higher the threat level, the higher;
the method comprises the steps of acquiring the flight height of an unmanned aerial vehicle based on target state information, wherein the lower the flight height is, the higher the threat level is;
the threat between the unmanned aerial vehicle and the prevention and control area is obtained, and the connecting distance between the unmanned aerial vehicle and the center point of the prevention and control area is obtained based on the target state information, wherein the smaller the connecting distance is, the higher the threat level is;
cluster unmanned aerial vehicle communication network unmanned aerial vehicle critical threat, and acquiring critical C of unmanned aerial vehicle i in the cluster communication network based on target state information i ：
wherein ,represents the number of shortest paths between the pair of unmanned plane nodes a and b passing through unmanned plane i, U ab Representing the total number of shortest paths between the unmanned plane node pairs a and b;
critical C i The higher the threat level, the higher.
Preferably, the TOPSIS sorting method based on the entropy weight specifically comprises the following steps:
normalizing 5 evaluation indexes in an unmanned aerial vehicle cluster threat evaluation index system;
wherein Index is ij Represents the j-th evaluation index of the ith unmanned aerial vehicle after normalization processing in the cluster, I ij The jth evaluation index of the ith unmanned aerial vehicle in the cluster is represented;
calculating entropy value H of j-th evaluation index according to normalization processing result j And entropy weight w of jth evaluation index j ；
k＝lnm
Constructing a normalized matrix A;
determination of ideal point A + And negative ideal point A - ；
Wherein, the forward index is the index with higher threat as the index value is larger; the negative index is the index with larger index value and smaller threat, and max 1≤i≤n Index ij and min1≤i≤n Index ij Respectively representing the maximum value and the minimum value of the j-th evaluation index in the evaluation area;
calculation A ij Distance to each ideal pointA ij Distance to each negative ideal point +.>
Calculating threat degree sigma of unmanned plane i i ；
Preferably, the countering decision is performed by combining the threat sequencing result of the unmanned aerial vehicle and the performance parameter of the countering equipment, specifically:
according to the threat sequencing result of the unmanned aerial vehicle, the following operations are sequentially carried out on each unmanned aerial vehicle from high threat level to low threat level of the unmanned aerial vehicle:
(1) Let the distance d between the unmanned plane and the prevention and control center point UVA Obtaining the minimum acting distance of each soft-killing reaction deviceMaximum distance of action->If a reaction device i is present, the reaction device i meetsGo to step (2) or else go to step (6);
(2) Distance d combining unmanned aerial vehicle distance prevention and control central point UVA Unmanned aerial vehicle relative flight speed v ′ Calculating time t of unmanned aerial vehicle flying to prevention and control area uva ：
t uva ＝ UVA / ′
(3) Obtaining maximum response time { t } of each device 1 ,……, n If there is a reaction device i satisfyingGo to step (4), otherwise go to step (6);
(4) Acquiring the use state of the countering equipment i, if the equipment is unused, and go to step (5), otherwise go to step (6);
(5) Performing countering decision on countering equipment i meeting the constraints of distance, time and use state according to the sequence of acoustic interference, electromagnetic interference, navigation interference and navigation decoy, and if the unmanned aerial vehicle cannot be countered successfully according to the sequence, turning to the step (6);
(6) The soft killing and countering equipment fails in countering, and the target unmanned aerial vehicle is knocked down by adopting the hard killing and countering equipment, wherein the hard killing and countering equipment comprises: laser, missile and patrol missile.
In a second aspect, the present invention provides a countering system for unmanned cluster attack, the system comprising:
the data acquisition processing module is used for acquiring detection data of the multi-source sensor, preprocessing the detection data, performing space-time alignment, data association, track tracking filtering and target fusion to obtain target state data, performing target recognition by using the standard database and the target recognition feature library, and combining the target state data to form final target state information; the multi-source sensor comprises a radar, a radio detection device and an optical detection device;
the threat ranking module is used for constructing an unmanned aerial vehicle cluster threat evaluation index system according to the type of the unmanned aerial vehicle, the relative speed of the unmanned aerial vehicle, the flight altitude of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and a prevention and control area and the criticality of the unmanned aerial vehicle in a clustered unmanned aerial vehicle communication network, and realizing the threat ranking of the unmanned aerial vehicle by adopting a TOPSIS ranking method based on entropy weight;
and the countercheck decision module is used for combining the threat sequencing result of the unmanned aerial vehicle and the performance parameters of countercheck equipment to make countercheck decisions.
