CN110502031B - Task demand-based heterogeneous unmanned aerial vehicle cluster cooperative optimal configuration method - Google Patents
Task demand-based heterogeneous unmanned aerial vehicle cluster cooperative optimal configuration method Download PDFInfo
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Abstract
The invention discloses a task demand-based heterogeneous unmanned aerial vehicle cluster cooperative optimal configuration method, which is used for solving the problem of configuration of cluster structures and scales before an unmanned aerial vehicle executes a task under the condition of unmanned aerial vehicle cluster cooperative combat, so that the method is suitable for an actual battlefield and further obtains an efficiency optimal cluster configuration result. According to a specific task scene, the cooperative characteristics of heterogeneous unmanned aerial vehicles are considered, the thermal efficiency of an unmanned aerial vehicle cluster and the required thermal efficiency are evaluated by adopting an analytic hierarchy process, and a cluster configuration model is established for the heterogeneous unmanned aerial vehicle cluster comprising the unmanned aerial vehicles for investigation, attack and refueling by adopting a nonlinear integer programming method to obtain a final cluster configuration result of the unmanned aerial vehicles.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle cluster cooperation, and particularly relates to a task demand-based heterogeneous unmanned aerial vehicle cluster cooperation optimal configuration method.
Background
In the traditional mode, the efficiency of the unmanned aerial vehicle cluster is calculated by simply adding the efficiencies of all unmanned aerial vehicles, and the sum is used as the overall efficiency of the unmanned aerial vehicle cluster, and the cooperative factors among heterogeneous unmanned aerial vehicles are not considered, so that the configuration results of the cluster are different.
With the development of cluster configuration technology, the characteristics of cooperation among unmanned aerial vehicles are considered, the overall efficiency of the unmanned aerial vehicle cluster is calculated by the novel scheme on the basis of considering the cooperation factors among the unmanned aerial vehicles, but a cluster result meeting task requirements is not configured by combining a specific battlefield environment, and the practical significance is lacked.
Therefore, it is necessary to provide a task-demand-based collaborative optimal configuration method for a heterogeneous unmanned aerial vehicle cluster, so as to be suitable for an actual battlefield and further obtain an optimal-efficiency cluster configuration result.
Disclosure of Invention
Object of the Invention
Aiming at the defects in the prior art, the invention aims to provide an optimal configuration method in heterogeneous unmanned aerial vehicle cluster cooperation based on task requirements, which is used for solving the problem of configuration of cluster structures and scales before unmanned aerial vehicles execute tasks under the condition of unmanned aerial vehicle cluster cooperative combat.
Technical solution of the invention
In order to achieve the purpose, the invention adopts the following technical scheme:
the task demand-based heterogeneous unmanned aerial vehicle cluster cooperative optimal configuration method comprises the following steps:
step 1: selecting the unmanned aerial vehicle cluster fire efficiency, the fire efficiency required by a battlefield, the unmanned aerial vehicle cluster cruising ability and the cruising ability required by the battlefield as battlefield matching combat evaluation indexes of enemy and my confrontation;
step 2: evaluating the unmanned aerial vehicle cluster fire efficiency and the required fire efficiency by adopting an analytic hierarchy process to respectively obtain fire attack efficiency values of the unmanned aerial vehicle cluster fire efficiency and the required fire efficiency;
and step 3: comparing the endurance capacity of the unmanned aerial vehicle cluster with the endurance capacity required by the battlefield, evaluating and obtaining the endurance values of the unmanned aerial vehicle cluster and the battlefield;
and 4, step 4: and establishing a cluster configuration model by adopting a nonlinear integer programming method to obtain the optimal configuration of the unmanned aerial vehicle.
Preferably, the method of step 1 is:
on the basis of unmanned aerial vehicle cluster combat effectiveness evaluation, the contrast process of the enemy and the my party is carried out, the configuration result can reflect the requirement that the unmanned aerial vehicle cluster comprehensively suppresses the enemy or meets the mission requirement, and the unmanned aerial vehicle cluster fire efficiency, the efficiency required by a battlefield, the unmanned aerial vehicle cluster cruising ability and the cruising ability required by the battlefield are selected as typical matching combat evaluation indexes for reflecting the enemy and the my confrontation.
Preferably, the method for evaluating the fire efficiency required by the cooperation of the enemy combat in the step 2 comprises the following steps:
the main factors influencing the efficiency of the required fire attack are obtained through analysis: the anti-strike capability of the enemy combat unit, the killing capability of a weapon system and the urgency degree of attack time are four factors, and the time urgency degree is influenced by the time of the occurrence of the target and the size factor of a time sensitive window of the enemy combat unit, so that a fire efficiency index system required by the enemy is established, and reference is made to figure 1.
Obtaining the influence effect values of each level of the fire effect index system required by the enemy through the weight index calculation and the index quantification process on the basis of the weight index calculation
And the fire attack efficiency value P required by the enemy top air defense combat unit je jThe calculation formula of (a) is as follows:
in the formula: n represents the number of elements associated with the level in the performance index system, wiThe weight corresponding to the element is represented,
the influence effect value of each element in each level on the superior index is shown.
Total fire attack efficiency P required by whole enemy air defense systeme totalThe calculation formula of (a) is as follows:
wherein N represents that there are N different types of enemy air combat units in the battlefield, and NjIndicating the number of units of each type of battle, Pe jRepresenting the desired fire attack efficacy value for different types of individual combat units.
