CN113792985A - Sensor-weapon dynamic joint task multi-target allocation method - Google Patents
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Abstract
The invention discloses a sensor-weapon dynamic joint task multi-target distribution method, which considers the mutual influence among different combat units and can effectively and quickly provide a plurality of command schemes for the scout task distribution and the attack task distribution of a sensor platform and a weapon platform. The method comprises the following steps: calculating targets which can be detected by the sensors and the weapons in each fighting stage, and respectively obtaining initial feasibility matrixes of the weapons and the sensors; constructing an initial population according to the initial feasibility matrix of the weapon and the sensor, carrying out constraint condition processing, and carrying out iterative search to obtain an external population; and judging whether the iterative search reaches the iteration step number, and stopping and outputting an external population if the iterative search reaches the iteration step number, namely a group of task allocation.
Description
Technical Field
The invention belongs to the technical field of intelligent control, relates to an optimized allocation technology of combat resources, and particularly relates to a sensor-weapon dynamic joint task multi-target allocation method.
Background
The task allocation is a key link of the battle command, directly influences the progress and victory or defeat of the battle, and is an important military problem for competitive research of all military strong countries. The main expression of the future war is the combined operation, a plurality of weapons can have better hit rate only by continuous guidance of the sensor platform, and the weapon platform and the sensor platform jointly implement the operation under the unified command of the combined command mechanism. The networked sensors, weapons and decision-makers can reach consensus, so that the command speed can be increased, the fighting rhythm can be accelerated, the attack destructiveness can be increased, the survival rate can be increased, and the better fighting efficiency can be realized.
In the process of allocating the combined combat tasks of the weapon platform and the sensor platform, the problems of limited sensors, the number of weapons, the combat time, the combat cost and the like need to be considered. How to perform the joint allocation of the sensor and the weapon task through an optimization algorithm makes the performance of the weapon sensor effectively exerted and the achievement of the optimal combat efficiency especially important.
Most of the prior art only considers weapon distribution and fails to consider the combined operation of weapons and sensors. For joint distribution of sensor weapons, there are certain academic researches, but structural methods are mostly adopted to perform one-time distribution of sensors and weapons, coupling among the sensor weapons cannot be considered, dynamic characteristics of actual combat cannot be considered, and a better solution cannot be obtained when the number of nodes in weapons and sensor networks is large.
Disclosure of Invention
The invention discloses a sensor-weapon dynamic joint task multi-target distribution method, which considers the mutual influence among different combat units and can effectively and quickly provide a plurality of command schemes for the detection task distribution and the attack task distribution of a sensor platform and a weapon platform.
The invention is realized by the following technical scheme.
A sensor-weapon dynamic joint task multi-target allocation method comprises the following steps:
calculating targets which can be detected by the sensors and the weapons in each fighting stage, and respectively obtaining initial feasibility matrixes of the weapons and the sensors;
constructing an initial population according to the initial feasibility matrix of the weapon and the sensor, carrying out constraint condition processing, and carrying out iterative search to obtain an external population;
and judging whether the iterative search reaches the iteration step number, and stopping and outputting an external population, namely a group of task allocation schemes, if so.
The invention has the beneficial effects that:
the invention considers the dynamic cooperation among weapon sensors and has higher fighting efficiency compared with a single fighting mode. Meanwhile, a heuristic method of comprehensive exploration, development and weight vector is adopted to initialize the population, a constraint processing method of fusion greedy strategy is adopted to carry out constraint processing, and sensors and weapon tasks which are judged to be invalid are eliminated in a self-adaptive mode, so that the solving efficiency and the solving capability of the algorithm are improved, the algorithm can be applied to the large-scale combat situation, the damage to enemy targets is effectively improved, the combat consumption is reduced, the commanding flexibility is improved, and the combat efficiency is effectively improved.
Drawings
FIG. 1 is a flow chart of a sensor-weapon dynamic joint task multi-objective assignment method of the present invention;
fig. 2 is a flowchart of one iterative search in an embodiment.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, the method for multi-target assignment of sensor-weapon dynamic joint task in this embodiment specifically includes:
step one, calculating targets which can be detected by a sensor and a weapon in each fighting stage, and respectively obtaining initial feasibility matrixes of the weapon and the sensor;
in this embodiment, the initial feasibility matrix of the weapon is represented as X ═ Xmst]M×S×TWherein x ismst∈{0,1}，xmst1 means that weapon m can attack target t in s phase; the initial feasibility matrix of the sensor is denoted as Y ═ Ynst]N×S×TWherein y isnst∈{0,1}，ynst1 means that sensor n can detect in s phaseAnd (4) target t.