Preferably, in the threat ranking module, an unmanned aerial vehicle cluster threat evaluation index system is constructed by the following units:
the unmanned aerial vehicle type threat unit is used for acquiring the size of the unmanned aerial vehicle based on the target state information, and threat levels are classified according to the size of the unmanned aerial vehicle, and the smaller the size of the unmanned aerial vehicle is, the higher the threat level is;
the unmanned aerial vehicle relative speed threat unit is used for acquiring speed v of the unmanned aerial vehicle relative to the prevention and control area based on the target state information ′ ：
Wherein v is the unmanned aerial vehicle speed; beta is the angle of the unmanned aerial vehicle relative to the central point of the prevention and control area; alpha is the flight heading of the unmanned aerial vehicle; velocity v ′ The higher the threat level, the higher;
the unmanned aerial vehicle flight height threat unit is used for acquiring the unmanned aerial vehicle flight height based on the target state information, wherein the lower the flight height is, the higher the threat level is;
the threat unit is used for acquiring the connecting line distance between the unmanned aerial vehicle and the central point of the prevention and control area based on the target state information, wherein the smaller the connecting line distance is, the higher the threat level is;
the unmanned aerial vehicle critical threat unit of the cluster unmanned aerial vehicle communication network is used for acquiring the critical C of the unmanned aerial vehicle i in the cluster communication network based on the target state information i ：
wherein ,represents the number of shortest paths between the pair of unmanned plane nodes a and b passing through unmanned plane i, U ab Representing the total number of shortest paths between the unmanned plane node pairs a and b;
critical C i The higher the threat level, the higher.
Preferably, in the threat ranking module, the threat ranking of the unmanned aerial vehicle is achieved through the following units sequentially executed:
the normalization unit is used for performing normalization processing on 5 evaluation indexes in the unmanned aerial vehicle cluster threat evaluation index system;
wherein Index is ij Represents the j-th evaluation index of the ith unmanned aerial vehicle after normalization processing in the cluster, I ij The jth evaluation index of the ith unmanned aerial vehicle in the cluster is represented;
an entropy weight calculation unit for calculating the entropy value H of the j-th evaluation index according to the normalization processing result j And entropy weight w of jth evaluation index j ；
The matrix calculation unit is used for constructing a normalized matrix A;
an ideal point calculating unit for determining ideal point A + And negative ideal point A - ；
Wherein, the forward index is the index with higher threat as the index value is larger; the negative index is the index with larger index value and smaller threat, and max 1≤i≤n Index ij and min1≤i≤n Index ij Respectively representing the maximum value and the minimum value of the j-th evaluation index in the evaluation area;
a distance calculating unit for calculating A ij Distance to each ideal pointA ij Distance to each negative ideal point +.>
Threat degree calculating unit for calculating threat degree sigma of unmanned plane i i ；
Preferably, the reaction decision module specifically includes the following units executed in sequence:
according to the threat sequencing result of the unmanned aerial vehicle, the following unit operations are sequentially executed for each unmanned aerial vehicle from high threat level to low threat level of the unmanned aerial vehicle:
a distance judging unit for setting the distance d between the unmanned aerial vehicle and the prevention and control center point UVA Obtaining the minimum acting distance of each soft-killing reaction deviceMaximum distance of action->If a reaction device i is present, it satisfies +.>Then turning to a speed judging unit, otherwise turning to a hard killing and countering unit;
a speed judging unit for combining the distance d between the unmanned aerial vehicle and the prevention and control center point UVA Unmanned aerial vehicle relative flight speed v ′ Calculating time t of unmanned aerial vehicle flying to prevention and control area uva ：
t uva ＝ UVA / ′
A time judging module for obtaining the maximum response time { t } of each device 1 ,……,t n If there is a reaction device i satisfyingThen turning to a state judging unit, otherwise turning to a hard killing and countering unit;
a state judging unit for acquiring the use state of the countering device i, if the device is unused, andturning to a soft kill reaction unit, otherwise turning to a hard kill reaction unit;
the soft killing and countering unit is used for countering the countering equipment i meeting the constraints of distance, time and use state according to the sequence of acoustic interference, electromagnetic interference, navigation interference and navigation decoy, and if the unmanned aerial vehicle cannot be countered successfully according to the sequence, the countering equipment i is switched to the hard killing and countering unit;
the hard killing and countering unit is used for failing the countering of the soft killing and countering equipment, and the target unmanned aerial vehicle is knocked down by adopting the hard killing and countering equipment, and the hard killing and countering equipment comprises: laser, missile and patrol missile.