Preferably, in step 2, the evaluation method for the fire efficiency of the cluster of the my heterogeneous unmanned aerial vehicles comprises the following steps:
main factors influencing the cluster fire attack performance of the heterogeneous unmanned aerial vehicle of our part: the number of ammunition carried by the unmanned aerial vehicle, the fire striking accuracy of the unmanned aerial vehicle, and the fire killing power of ammunition refer to fig. 2. Considering the cooperative influence of the unmanned aerial vehicle cluster as the influence of the fire striking capability of the attacking unmanned aerial vehicle on the detection precision of the detecting unmanned aerial vehicle, the cooperative efficiency influence factors are as follows: aircraft altitude, resolution accuracy and scout distance, see fig. 3. Obtaining the early tracking and positioning efficiency of the attacking unmanned aerial vehicle before and after investigation cooperation
And
the calculation formula of (a) is as follows:
in the formula: n denotes the number of relevant elements of the hierarchy, wiThe representation elements correspond to weights, and
and
and respectively tracking and positioning the efficiency index values in the evaluation system for the attacking unmanned aerial vehicle.
The formula for calculating the synergistic effect factor is as follows:
the synergistic effect of many reconnaissance unmanned aerial vehicles of the same model to attacking unmanned aerial vehicle can superpose, and the computational formula is as follows:
in the formula, NDFor scouting the number of drones in the cluster, ε is a synergistic influence factor, I0Attack the attack accuracy of unmanned aerial vehicle for reconnaissance of cooperative influence of unmanned aerial vehicle, I1Attack unmanned aerial vehicle's striking precision for when having reconnaissance unmanned aerial vehicle synergistic effect.
Index quantification is carried out to obtain unmanned aerial vehicle firepower efficiency evaluation value
Get out m level unmanned aerial vehicle's firepower efficiency value Pa mThe calculation formula of (a) is as follows:
wherein n represents the number of related elements of the hierarchy, wiThe presentation element corresponds to a weight that,
is the firepower efficiency evaluation value of the unmanned aerial vehicle.
Obtaining a total value P of the cluster fire efficiency of unmanned aerial vehicles at our party according to the number of unmanned aerial vehicles at our party based on expected eliminationa totalThe calculation formula of (a) is as follows:
in the formula: n represents attack unmanned aerial vehicle carrying loads of different models in total N in cluster, NiNumber of unmanned aerial vehicles carrying loads of various types, Pa iIndicating the fire efficacy value of the i-th level drone.
Preferably, the method for calculating the required cruising ability of the unmanned aerial vehicle cluster in step 3 includes:
will not be except for refuelling unmanned aerial vehicleThe man-machine is called as a functional unmanned aerial vehicle, and the rated endurance EC is obtained according to the oil loading capacity of the functional unmanned aerial vehicle irThe calculation formula of (a) is as follows:
in the formula: i denotes unmanned plane i, OLiAnd OWiRespectively representing the oil loading and hundred kilometers oil consumption of the unmanned aerial vehicle i, and converting OWiSet to a constant value.
Comparison of
Task voyage Dis with unmanned aerial vehicle iiWhen it occurs
The situation of (2) indicates that the cruising ability of the unmanned aerial vehicle is insufficient, and the calculation formula of the cruising ability REC required by the unmanned aerial vehicle cluster is obtained as follows:
in the formula: i denotes unmanned plane i, NUNumber of unmanned aerial vehicles with indication function, DisiRepresenting the mission course of drone i,
rated cruising ability, OW, for unmanned aerial vehicle iiRepresenting the hundred kilometers fuel consumption of drone i.
Preferably, the method for calculating the cruising ability of the unmanned aerial vehicle cluster in the step 3 comprises:
the calculation formula of the available cruising ability AEC of the refueling unmanned aerial vehicle is as follows:
AEC＝NO·OLO
in the formula: n is a radical ofOAnd OLORespectively representing the number and the oil carrying capacity of the refueling unmanned aerial vehicles.
Preferably, the configuration method of the drones with different functions in step 4 is as follows:
the probability of the investigation unmanned aerial vehicle successfully executing the task on the target is PD0Requires NDThe unmanned aerial vehicle for frame investigation executes the task to the target simultaneously to ensure that the success probability of the task reaches PDmaxAbove, then NDThe calculation formula of (a) is as follows:
in the formula: pD0Representing the probability of successful task execution of the target by the investigation drone, NDFor investigating the number of unmanned aerial vehicles, PDmaxIs NDThe success probability of the rack reconnaissance drone performing the task on the target simultaneously.
The total value of the overall fire efficiency of the unmanned aerial vehicle cluster is larger than the total value of the required fire efficiency of the enemy, and the expression is as follows:
in the formula: penemyIndicating the fire efficacy value required by the enemy,
indicating the fire efficacy value of the unmanned aerial vehicle cluster of my party.
The configuration result of the refueling unmanned aerial vehicle must enable the usable cruising ability of the cluster to be larger than the cruising ability required by a battlefield, and the expression is as follows:
REC＜AEC
in the formula: REC represents the required range of the drone cluster and AEC represents the available range of the drone cluster.