Secondly, constructing an initial population according to the initial feasibility matrix of the weapon and the sensor, carrying out constraint condition processing, and carrying out iterative search to obtain an external population;
in this embodiment, the following method is adopted for constructing the initial population:
setting p individuals X ═ X1,x2,...xnAs an initial population, the individual length N is S x (M + N),
wherein S is the total number of stages, M is the total number of weapons, and N is the total number of sensors.
The construction method for each gene locus is as follows:
first, p weight vectors λ ═ w are constructed1,w2In which w1+w2＝1,
According to the weight vector λ ═ w1,w2Determine the probability of allocating a task to a combat resource:
wi＝aw1+b
si＝cw1+d
wherein, w1Representing the weight, w, of the objective function 12Representing the weight of the objective function 2, wi being the probability of allocating tasks to weapons, si being the probability of allocating tasks to sensors, a, b, c, d being preset coefficients;
then for each gene position xiCalculating whether the task needs to be allocated to the fighting resource according to wi and si, and calculating the corresponding fighting stage s and different gene positions x by the formula i/(M + N) when the task needs to be allocated to the fighting resourceiThe corresponding combat resource may be calculated using the following equation:
wherein i represents the gene locus xiSubscript of (1), mi％(M+N)Denotes the x% (M + N) th weapon, Ni％(M+N)-M tableThe ith% (M + N) -M sensors are shown.
When the combat resource is calculated to be a weapon, according to the corresponding weapon and the combat stage and according to the feasibility matrix X in the step one, the targets which can be attacked by the weapon in the combat stage can be obtained, and a feasible target is randomly selected as an allocation result. When the calculated fighting resources are sensors, according to corresponding weapons and fighting stages and in the feasibility matrix Y in the step one, targets which can be detected by the sensors in the fighting stages can be obtained, and a feasible target is randomly selected as an allocation result.
The preliminary individual X is calculated in the steps, but in the specific implementation, when the generated individuals cannot meet all constraint conditions and cannot be combined with the coupling of the weapon and the sensor, the weapon can successfully hit a target only by being guided by the sensor for a period of time, and the weapon cannot be continuously fired, so that the individual is required to be subjected to constraint condition processing; and (4) carrying out constraint condition processing on the individual, and setting a weapon not to execute the task at the stage when the previous stage of the weapon is assigned with the task or is not continuously guided by the sensor. Thereby finally constructing an initial population.
As shown in fig. 2, in this embodiment, the steps of one iterative search are as follows:
step 1, crossing: randomly selecting two individuals x from TjAnd xkRandomly selecting continuous n gene sites to generate a new individual x in a crossed manner, and performing heuristic constraint processing of a fusion greedy algorithm on the x;
step 2, mutation: carrying out single-locus mutation on each individual in the population, firstly randomly selecting a locus, calculating a corresponding operation stage and an operation unit according to the position, selecting a feasibility target from the feasibility matrix, setting the probability of pi in the mutation process without distributing the target, wherein pi is a preset coefficient, preferably 0.3-0.5, and finally carrying out constraint processing on a new individual x by adopting a heuristic method of a fusion greedy algorithm;
in this embodiment, the heuristic constraint processing based on the fusion greedy algorithm adopts the following method:
a) for the weapon fire-turning constraint, setting the probability of adopting a greedy strategy as pg, wherein the calculation formula is pg-I/(I + I), I is the current iteration frequency, and I is the total iteration frequency; when a certain weapon w violates the fire-turning constraint, eliminating the constraint conflict; and respectively calculating the weighted sum of each objective function under different conditions by adopting a greedy strategy, and adopting a mode of maximizing the weighted sum. In specific implementation, if a greedy strategy is not adopted, any mode is randomly adopted.
The constraint conflict elimination can adopt the following three ways: the method comprises the steps of firstly, performing tasks on the W weapon in the previous stage, secondly, performing tasks on the W weapon in the current stage, and thirdly, performing tasks on the W weapon in neither stage.
b) When weapons and sensors fail to meet continuous guidance constraints, feasible idle sensors are randomly selected in the feasibility matrix to assign tasks to meet guidance constraints without changing sensors already assigned to tasks, and if no feasible idle sensors exist, the assigned weapons task fails.