In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:
(1) The invention provides an unmanned aerial vehicle cluster reconnaissance detection method based on multi-source fusion, which comprises the steps of preprocessing unified data structures of target information detected by radar detection, radio detection, optical detection and other equipment, and fusing multi-source data by adopting a fusion algorithm to generate comprehensive information of a clustered unmanned aerial vehicle target; the method adopts a mode of complementation and fusion of a plurality of detection means, solves the problems of large detection blind area, easy interference, insufficient precision and high error rate of the existing unmanned aerial vehicle cluster detection method, and improves the accuracy of unmanned aerial vehicle cluster recognition;
(2) The invention provides an unmanned aerial vehicle cluster threat ranking method based on multi-factor recognition, which comprehensively considers the flight characteristics of unmanned aerial vehicles, acquires unmanned aerial vehicle recognition information through each detection device, acquires the platform capability of an unmanned aerial vehicle, combines factors influencing the threat degree of the unmanned aerial vehicles, such as the type of the unmanned aerial vehicle, the relative speed of the unmanned aerial vehicle, the flight height of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and a prevention and control area, the criticality of a clustered unmanned aerial vehicle communication network unmanned aerial vehicle and the like, constructs an unmanned aerial vehicle cluster threat evaluation index system, and adopts a TOPSIS ranking method based on entropy weight to realize the threat ranking of the unmanned aerial vehicle clusters; the method comprehensively and comprehensively considers key factors influencing the threat degree of the unmanned aerial vehicle, and can effectively provide reasonable support for the countering decision sequence of the unmanned aerial vehicle in the unmanned aerial vehicle cluster; the method comprises the steps of carrying out a first treatment on the surface of the
(3) The unmanned aerial vehicle group countering decision method based on the countering equipment performance is provided by combining the unmanned aerial vehicle threat sequencing result and the countering equipment performance parameter, and effectively shortens the countering decision time and improves the countering efficiency and the countering power.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the present invention;
fig. 2 is a schematic diagram of an unmanned aerial vehicle cluster reconnaissance detection method based on multi-source fusion in an embodiment of the invention;
FIG. 3 is a schematic diagram of the relative speeds of a drone in an embodiment of the present invention;
fig. 4 is a diagram of an unmanned aerial vehicle threat indicator system in an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
As shown in fig. 1, an embodiment of the present invention includes the steps of:
step one, unmanned aerial vehicle cluster reconnaissance detection method based on multisource fusion:
the primary step of the unmanned aerial vehicle cluster countering method in the key protection area is to detect unmanned aerial vehicle targets in the protection area, and the unmanned aerial vehicle cluster reconnaissance detection method based on multi-source fusion is provided for better realizing reconnaissance and detection of unmanned aerial vehicle clusters near the prevention and control area. The method integrates sensors such as radar, radio detection, photoelectric infrared and the like, and performs cooperative detection on the target. The radio detection equipment has the advantages that the target position can be quickly acquired, the multi-station can realize cross positioning, the target frequency and the model of the unmanned aerial vehicle can be identified, the positioning accuracy is low, and the false alarm rate is high in a complex electromagnetic environment; the radar equipment can realize all-weather all-day active detection, has higher positioning precision, and has the defects that the type of a target cannot be judged and the radar equipment is sensitive to target shielding; the photoelectric tracking device can identify the target image, and has the defect of searching under the guidance of a radar. In summary, each sensor has respective advantages and disadvantages, and for better realizing continuous tracking and accurate identification of the unmanned aerial vehicle target, the sensors are required to work cooperatively, and the advantages are complementary. The unmanned aerial vehicle cluster reconnaissance detection method based on multi-source fusion, as shown in fig. 2, comprises the following specific steps:
1. and (3) information acquisition: and collecting data reported by radar detection equipment, radio detection equipment and optical detection equipment, wherein the data comprises target tracks, points, frequency spectrums and the like.
1) When the radar does not detect the target and the radio detects the target, adopting the radio to detect the track;
2) When the radar detects a target and the radio detection equipment does not detect the target, adopting a radar track;
3) When the radar and the radio detection find the target at the same time, the radar track is preferentially utilized, and the type and the frequency of the unmanned aerial vehicle are identified by adopting the radio detection at the same time;
4) And (2) when the radar and the photoelectric infrared are tracked simultaneously, carrying out association, calculation and fusion on the distance of the radar and the azimuth and the pitching of the photoelectric infrared, and detailed description is given in the step (2).
2. Multi-source data fusion: and classifying and preprocessing the received various target information, and correlating and synthesizing the preprocessed target feature vector, image information and other information.