Preferably, the nonlinear integer programming model for cluster optimal configuration in step 4 is:
min(costammunition+costUnmanned plane)
In the formula: costAmmunitionCost for ammunitionUnmanned planeFor unmanned aerial vehicle cost, the fuel is ignored.
THE ADVANTAGES OF THE PRESENT INVENTION
The invention has the advantages that:
1. on the basis of establishing an unmanned aerial vehicle cluster efficiency evaluation index system by an analytic hierarchy process, the invention adopts a specific quantification criterion to quantify the efficiency of each element, including qualitative description by using a 9-level quantification theory and quantitative description by using interval quantification, and has the beneficial effect of realizing the purpose of specifying the efficiency index value.
2. The method selects the cluster cruising ability and the cruising ability required by the battlefield as the unmanned aerial vehicle cluster configuration conditions through the task demand analysis, and has the advantages of realizing the combination of actual scenes and considering the configuration requirements of the task demand.
3. The cooperative effect influence degree of the investigation unmanned aerial vehicle on the attack unmanned aerial vehicle in the heterogeneous unmanned aerial vehicle cluster, namely the cooperative effect influence factor epsilon, is calculated quantitatively, so that the efficiency error caused by the cooperative characteristic is reduced, and the cluster configuration result can be obtained more accurately.
Drawings
FIG. 1 is a diagram of a fire attack performance index system for an enemy battlefield.
Fig. 2 is an unmanned aerial vehicle cluster fire attack performance index system diagram.
Fig. 3 is a graph of the influence factors of tracking and positioning efficiency of the unmanned aerial vehicle.
Fig. 4 is a flow chart of estimating the fire attack efficiency of the unmanned aerial vehicle attacked by my party.
Detailed Description
The detailed description of the embodiments of the present invention is provided in conjunction with the summary of the invention and the accompanying drawings.
The method specifically specifies a task scene, considers the cooperative characteristics of the heterogeneous unmanned aerial vehicle, and obtains the final cluster configuration result of the unmanned aerial vehicle by using a nonlinear integer programming method through efficiency analysis and comparison on the basis.
Specifically, the task demand-based collaborative optimal configuration method for the heterogeneous unmanned aerial vehicle cluster is as follows:
step 1: selecting the fire efficiency of the unmanned aerial vehicle, the fire efficiency required by a battlefield, the cluster cruising ability of the unmanned aerial vehicle and the cruising ability required by the battlefield as battlefield matching combat evaluation indexes of enemy and my confrontation;
step 2: evaluating the thermal efficiency of the unmanned aerial vehicle cluster and the thermal efficiency required by a battlefield by adopting an analytic hierarchy process to respectively obtain the thermal attack efficiency values of the unmanned aerial vehicle cluster and the battlefield;
and step 3: comparing the endurance capacity of the unmanned aerial vehicle cluster with the endurance capacity required by the battlefield, evaluating and obtaining the endurance values of the unmanned aerial vehicle cluster and the battlefield;
and 4, step 4: and establishing a cluster configuration model for a heterogeneous unmanned aerial vehicle cluster comprising a detection unmanned aerial vehicle, an attack unmanned aerial vehicle and a refueling unmanned aerial vehicle by adopting a nonlinear integer programming method to obtain the optimal configuration of the unmanned aerial vehicle.
Step 1, combining the requirements of the SEAD task, supposing that three types of unmanned aerial vehicles of detection, attack and refueling are stored in the base of the local, and selecting the fire efficiency and the cruising ability of the unmanned aerial vehicle cluster as the cluster combat efficiency evaluation indexes. And corresponding performance indexes of enemy air defense combat units are evaluated, namely the performance indexes required by the battlefield are configured to meet the requirement of comprehensively suppressing enemy and mission. In conclusion, two pairs of indexes, namely the fire efficiency of the unmanned aerial vehicle cluster and the fire efficiency required by a battlefield, and the overall cruising ability of the unmanned aerial vehicle cluster and the cruising ability required by the battlefield, are selected as matching combat evaluation indexes of the enemy and my confrontation.
Step 2, evaluating the cluster fire efficiency and the required fire efficiency by adopting an analytic hierarchy process, wherein the evaluation comprises the following steps:
step 2.1: evaluating the fire efficiency required by the cooperation of the enemy combat;
firstly, aiming at rapidity and time-sensitive characteristics displayed by a battlefield environment, the influence of a time-sensitive target is considered in an unmanned aerial vehicle cluster configuration process, and a fire efficiency index system required by the diagram 1 is established. The main factors affecting the effectiveness of the fire attack include: battle by enemyUnit self anti-hammering capability (B)1) Weapon system lethality (B)2) Degree of attack time urgency (B)3) The time urgency is further influenced by the time of the appearance of the target (C)1) And its own time sensitive window size (C)2) Influence.
Wherein, the attack time urgency degree reflects the harmonious time range of the unmanned aerial vehicle cluster in the process of attacking the target. The earlier the enemy target appears means that the cluster has a smaller adjustable time window at the time of attack, thus resulting in a higher urgency for the target to attack time. Similarly, if the time sensitive window of a target is small, the time urgency of the target is also high.
Secondly, aiming at an efficiency index system, based on a nine-scale scoring rule table, an expert group scores layer by layer to construct a related element importance judgment matrix, and then a weight calculation method is used for solving a weight value between efficiency indexes required by an enemy.