Step 3, optimizing the individuals, eliminating invalid combat missions with probability, and obtaining new individuals x;
the invalid combat missions in this embodiment include the following two types: one is that the target has already been assigned more weapons, at which time no weapons are assigned to the target; second, the tracking of the target by the sensor fails to successfully direct the weapon.
And 4, updating, namely updating the worst reference point z (z) of all the optimization targets j of {1,2} for the generated new individual x1,z2) Wherein z is1Minimum f in individuals representing the first optimization objective1(x)，z2Minimum f in individuals representing the second optimization objective2(x) If f isj(x)＜zjSet up zj＝fj(x)。
And 5, updating a neighborhood solution and updating a neighborhood range, namely, a new individual x' generated by crossing, mutating or optimizing the individual corresponding to the vector lambda is subjected to all other individuals x in the neighborhoodi(its corresponding weight vector λ)i) For all targets j ═ 1,2},if it is not
Then x is setiX', where i is the current algebra;
and 6, updating the external population EP according to the congestion degree relation, namely removing all the individuals dominated by x 'from the EP for each new individual x', and if no individual dominates x 'in the EP, keeping the Euclidean distance from the individual x' to be not less than dmin，dminFor a self-set minimum distance, then x' is added to the EP.
And step three, judging whether the iterative search reaches the iteration step number, and stopping and outputting the external population if the iterative search reaches the iteration step number.
According to the above steps, it can be seen that the external population contains a plurality of individuals, each individual representing a solution of task assignment, and thus the external population is a group of solutions, i.e., a group of task assignment methods.
Example 1:
the battle scene aimed at by the embodiment is specifically as follows: the defense party deploys a plurality of combat platforms, the combat is divided into S stages, and the weapon platform set is W ═ W1,w2,...wMThe set of sensor platforms is S ═ sen1,sen2,...senNAnd the sensor unit and the weapon unit can communicate with each other, T airplanes are arranged at an attack side, and the target is breakthrough defense. The defense party needs to perform two tasks of detecting and tracking a plurality of attacking targets of the enemy party and target hitting. The sensor unit is responsible for detecting and capturing tasks, and when an enemy target is successfully detected and guided, the weapon unit strikes the tracked and captured enemy target. The optimization goal is to eliminate the target threat maximally and minimize the battle resource consumption, and the objective function is f1(x) And f2(x) In that respect Sensor weapon complex now the weapon needs sensor platform's continuous guide to have better hit rate, need to guarantee sensor platform's multiple stage to the tracking of target, will weapon guide target, weapon unit itself need satisfy the turn fire constraint, after a period of time before assign to a given target, current stage need be afterSegments cannot be assigned to other targets.
For the battle scene, the method of the invention is described in detail according to the specific implementation steps as follows:
step 1, setting an external population EP as an empty set for storing an optimal solution. Generating a group of weight vectors which are uniformly distributed in a two-dimensional space, calculating Euclidean distance between any two weight vectors, taking T weight vectors which are nearest to each weight vector as neighborhood vectors according to the Euclidean distance, and collecting the weight vectors as TV ═ lambda1,λ2...λT}。
Step 2, calculating targets which can be detected by the sensors and the weapons in each fighting stage through the predicted target tracks, and obtaining initial feasibility matrixes of the weapons and the sensors, wherein the initial feasibility matrix of the weapons is represented as X ═ Xmst]M×S×TWherein x ismst∈{0,1}，xmst1 indicates that the weapon can attack the target t in the s phase, and similarly, the initial feasibility matrix of the sensor is represented as Y ═ Ynst]N×S×TWherein y isnst∈{0,1}，ynst1 means that the sensor can detect the target t in s phase.
And 3, constructing an initial population by adopting a heuristic method considering weight influence. Individuals in the population are set to pi. For each individual, the construction is made by the following steps.
And 3.1, setting the length of each individual to be S x (M + N), wherein S is the number of operation stages, M is the total number of weapons, and N is the total number of sensors. The integer t is adopted for coding, different integers t represent different enemy targets, wherein partial positions represent that a weapon m is allocated to the target t or not allocated to a task at the stage s, and partial positions represent that a sensor n is allocated to the target t or not allocated to the task at the stage s, and the calculation method comprises the following steps:
for gene position xiThe corresponding fighting stage is i/(M + N), and the corresponding fighting resource is calculated by the following formula:
wherein i represents the gene locus xiSubscript of (1), mi％(M+N)Denotes the x% (M + N) th weapon, Ni％(M+N)-MRepresenting the ith% (M + N) -M sensors.