1) Pretreatment:
A. carrying out protocol conversion on the collected target data to form a unified data format;
B. judging and eliminating the wild value data;
C. the data is compressed to reduce the amount of data transferred.
2) Real-time data fusion
A. Space-time alignment
Because the radar, radio detection, photoelectric infrared and other sensors have different functions, performances and detection principles, the sensor belongs to heterogeneous data, in the fusion positioning process, the time calibration is firstly carried out on targets of all information sources through a time system, and then the space calibration is carried out on detection data of all information members by taking a unified coordinate system as a reference coordinate system. And after the space-time calibration is finished, carrying out protocol and format conversion on the acquired multi-source data, and converting the multi-source data into a uniform data format.
B. Data association
Aiming at the multi-target data information acquired by each sensor, a track association algorithm carries out data association according to the precision of the detector and the data dimension, and judges which target the acquired positioning data belong to.
C. Track following filtering
And adopting an unscented Kalman filtering algorithm to carry out track tracking filtering.
D. Target fusion
After track association and multi-target state estimation are carried out on the data information of each sensor, fusion processing is carried out on the multi-sensor track information of the same target so as to realize tracking of the target.
3. Target comprehensive identification: and comprehensively utilizing the standard database and the target recognition feature library to perform feature level target recognition, performing target comprehensive recognition by utilizing decision rules, and combining the fused target state data to form final target state information.
Step two, an unmanned aerial vehicle cluster threat sequencing method based on multi-factor identification:
according to the unmanned aerial vehicle cluster threat ranking method based on multi-factor recognition, unmanned aerial vehicle cluster flight characteristics are comprehensively considered, unmanned aerial vehicle platform capability is learned through unmanned aerial vehicle recognition information acquired by each detection device, and factors such as unmanned aerial vehicle type, unmanned aerial vehicle relative speed, unmanned aerial vehicle flight height, unmanned aerial vehicle and prevention area distance, unmanned aerial vehicle communication network unmanned aerial vehicle criticality influence the degree of threat of unmanned aerial vehicles are combined to form an unmanned aerial vehicle cluster threat evaluation index system.
The unmanned aerial vehicle is different from a common low-altitude attack target, has the characteristics of strong prominence, difficult detection, simple attack pattern and the like, and when threat assessment is carried out on the unmanned aerial vehicle, various factors influencing the threat degree of the unmanned aerial vehicle need to be comprehensively considered.
1. Unmanned aerial vehicle threat index system construction based on multi-factor identification:
1) Unmanned aerial vehicle type
When unmanned aerial vehicle cluster attack is carried out, a plurality of unmanned aerial vehicles are generally adopted for cluster fight, so that the unmanned aerial vehicle type is used as an important index for influencing the threat degree of the unmanned aerial vehicle. Because the limited information of the unmanned aerial vehicle obtained by the radar, the radio detection and the photoelectric detection equipment is limited, the unmanned aerial vehicle type is divided according to the size of the unmanned aerial vehicle, namely, the unmanned aerial vehicle type is divided into a micro unmanned aerial vehicle, a light unmanned aerial vehicle, a small unmanned aerial vehicle and a large unmanned aerial vehicle, the corresponding grade is 1-4, and the smaller unmanned aerial vehicle is more difficult to find according to the unmanned aerial vehicle detection principle, so that the threat of the unmanned aerial vehicle with smaller size to the prevention and control area is higher.
2) Relative speed of unmanned plane
The relative speed of the unmanned aerial vehicle refers to the speed of the unmanned aerial vehicle relative to the prevention and control area, and the relative speed of the unmanned aerial vehicle directly influences the difficulty and the success rate of unmanned aerial vehicle reaction. In general, the approach direction is positive, the distance direction is negative, and the greater the relative speed of the unmanned aerial vehicle is, the higher the threat of the unmanned aerial vehicle to the prevention and control area is. The relative speed is an important index for comprehensively evaluating the threat of the speed and the threat of the movement direction of the unmanned aerial vehicle.
As can be seen from fig. 3, the operation speed v of the unmanned aerial vehicle, the distance D from the central point of the prevention and control area, the flying heading α of the unmanned aerial vehicle, and the angle β of the unmanned aerial vehicle relative to the central point of the prevention and control area can be obtained by each detection device, and the relative speed v of the unmanned aerial vehicle ′ Can be obtained by calculation from the following formula:
3) Unmanned aerial vehicle fly height
The flying height of the unmanned aerial vehicle can be obtained by radar equipment and radio detection equipment, and as the flying characteristic of the unmanned aerial vehicle is low-altitude flying, the lower the flying height is, the lower the probability of being detected and found is, and the higher the threat to the prevention and control area is. Therefore, unmanned aerial vehicle flight altitude is an important evaluation factor for threat prevention and control areas.