Thirdly, on the basis of the index weight, the efficiency of each element is quantized by adopting specific quantification, the qualitatively described attribute is quantized by adopting an improved G.AMILLER 9-level quantization theory, and the quantitatively described attribute is quantized by adopting an interval quantization method.
The invention improves the 9-level quantization theory, and according to the actual situation of quantitative or qualitative attributes, the minimum value of the attributes is set to be 0 level or 1 level, while the maximum value is generally set to be 9 levels with higher levels, which are respectively called as the left base point and the right base point of a quantization level interval. If the boundary value of the quantization interval is already given, the corresponding base point of the level can be directly determined. Therefore, the quantization criteria for each attribute are introduced in bottom-up order as follows:
1) index of C layer
(1) Target appearance time of enemy air combat unit
And acquiring the occurrence time of the target according to the time-sensitive window of each enemy air defense unit. The target occurrence time is differed from the unmanned aerial vehicle takeoff time, the distance between the unmanned aerial vehicle from a takeoff airport to the target is calculated by referring to parameters such as the flight speed of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and the target and the flight speed performance interval [ V ]min,Vmax]An Estimated Time of Arrival (ETA) window of the unmanned aerial vehicle flight target under restriction, where VmaxAnd VminRespectively the maximum and minimum speed of the drone. With [ ETA ]min,ETAmax]The left and right boundary values of the window are respectively used as 9-level and 1-level base points, and the time of occurrence of each target is quantified at equal intervals, wherein ETAmaxAnd ETAminAnd calculating the ETA time from the unmanned aerial vehicle to the target point according to the distance between the unmanned aerial vehicle and the target point and the maximum and minimum speed of the unmanned aerial vehicle. If the time-sensitive time window of the target is at ETAminPreviously, it was explained that the time-sensitive target is extremely urgent and needs to be hit preferentially; if the time sensitive window is in ETAmaxLater, the target appears later, and a certain coordination space can be left for the cluster in the subsequent planning process.
(2) Time sensitive window size of enemy air combat unit
The time-sensitive window size reflects the task killing chain length of the target, and smaller window size indicates higher degree of killing chain compression. Firstly, acquiring time-sensitive windows of all enemy air defense units, comparing the sizes of all target time-sensitive windows, setting the target with the minimum window length as a right base point to be 9 grades, and setting a left base point which is not required by the time-sensitive windows to be 0 grade, and then sequentially quantizing the sizes of all target time-sensitive windows at equal intervals.
2) Index of B layer
(1) Anti-strike capability of enemy air defense combat unit
And classifying the enemy combat units in sequence according to the anti-strike capability strength classification table.
(2) Firepower killing capability of enemy air defense combat unit
Quantifying the enemy combat units in sequence according to the fire killing capability classification table, for example, quantifying the fire killing capability of the enemy air defense combat units to 0-9 levels at equal intervals of 0-20 kg. The 20 kg explosion equivalent is equivalent to the explosion energy of 2-3 electromagnetic rail guns in the American navy, and therefore, the equivalent is enough to be used as the maximum base point of the text quantization interval.
(3) Attack time urgency of enemy air combat unit
The attack time urgency of the enemy air combat unit is determined by two factors of the next layer, the appearance time and the time sensitive window, and has no practical significance. Therefore, the base point of the quantitative interval can be set in a mode of estimating the maximum and minimum values, and the task scene is combined according to the latest target occurrence time TlateAnd a maximum time sensitive window TSTmaxAnd calculating the minimum value of the attack time urgency degree, calculating the maximum value of the time urgency degree in the same way, and respectively using the minimum value and the maximum value as the base points of the 1-9 levels.
Finally, we performed the calculation of potency:
the influence effect value of each element in each layer on the superior index can be obtained through the quantization principle
Combining with the weighted value, the required fire power efficiency value P of the top enemy air defense combat unit j is obtainede j：
In the formula: n denotes the number of relevant elements of the hierarchy, wiThe weight corresponding to the element is represented,
and expressing the influence effect value of each element in each level in the corresponding effect index system on the upper level index.
After the required fire efficiency values of all the enemy air defense combat units in the battlefield environment are obtained through calculation, the total value P of the required fire efficiency of the enemy air defense system in the whole battlefield can be calculated according to the number of all the enemy air defense combat unitse total：
In the formula: n denotes that there are N different types of enemies in the battlefieldAir defense combat unit, niIndicating the number of units of each type of battle, Pe iRepresenting the desired fire attack efficacy value for different types of individual combat units.
Step 2.2: evaluating the thermal efficiency of the heterogeneous unmanned aerial vehicle cluster;
similarly, a cluster fire efficiency index system of heterogeneous unmanned aerial vehicles is established. The factors influencing the fire attack capability of the unmanned aerial vehicle of the same party mainly comprise: number of payload (H) of unmanned aerial vehicle1) Fire striking precision (H) of unmanned aerial vehicle2) Firepower killing power of ammunition (H)3). The payload capacity of the unmanned aerial vehicle can measure the continuous attack capability of the unmanned aerial vehicle to some extent; the probability that the fire striking precision of the unmanned aerial vehicle points to a target is mainly influenced by an airborne sensor of the unmanned aerial vehicle; ammunition firepower lethality is a hard index for measuring the firepower attack capability of the unmanned aerial vehicle and is the basis of other two influence factors.
The cooperation influence of the heterogeneous unmanned aerial vehicles is considered, and when the heterogeneous unmanned aerial vehicle cluster cooperates in a battle, certain positive effects can be generated among different types of unmanned aerial vehicles. For example, when an attacking unmanned aerial vehicle executes an attacking task, airborne intelligence reconnaissance equipment is required to be used for obtaining information such as the position, the appearance and the motion state of a target, and positioning and aiming are carried out during attacking. However, due to the limitation of bearing capacity, the attacking unmanned aerial vehicle is difficult to carry high-precision reconnaissance equipment, and the precision of the subsequent attacking process is seriously influenced. However, when the reconnaissance unmanned aerial vehicle carrying the high-precision reconnaissance equipment exists in the cluster, the overall information acquisition capacity of the cluster can be greatly enhanced. High accuracy information is transmitted through the data communication chain between the unmanned aerial vehicles, and the attack precision of attacking the unmanned aerial vehicles can be effectively improved, so that the overall fire efficiency of the cluster is improved. The larger the number of unmanned aerial vehicles to be detected in the cluster, the larger the positive effect on the unmanned aerial vehicle to be attacked.
The degree of the cooperative influence of the fire striking capacity of the attacking unmanned aerial vehicle on the reconnaissance unmanned aerial vehicle is expressed by a cooperative efficiency influence factor epsilon, namely the fire striking efficiency ratio of the attacking unmanned aerial vehicle before and after the cooperative assistance of the reconnaissance unmanned aerial vehicle in the cluster. Since the cooperative process mainly affects the tracking and positioning process of the attack task, the method for defining epsilonEfficiency is mainly determined by the high accuracy scout load performance of scout unmanned aerial vehicle. The influence factors are as follows: altitude of flight (E)1) Resolution accuracy (E)2) And scouting distance (E)3) The tracking and positioning efficiency of the attacking unmanned aerial vehicle is evaluated by adopting an analytic hierarchy process, index efficiency values in an attacking unmanned aerial vehicle tracking and positioning evaluation system before and after the cooperation of the detecting unmanned aerial vehicle can be obtained through quantification, and the index efficiency values are respectively
And
and combining the weights, the early tracking and positioning efficiency of the attacking unmanned aerial vehicle before and after the cooperation of the reconnaissance unmanned aerial vehicle can be solved
And
in the formula: n denotes the number of relevant elements of the hierarchy, wiThe presentation element corresponds to a weight that,
and
respectively representing the index effect values in the tracking and positioning evaluation system of the attacking unmanned aerial vehicle. Thus, the synergistic impact factor ε may be expressed as:
in the formula:
and
the early tracking and positioning efficiencies of the attacking unmanned aerial vehicle of one party before and after the cooperation of the reconnaissance unmanned aerial vehicle are respectively expressed.
When a plurality of the same type detection unmanned aerial vehicles exist in the cluster, the synergistic influence effects of the detection unmanned aerial vehicles on the attack unmanned aerial vehicles can be superposed, and then the attack precision I of the attack unmanned aerial vehicles after the synergistic influence of the detection unmanned aerial vehicles1Can be expressed as:
in the formula: n is a radical ofDFor scouting the number of drones in the cluster, ε is a synergistic influence factor, I0Attack unmanned attack accuracy of unmanned aerial vehicle for no reconnaissance unmanned aerial vehicle synergy1Attack unmanned striking precision of unmanned aerial vehicle for reconnaissance unmanned aerial vehicle when influence is cooperateed.
Index efficiency value in the unmanned aerial vehicle fire efficiency evaluation system of our party is obtained through index quantification
The firepower effect value P of the unmanned aerial vehicle j of the same party can be solved by combining the weighta j：
In the formula: n denotes the number of relevant elements of the hierarchy, wiThe presentation element corresponds to a weight that,
and indicating the index effect value in the unmanned aerial vehicle fire effect evaluation system of the same party.
Calculating the total value P of the cluster fire attack efficiency of our party according to the number of various types of unmanned aerial vehicles which are expected to be sent out for operation in the operation base of our partya totalThe following were used:
in the formula: n represents attack unmanned aerial vehicle carrying loads of different models in total N in cluster, NiNumber of unmanned aerial vehicles carrying loads of various types, Pa iIndicating the fire attack efficacy value for drone j.
And step 3: the step 3 comprises the following steps:
step 3.1: the required cruising ability calculation method of the unmanned aerial vehicle cluster comprises the following steps:
unmanned aerial vehicles except the refueling unmanned aerial vehicle are called functional unmanned aerial vehicles, and the rated cruising ability EC is calculated according to the fuel loading capacity of the functional unmanned aerial vehicle irThe calculation formula of (a) is as follows:
in the formula: OLiAnd OWiRespectively representing the oil loading and hundred kilometers oil consumption of the unmanned aerial vehicle i, and converting OWiSet to a constant value.
Will be provided with
Task voyage Dis with unmanned aerial vehicle iiBy comparison, when present
The situation of (2) indicates that the cruising ability of the unmanned aerial vehicle is insufficient, and a calculation formula for obtaining the required cruising ability REC is as follows:
in the formula: i tableUnmanned plane i, NUNumber of unmanned aerial vehicles with indication function, DisiRepresenting the mission course of drone i,
rated cruising ability, OW, for unmanned aerial vehicle iiRepresenting the hundred kilometers fuel consumption of drone i.