4) Distance between unmanned aerial vehicle and prevention and control area
The distance between the unmanned aerial vehicle and the prevention and control area refers to the connecting line distance of the unmanned aerial vehicle relative to the center point of the prevention and control area, and the smaller the distance between the unmanned aerial vehicle and the prevention and control area is, the higher the threat degree to the prevention and control area is.
5) Cluster unmanned aerial vehicle communication network unmanned aerial vehicle criticality
When the unmanned aerial vehicle clusters execute tasks such as attack and reconnaissance, the communication network among the unmanned aerial vehicle clusters is smooth, the communication is carried out in an ad hoc mode, the clustered unmanned aerial vehicle ad hoc network refers to a complex network which is constructed by taking a single unmanned aerial vehicle as an independent communication network node and depending on communication load according to actual internal and external environment requirements in the process of cooperatively executing the tasks by the clustered unmanned aerial vehicles. In a cluster unmanned aerial vehicle communication network, the criticality of each unmanned aerial vehicle in the cluster communication network is evaluated by intercepting communication signals, so that the importance degree of the unmanned aerial vehicle in the cluster network is determined. The stronger the keywords of the unmanned aerial vehicle in the trunking communication network, the higher the importance degree of the unmanned aerial vehicle in the network is proved, the more the unmanned aerial vehicles are used for communication, and the higher the threat degree of the unmanned aerial vehicle to the prevention and control area is.
The invention adopts the betweenness center index evaluation method to evaluate the criticality of the unmanned aerial vehicle communication network of the cluster unmanned aerial vehicle, namely, the higher the frequency of the unmanned aerial vehicle i on the shortest path of all network unmanned aerial vehicle node pairs of the cluster communication network is, the larger the betweenness of the unmanned aerial vehicle i is, and the higher the criticality of the unmanned aerial vehicle i is. The calculation formula is as follows:
in the above, C i The communication network unmanned plane i is a cluster unmanned plane communication network unmanned plane i at a critical degree,represents the number of shortest paths between the pair of unmanned plane nodes a and b passing through unmanned plane i, U ab Representing the total number of shortest paths between the pair of drone nodes a and b.
The threat index system of the unmanned aerial vehicle is constructed through the factor identification, and is shown in fig. 4.
2. The TOPSIS unmanned aerial vehicle cluster threat ordering method based on entropy weight comprises the following steps:
and (2) combining the threat index system of the unmanned aerial vehicle constructed in the step (1), and sequencing the threats of m unmanned aerial vehicles in the unmanned aerial vehicle cluster by using a TOPSIS method based on entropy weight, namely, objectively determining the weight of the target index by using the entropy weight method, and then sequencing and evaluating the novel threat of the unmanned aerial vehicle in the cluster by using the TOPSIS sequencing method approaching an ideal solution. The method comprises the following specific steps:
1) The entropy weight method determines the weight of the index:
A. respectively carrying out normalization processing on 5 indexes in the unmanned plane cluster threat factor identification:
wherein, index ij Representing the evaluation index of the ith unmanned plane after the jth normalization processing in the cluster, I ij The j-th evaluation index of the ith unmanned aerial vehicle in the cluster is represented, and m represents the number of unmanned aerial vehicles in the unmanned aerial vehicle cluster.