The calculation formula of the endurance AEC required by the battlefield of the refueling unmanned aerial vehicle is as follows:
AEC＝NO·OLO
in the formula: n is a radical ofOAnd OLORespectively representing the number and the oil carrying capacity of the refueling unmanned aerial vehicles.
Step 3.2: the method for calculating the required cruising ability of the unmanned aerial vehicle cluster in the battlefield comprises the following steps:
the calculation formula of the endurance AEC required by the battlefield of the refueling unmanned aerial vehicle is as follows:
AEC＝NO·OLO
in the formula: n is a radical ofOAnd OLORespectively represent refuel unmanned aerial vehicle quantity and fuel capacity.
Preferably, step 4 comprises:
step 4.1: the configuration method of the unmanned aerial vehicle with different functions comprises the following steps:
in the scene thought, the reconnaissance unmanned aerial vehicle only executes confirmation and damage evaluation tasks, so that the configuration of the reconnaissance unmanned aerial vehicle is simplified, and from the perspective of the probability of successfully reconnaissance of targets, the probability of successfully executing the tasks on the targets by one reconnaissance unmanned aerial vehicle is PD0Requires NDThe reconnaissance unmanned aerial vehicle can ensure that the success probability of the task reaches P only by simultaneously executing the task on the targetDmaxAbove, then NDThe calculation formula of (a) is as follows:
in the formula: pD0Representing the probability of successful task execution of the target by the investigation drone, NDFor investigating the number of unmanned aerial vehicles, PDmaxIs NDThe frame investigation unmanned aerial vehicle aims at the targetWhile the success probability of the task is performed.
According to the configuration result of the attacking unmanned aerial vehicle, the total value of the overall fire striking efficiency of the unmanned aerial vehicle cluster of the party is larger than the total value of the required fire striking efficiency of the enemy at the moment, so that the unmanned aerial vehicle cluster of the party has the capability of suppressing the enemy and meeting the task requirement, and the mathematical expression is as follows:
PUAVs≥Penemy
in the formula: pUAVsRepresents the total value of the unmanned aerial vehicle cluster fire striking efficiency, PenemyAnd the total value of the fire striking efficiency required by the enemy is represented.
The configuration result of the refueling unmanned aerial vehicle must enable the usable cruising ability of the cluster to be larger than the cruising ability required by a battlefield, and the expression is as follows:
REC>AEC
the total value of the overall fire efficiency of the unmanned aerial vehicle cluster is larger than the total value of the required fire efficiency of the enemy, and the expression is as follows:
in the formula: penemyIndicating the fire efficacy value required by the enemy,
indicating the fire efficacy value of the unmanned aerial vehicle cluster of my party.
Step 4.2: the cluster optimal configuration model is as follows:
the formula described in 4.1 is a constraint term for cluster optimization configuration, and the objective function should maximize the effectiveness of drone cluster combat. The group combat effectiveness is described by the combat cost price, and a reasonable result is solved by using a Monte Carlo method. The optimal configuration model is obtained as follows:
min(costammunition+costUnmanned plane)
In the formula: costAmmunitionCost for ammunitionUnmanned planeFor unmanned aerial vehicle cost, the fuel is ignored.
Claims (9)
1. A task demand-based heterogeneous unmanned aerial vehicle cluster cooperative optimal configuration method is characterized by comprising the following steps:
step 1: selecting the unmanned aerial vehicle cluster fire efficiency, the fire efficiency required by a battlefield, the unmanned aerial vehicle cluster cruising ability and the cruising ability required by the battlefield as battlefield matching combat evaluation indexes of enemy and my confrontation;
step 2: evaluating the thermal efficiency of the unmanned aerial vehicle cluster and the thermal efficiency required by a battlefield by adopting an analytic hierarchy process to respectively obtain the thermal attack efficiency values of the unmanned aerial vehicle cluster and the battlefield;
and step 3: comparing the cruising ability of the unmanned aerial vehicle cluster with the cruising ability required by a battlefield to evaluate and obtain the cruising ability values of the unmanned aerial vehicle cluster and the battlefield;
and 4, step 4: establishing a cluster configuration model by adopting a nonlinear integer programming method to obtain the optimal configuration of the unmanned aerial vehicle; the method specifically comprises the following steps: firstly, determining constraint items of cluster optimization configuration of unmanned aerial vehicles with different functions, wherein an objective function is to maximize the operational efficiency of the unmanned aerial vehicle cluster; and describing the cluster combat effectiveness by using the combat cost, and solving a reasonable result by using a Monte Carlo method to obtain an optimal configuration model.
2. The task demand-based collaborative optimal configuration method for the heterogeneous unmanned aerial vehicle cluster is characterized in that the step 1 is specifically to perform a comparison process between the enemy and the my on the basis of the unmanned aerial vehicle cluster combat effectiveness evaluation, and the configuration result can reflect the requirement that the unmanned aerial vehicle cluster comprehensively suppresses the enemy or meets the task demand.
3. The task demand-based heterogeneous unmanned aerial vehicle cluster cooperative optimal configuration method according to claim 2, wherein the method for evaluating the fire efficiency required by the cooperative enemy combat in step 2 comprises the following steps:
analyzing to obtain main factors influencing the required fire attack efficiency, namely the self anti-strike capability of the enemy combat unit, the killing capability of a weapon system and the urgency degree of attack time, wherein the urgency degree of time is influenced by the time of occurrence of the target and the size factor of a time sensitive window of the enemy combat unit, so that an index system of the required fire efficiency of the enemy is established;
obtaining the influence effect values of each level of the fire effect index system required by the enemy through the weight index calculation and the index quantification process on the basis of the weight index calculation
And the fire attack efficiency value required by the enemy top air defense combat unit j
The calculation formula of (a) is as follows:
in the formula: n represents the number of elements associated with the level in the performance index system, wiThe weight corresponding to the element is represented,
the influence effect value of each element in each level on the superior index is obtained;
total fire attack efficiency P required by whole enemy air defense systeme totalThe calculation formula of (a) is as follows:
wherein N represents that there are N different types of enemy air combat units in the battlefield, and NjIndicating the number, P, of each type of enemy air combat unite jRepresenting the desired fire attack efficacy value of a different type of individual enemy air combat unit.
4. The task demand-based collaborative optimal configuration method for the heterogeneous unmanned aerial vehicle cluster is characterized in that in the step 2, the evaluation method for the thermal efficiency of the heterogeneous unmanned aerial vehicle cluster in my party is as follows:
the main factors influencing the fire attack efficiency of the unmanned aerial vehicle cluster are the bomb carrying quantity of the unmanned aerial vehicle, the fire attack precision of the unmanned aerial vehicle and the fire damage force of ammunition, the synergistic influence of the unmanned aerial vehicle cluster is considered, the fire attack capability of the unmanned aerial vehicle is influenced by the detection precision of the unmanned aerial vehicle, the synergistic efficiency influence factors include the height of the aircraft, the resolution precision and the detection distance, and the early tracking and positioning efficiency of the unmanned aerial vehicle before and after the detection coordination is obtained
And
the calculation formula of (a) is as follows:
in the formula: n denotes the number of relevant elements of the hierarchy, wiThe representation elements correspond to weights, and
and
tracking and positioning an efficiency index value in an evaluation system for the attacking unmanned aerial vehicle;
the formula for calculating the synergistic effect factor is as follows:
the synergistic effect of many reconnaissance unmanned aerial vehicles of the same model to attacking unmanned aerial vehicle can superpose, and the computational formula is as follows:
in the formula, NDFor scouting the number of drones in the cluster, ε is a synergistic influence factor, I0Attack the attack accuracy of unmanned aerial vehicle for reconnaissance of cooperative influence of unmanned aerial vehicle, I1Attack precision of the unmanned aerial vehicle is attacked when the unmanned aerial vehicle is reconnaissance cooperative influence exists;
index quantification is carried out to obtain unmanned aerial vehicle firepower efficiency evaluation value
Get out firepower efficiency value P of mth level unmanned aerial vehiclea mThe calculation formula of (a) is as follows:
wherein n represents the number of related elements of the hierarchy, wiThe presentation element corresponds to a weight that,
the fire efficiency evaluation value of the unmanned aerial vehicle is obtained;
obtaining a total value P of the cluster fire efficiency of unmanned aerial vehicles at our party according to the number of unmanned aerial vehicles at our party based on expected eliminationa totalThe calculation formula of (a) is as follows:
in the formula: n represents total N attacking unmanned aerial vehicles carrying loads of different models in unmanned aerial vehicle cluster of one party，niNumber of attacking unmanned aerial vehicles carrying loads of various types, Pa iIndicating the fire efficacy value of the i-th level drone.
5. The task demand-based collaborative optimal configuration method for the heterogeneous unmanned aerial vehicle cluster is characterized in that in the step 3, the required cruising ability calculation method for the unmanned aerial vehicle cluster is as follows:
unmanned aerial vehicles except the refueling unmanned aerial vehicle are called functional unmanned aerial vehicles, and rated cruising ability EC is obtained according to the fuel carrying capacity of the functional unmanned aerial vehicle irThe calculation formula of (a) is as follows:
in the formula: i denotes unmanned plane i, OLiAnd OWiRespectively representing the oil loading and hundred kilometers oil consumption of the unmanned aerial vehicle i, and converting OWiSetting the value as a fixed value;
comparison of
Task voyage Dis with unmanned aerial vehicle iiWhen it occurs
The situation of (2) indicates that the cruising ability of the unmanned aerial vehicle is insufficient, and the calculation formula of the cruising ability REC required by the unmanned aerial vehicle cluster is obtained as follows:
in the formula: i denotes unmanned plane i, NUNumber of unmanned aerial vehicles with indication function, DisiRepresenting the mission course of drone i,
rated cruising ability, OW, for unmanned aerial vehicle iiRepresenting the hundred kilometers fuel consumption of drone i.
6. The task demand-based collaborative optimal configuration method for the heterogeneous unmanned aerial vehicle cluster is characterized in that in the step 3, the unmanned aerial vehicle cluster cruising ability calculation method is as follows:
the calculation formula of the available cruising ability AEC of the refueling unmanned aerial vehicle is as follows:
AEC＝NO·OLO
in the formula: n is a radical ofOAnd OLORespectively representing the number and the oil carrying capacity of the refueling unmanned aerial vehicles.