B. Calculating the entropy value H of the j index according to the normalization processing result j And entropy weight w of jth index j ：
2) TOPSIS ordering:
A. constructing a normalized matrix:
B. determination of ideal point A + And negative ideal point A - ：
Wherein, the forward index is the index with higher threat as the index value is larger; the negative-type index is the index with larger index value and smaller threat, in the invention, indexes 2 and 5 are positive-type indexes, and indexes 1,3 and 4 are negative-type indexes;
max 1≤1≤n Index ij 、min 1≤1≤n Index ij respectively representing the maximum value and the minimum value of the j index in the evaluation area;
C. calculation A ij Distance to each positive ideal point and negative ideal point:
D. calculating relative proximity sigma of evaluation object and ideal solution i The threat degree of the unmanned plane i is:
thirdly, the unmanned aerial vehicle group countering decision method based on the countering equipment performance:
under normal conditions, the unmanned aerial vehicle countering system sets a protection area as a protection area with the radius of lkm, a refusing area with the radius of 3km and an early warning area with the radius of 5km, detects and early warns unmanned aerial vehicles in the early warning area, and makes countering decisions on the unmanned aerial vehicles entering the refusing area. In order to realize accurate unmanned aerial vehicle cluster countering decision, the invention provides an unmanned aerial vehicle cluster countering decision method based on countering equipment performance by combining an unmanned aerial vehicle threat sequencing result and countering equipment performance parameters. The method comprises the following specific steps:
1. after the unmanned aerial vehicle group enters the early warning area, the detection equipment detects and monitors the unmanned aerial vehicle group, and threat sequencing is carried out on unmanned aerial vehicles in the group by combining the monitoring result;
2. in the entering refusing area, according to the threat sequencing order of the unmanned aerial vehicle, the comprehensive decision is made by combining the performances of all anti-soft killing and countering devices (such as electromagnetic interference, navigation interference, acoustic interference and navigation decoy);
1) Let the distance d between the unmanned plane and the prevention and control center point UVA The minimum acting distance of each reaction device is obtained as followsMaximum range of action->If a reaction device is present, the reaction device satisfiesGo to step 2), otherwise go to step 6);
2) Distance d combining unmanned aerial vehicle distance prevention and control central point UVA Unmanned aerial vehicle relative flight speed v ′ Calculating time t of unmanned aerial vehicle flying to prevention and control area uva
t uva ＝ UVA / ′
3) Obtaining maximum response time { t } of each device of distance constraint 1 ,……, n If (3) Go to step 4), otherwise go to step 6)
4) Acquiring the use state of the equipment meeting the distance and time constraint, if the equipment is not used, andgo to step 5), otherwise go to step 6)
5) Performing countering decision on countering equipment meeting distance, time and using state constraint according to the sequence of acoustic wave interference, electromagnetic interference, navigation interference and navigation decoy, and if the countering of the unmanned aerial vehicle is unsuccessful according to the sequence, turning to step 6
6) If the device does not have the reaction equipment meeting the conditions or the soft-killing reaction equipment does not succeed in reaction, hard killing means such as laser, missile, patrol missile and the like are adopted.
It will be readily appreciated by those skilled in the art that the foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A countering method for unmanned aerial vehicle cluster attack, the method comprising:
acquiring detection data of a multisource sensor, preprocessing the detection data, performing space-time alignment, data association, track tracking filtering and target fusion to obtain target state data, performing target recognition by using a standard database and a target recognition feature library, and combining the target state data to form final target state information; the multi-source sensor comprises a radar, a radio detection device and an optical detection device;
constructing an unmanned aerial vehicle cluster threat evaluation index system according to the type of the unmanned aerial vehicle, the relative speed of the unmanned aerial vehicle, the flight height of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and a prevention and control area and the criticality of the unmanned aerial vehicle in a cluster unmanned aerial vehicle communication network, and realizing the threat sequencing of the unmanned aerial vehicle by adopting a TOPSIS sequencing method based on entropy weight;
and performing countercheck decision by combining the threat sequencing result of the unmanned aerial vehicle and the performance parameters of countercheck equipment.
2. The method according to claim 1, wherein the acquiring multisource sensor detection data specifically comprises:
when the radar does not detect the target, the radio detection equipment acquires a radio detection track when detecting the target;
when the radar detects a target and the radio detection equipment does not detect the target, a radar track is acquired;
when the radar and the radio detection equipment find targets at the same time, the radar track is preferentially collected, and meanwhile, the model number and the frequency of the unmanned aerial vehicle identified by the radio detection equipment are collected;
and when the radar and the optical detection equipment detect the target at the same time, detection data of the radar and the optical detection equipment are collected at the same time.
3. The method according to claim 1 or 2, wherein the target state data is obtained after the detection data is preprocessed and subjected to space-time alignment, data correlation, track tracking filtering and target fusion, and specifically comprises the following steps:
performing protocol and format conversion on the detected data to form a unified data format;
judging and eliminating the wild value in the detection data;
compressing the detection data;
performing time calibration and space calibration on the detection data;
aiming at the multi-target detection data acquired by each sensor, carrying out data association according to the accuracy of the sensor and the data dimension based on a track association algorithm, and judging which target the acquired detection data belongs to;
carrying out track tracking filtering on the target track by adopting a unscented Kalman filtering algorithm;
and carrying out fusion processing on the multi-sensor track information of the same target so as to track the target.