7. The task demand-based collaborative optimal configuration method for the cluster of heterogeneous unmanned aerial vehicles according to claim 1, wherein the step 4 comprises the steps of:
step 4.1, configuring unmanned aerial vehicles with different functions, wherein the configuration method comprises the following steps:
the probability of the investigation unmanned aerial vehicle successfully executing the task on the target is PD0Requires NDThe unmanned aerial vehicle for frame investigation executes the task to the target simultaneously to ensure that the success probability of the task reaches PDmaxAbove, then NDThe calculation formula of (a) is as follows:
in the formula: pD0Representing the probability of successful task execution of the target by the investigation drone, NDFor investigating the number of unmanned aerial vehicles, PDmaxIs NDThe success probability of simultaneously executing the tasks on the target by the frame reconnaissance unmanned aerial vehicle;
the total value of the overall fire efficiency of the unmanned aerial vehicle cluster is larger than the total value of the required fire efficiency of the enemy, and the expression is as follows:
in the formula:Penemyindicating the fire efficacy value required by the enemy,
representing the fire efficiency value of the unmanned aerial vehicle cluster of the party;
the configuration result of the refueling unmanned aerial vehicle must enable the usable cruising ability of the cluster to be larger than the cruising ability required by a battlefield, and the expression is as follows:
REC<AEC
in the formula: REC represents the required endurance of the drone cluster, AEC represents the available endurance of the drone cluster;
step 4.2, optimally configuring the nonlinear integer programming model by the cluster, which comprises the following steps:
min(costammunition+costUnmanned plane)
In the formula: costAmmunitionCost for ammunitionUnmanned planeFor unmanned aerial vehicle cost, the fuel is ignored.
8. The task demand-based collaborative optimal configuration method for the heterogeneous unmanned aerial vehicle cluster is characterized in that the weight index calculation process is based on a nine-scale scoring criterion table, an expert group scores layer by layer to construct a related element importance judgment matrix, and then a weight calculation method is used for solving a weight value between performance indexes required by an enemy.
9. The task demand-based collaborative optimal configuration method for the heterogeneous unmanned aerial vehicle cluster is characterized in that qualitative description attributes are quantized by using an improved G.AMILLER 9-level quantization theory, and quantitative description attributes are quantized by using an interval quantization method; the theory of improved g.amiller's 9-level quantization is specifically as follows: according to the actual situation of quantitative or qualitative attributes, the minimum value of the attribute is set to be 0 level or 1 level, the maximum value is set to be 9 levels with higher levels, and the minimum value and the maximum value are respectively called as a left base point and a right base point of a quantization level interval; if the boundary value of the quantization interval is given, the corresponding grade base point can be directly solved; therefore, the quantization criteria for each attribute are introduced in bottom-up order as follows:
1) index of C layer
(1) Target appearance time of enemy air combat unit
Acquiring the occurrence time of a target according to the time-sensitive windows of all enemy air defense units; the target occurrence time is differed from the unmanned aerial vehicle takeoff time, the distance between the unmanned aerial vehicle from a takeoff airport to the target is calculated by referring to parameters such as the flight speed of the unmanned aerial vehicle, the distance between the unmanned aerial vehicle and the target and the flight speed performance interval [ V ]min,Vmax]Estimated arrival time window of unmanned aerial vehicle flight to target point under restriction, wherein VmaxAnd VminThe maximum and minimum speeds of the unmanned aerial vehicle are respectively; with [ ETA ]min,ETAmax]The left and right boundary values of the window are respectively used as 9-level and 1-level base points, and the time of occurrence of each target is quantified at equal intervals, wherein ETAmaxAnd ETAminCalculating the estimated arrival time from the unmanned aerial vehicle to the target point according to the distance between the unmanned aerial vehicle and the target point and the maximum and minimum speed of the unmanned aerial vehicle; if the time-sensitive time window of the target is at ETAminPreviously, it was explained that the time-sensitive target is extremely urgent and needs to be hit preferentially; if the time sensitive window is in ETAmaxLater, the target appears later, and a certain coordination space can be reserved for the cluster in the subsequent planning process;
(2) time sensitive window size of enemy air combat unit
The time-sensitive window size reflects the task killing chain length of the target, and the smaller the time-sensitive window size is, the higher the degree of the killing chain compression is; firstly, acquiring time-sensitive windows of all enemy air defense units, comparing the sizes of all target time-sensitive windows, setting the target with the minimum window length as a right base point to be 9 grades, and setting a left base point which is not required by the time-sensitive windows to be 0 grade, and then sequentially quantizing the sizes of all target time-sensitive windows at equal intervals;
2) index of B layer
(1) Anti-strike capability of enemy air defense combat unit
Sequentially classifying the enemy combat units according to the anti-strike capacity intensity classification table;
(2) firepower killing capability of enemy air defense combat unit
Carrying out quantitative quantification on the enemy combat units in sequence according to the fire killing capability classification table;
(3) attack time urgency of enemy air combat unit
Setting a base point of the quantitative interval by estimating the maximum and minimum values, combining a task scene and according to the latest target occurrence time TlateAnd a maximum time sensitive window TSTmaxAnd calculating the minimum value of the attack time urgency degree, calculating the maximum value of the time urgency degree in the same way, and respectively using the minimum value and the maximum value as the base points of the 1-9 levels.
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