4. The method of claim 1, wherein the unmanned aerial vehicle cluster threat assessment index system specifically comprises:
the method comprises the steps of acquiring the size of the unmanned aerial vehicle based on target state information, and classifying threat levels according to the size of the unmanned aerial vehicle, wherein the smaller the size of the unmanned aerial vehicle is, the higher the threat level is;
acquiring relative speed threat of unmanned aerial vehicle based on target state informationVelocity v in control zone ′ ：
Wherein v is the unmanned aerial vehicle speed; beta is the angle of the unmanned aerial vehicle relative to the central point of the prevention and control area; alpha is the flight heading of the unmanned aerial vehicle; velocity v ′ The higher the threat level, the higher;
the method comprises the steps of acquiring the flight height of an unmanned aerial vehicle based on target state information, wherein the lower the flight height is, the higher the threat level is;
the threat between the unmanned aerial vehicle and the prevention and control area is obtained, and the connecting distance between the unmanned aerial vehicle and the center point of the prevention and control area is obtained based on the target state information, wherein the smaller the connecting distance is, the higher the threat level is;
cluster unmanned aerial vehicle communication network unmanned aerial vehicle critical threat, and acquiring critical C of unmanned aerial vehicle i in the cluster communication network based on target state information i ：
wherein ,represents the number of shortest paths between the pair of unmanned plane nodes a and b passing through unmanned plane i, U ab Representing the total number of shortest paths between the unmanned plane node pairs a and b;
critical C i The higher the threat level, the higher.
5. The method according to claim 4, characterized in that the entropy weight based TOPSIS ordering method specifically comprises the following steps:
normalizing 5 evaluation indexes in an unmanned aerial vehicle cluster threat evaluation index system;
wherein Index is ij Represents the j-th evaluation index of the ith unmanned aerial vehicle after normalization processing in the cluster, I ij The jth evaluation index of the ith unmanned aerial vehicle in the cluster is represented;
calculating entropy value H of j-th evaluation index according to normalization processing result j And entropy weight w of jth evaluation index j ；
k＝lnm
Constructing a normalized matrix A;
determination of ideal point A + And negative ideal point A - ；
Wherein, the forward index is the index with higher threat as the index value is larger; the negative index is the index with larger index value and smaller threat, and max 1≤i≤n Index ij and min1≤i≤n Index ij Respectively representing the maximum value and the minimum value of the j-th evaluation index in the evaluation area;
calculation A ij Distance to each ideal pointA ij Distance to each negative ideal point +.>
Calculating threat degree sigma of unmanned plane i i ；
6. The method of claim 1, wherein the countering decision is made in combination with the unmanned aerial vehicle threat ranking result and a countering device performance parameter, in particular:
according to the threat sequencing result of the unmanned aerial vehicle, the following operations are sequentially carried out on each unmanned aerial vehicle from high threat level to low threat level of the unmanned aerial vehicle:
(1) Let the distance d between the unmanned plane and the prevention and control center point UVA Obtaining the minimum acting distance of each soft-killing reaction deviceMaximum distance of action->If a reaction device i is present, the reaction device i meetsGo to step (2) or else go to step (6);
(2) Distance d combining unmanned aerial vehicle distance prevention and control central point UVA Unmanned aerial vehicle relative flight speed v ′ Calculating time t of unmanned aerial vehicle flying to prevention and control area uva ：
t uva ＝d UVA /v′
(3) Obtaining maximum response time { t } of each device 1 ,……,t n If there is a reaction device i satisfyingGo to step (4), otherwise go to step (6);
(4) Acquiring the use state of the countering equipment i, if the equipment is unused, and go to step (5), otherwise go to step (6);
(5) Performing countering decision on countering equipment i meeting the constraints of distance, time and use state according to the sequence of acoustic interference, electromagnetic interference, navigation interference and navigation decoy, and if the unmanned aerial vehicle cannot be countered successfully according to the sequence, turning to the step (6);
(6) The soft killing and countering equipment fails in countering, and the target unmanned aerial vehicle is knocked down by adopting the hard killing and countering equipment, wherein the hard killing and countering equipment comprises: laser, missile and patrol missile.
7. A countering system for unmanned cluster attacks, the system comprising:
the data acquisition processing module is used for acquiring detection data of the multi-source sensor, preprocessing the detection data, performing space-time alignment, data association, track tracking filtering and target fusion to obtain target state data, performing target recognition by using the standard database and the target recognition feature library, and combining the target state data to form final target state information; the multi-source sensor comprises a radar, a radio detection device and an optical detection device;
the threat ranking module is used for constructing an unmanned aerial vehicle cluster threat evaluation index system according to the type of the unmanned aerial vehicle, the relative speed of the unmanned aerial vehicle, the flight altitude of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and a prevention and control area and the criticality of the unmanned aerial vehicle in a clustered unmanned aerial vehicle communication network, and realizing the threat ranking of the unmanned aerial vehicle by adopting a TOPSIS ranking method based on entropy weight;
and the countercheck decision module is used for combining the threat sequencing result of the unmanned aerial vehicle and the performance parameters of countercheck equipment to make countercheck decisions.
8. The system of claim 7, wherein the threat ranking module constructs the drone cluster threat assessment index system by:
the unmanned aerial vehicle type threat unit is used for acquiring the size of the unmanned aerial vehicle based on the target state information, and threat levels are classified according to the size of the unmanned aerial vehicle, and the smaller the size of the unmanned aerial vehicle is, the higher the threat level is;
the unmanned aerial vehicle relative speed threat unit is used for acquiring speed v of the unmanned aerial vehicle relative to the prevention and control area based on the target state information ′ ：
Wherein v is the unmanned aerial vehicle speed; beta is the angle of the unmanned aerial vehicle relative to the central point of the prevention and control area; alpha is the flight heading of the unmanned aerial vehicle; velocity v ′ The higher the threat level, the higher;
the unmanned aerial vehicle flight height threat unit is used for acquiring the unmanned aerial vehicle flight height based on the target state information, wherein the lower the flight height is, the higher the threat level is;
the threat unit is used for acquiring the connecting line distance between the unmanned aerial vehicle and the central point of the prevention and control area based on the target state information, wherein the smaller the connecting line distance is, the higher the threat level is;
the unmanned aerial vehicle critical threat unit of the cluster unmanned aerial vehicle communication network is used for acquiring the critical C of the unmanned aerial vehicle i in the cluster communication network based on the target state information i ：
wherein ,represents the number of shortest paths between the pair of unmanned plane nodes a and b passing through unmanned plane i, U ab Representing the total number of shortest paths between the unmanned plane node pairs a and b;
critical C i The higher the threat level, the higher.
9. The system of claim 8, wherein the threat ranking module implements unmanned aerial vehicle threat ranking by sequentially performing the following elements:
the normalization unit is used for performing normalization processing on 5 evaluation indexes in the unmanned aerial vehicle cluster threat evaluation index system;
wherein Index is ij Represents the j-th evaluation index of the ith unmanned aerial vehicle after normalization processing in the cluster, I ij The jth evaluation index of the ith unmanned aerial vehicle in the cluster is represented;
an entropy weight calculation unit for calculating the entropy value H of the j-th evaluation index according to the normalization processing result j And entropy weight w of jth evaluation index j ；
k＝lnm
The matrix calculation unit is used for constructing a normalized matrix A;
an ideal point calculating unit for determining ideal point A + And negative ideal point A - ；
Wherein, the forward index is the index with higher threat as the index value is larger; the negative index is the index with larger index value and smaller threat, and max 1≤i≤n Index ij and min1≤i≤n Index ij Respectively representing the maximum value and the minimum value of the j-th evaluation index in the evaluation area;
a distance calculating unit for calculating A ij Distance to each ideal pointA ij Distance to each negative ideal point +.>
Threat degree calculating unit for calculating threat degree sigma of unmanned plane i i ；
10. The system of claim 7, wherein the countercheck decision module comprises, in particular, the following units, executed in sequence:
according to the threat sequencing result of the unmanned aerial vehicle, the following unit operations are sequentially executed for each unmanned aerial vehicle from high threat level to low threat level of the unmanned aerial vehicle:
a distance judging unit for setting the distance d between the unmanned aerial vehicle and the prevention and control center point UVA Obtaining the minimum acting distance of each soft-killing reaction deviceMaximum distance of action->If a reaction device i is present, the reaction device i meetsThen turning to a speed judging unit, otherwise turning to a hard killing and countering unit;
a speed judging unit for combining the distance d between the unmanned aerial vehicle and the prevention and control center point UVA Unmanned aerial vehicle relative flight speed v ′ Calculating time t of unmanned aerial vehicle flying to prevention and control area uva ：
t uva ＝d UVA /v′
A time judging module for obtaining the most of the devicesLarge response time { t } 1 ,……,t n If there is a reaction device i satisfyingThen turning to a state judging unit, otherwise turning to a hard killing and countering unit;
a state judging unit for acquiring the use state of the countering device i, if the device is unused, andturning to a soft kill reaction unit, otherwise turning to a hard kill reaction unit;
the soft killing and countering unit is used for countering the countering equipment i meeting the constraints of distance, time and use state according to the sequence of acoustic interference, electromagnetic interference, navigation interference and navigation decoy, and if the unmanned aerial vehicle cannot be countered successfully according to the sequence, the countering equipment i is switched to the hard killing and countering unit;
the hard killing and countering unit is used for failing the countering of the soft killing and countering equipment, and the target unmanned aerial vehicle is knocked down by adopting the hard killing and countering equipment, and the hard killing and countering equipment comprises: laser, missile and patrol missile.
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