And (3) initializing according to the corresponding operation stage and operation resources (weapons or sensors) of each gene position, the weight vector and the feasibility matrix established in the step (2).
Step 3.2, according to the weight vector lambda, w ═ w1,w2Determining the probability of assigning a task to a combat resource, where x1Weight, x, representing the goal of maximum elimination of the target threat2Representing the weight of minimizing the battle resource consumption goal. For tasks that are more in need of targeted threat mitigation, a higher probability of assigning tasks is set at initial assignment. Setting the probability of assigning tasks to weapons as wi and the probability of assigning tasks to sensors as si, preferably, the task assignment probability is set according to the following formula:
wi＝aw1+b
si＝cw1+d
wherein a, b, c and d are coefficients, preferably, c and d are set to be 1.5 times of a and b, b is 0.1, and a is determined by dividing the total number of effective distribution in the weapon feasibility table by 3-7 times of the total number of targets.
And (3) after the corresponding fighting resources and the fighting stage are calculated, randomly selecting a feasible target as an allocation result according to the feasibility matrix established in the step (2).
And 3.3, the individuals generated through the steps 3.1 and 3.2 cannot meet all constraint conditions and cannot consider continuous guidance constraint and weapon fire turning constraint, so that the generated individuals need to be subjected to constraint condition processing, and when a certain weapon is assigned with a task in the previous stage or is not continuously guided by a sensor, the weapon is set not to execute the task in the stage.
Repeatedly executing the above operations to obtain an initial population Pini。
Step 4, setting an initial reference point zini＝{z1,z2To the initial population PiniCalculating a fitness function of each target of each individual in the current generationValue of then
Obtaining an initial population PiniReference point z of0。
The following steps 5 to 10 are taken as an iterative process, and a current algebra is set as i:
and 5, crossing. Randomly selecting two individuals x from TjAnd xkRandomly selecting continuous n gene sites to generate a new individual x in a crossed manner, and carrying out heuristic constraint processing of a fusion greedy algorithm on x.
And 5.1, setting the probability of adopting a greedy strategy as pg for weapon fire-turning constraint, preferably, setting a calculation formula of pg as I/(I + I), wherein I is the current iteration number, and I is the total iteration number. When a weapon w violates the fire-turning constraint, there are three ways to resolve the constraint conflict, namely, w weapon does not perform the task in the previous stage, w weapon does not perform the task in the current stage, and w weapon does not perform the task in both stages. When a greedy strategy is adopted, the weighted sum of the objective functions under three conditions is calculated respectively, and a method for maximizing the weighted sum is adopted. When the greedy strategy is not adopted, the method is adopted randomly.
And 5.2, when the weapon and the sensors fail to meet the continuous guidance constraint, randomly selecting feasible idle sensors in the feasibility matrix to distribute the tasks to meet the guidance constraint under the condition of not changing the sensors which are distributed with the tasks, and if the feasible idle sensors do not exist, disabling the distributed weapon tasks.
And 6, mutation. For single-locus mutation of each individual in the population, firstly, randomly selecting a locus, calculating a corresponding operation stage and an operation unit according to the position, selecting a feasibility target from a feasibility matrix, setting the probability of p in the mutation process to not distribute the target, preferably, p is generally 0.3-0.5, and finally, carrying out constraint processing on a new individual x generated after mutation by adopting the constraint processing method in the step 5.
And 7, optimizing the individuals, and eliminating invalid combat missions with probability, wherein the combat missions comprise the following two types: a target has more weapons assigned to it, at which time weapons are assigned to it; when the tracking of the target by the sensor fails to successfully direct the weapon. The probability of elimination Pe is set, preferably,
and for invalid combat missions, setting the missions to be invalid according to the probability Pe, and obtaining a new individual x through optimization operation.
And 8, updating the reference point z. For the new individual x generated, for all task objectives j ═ 1,2, if objective function fj(x)＜zjSet up zj＝fj(x)。
And 9, updating the neighborhood solution and updating the neighborhood range. New individual x' generated by crossing, mutation or optimization for the individual corresponding to vector λ for all other individuals x in the neighborhoodi(its corresponding weight vector λ)i) For all targets j ═ 1,2, if
Then x is seti＝x’。
And step 10, updating the external population EP according to the congestion degree relation. For each new individual x ', all individuals dominated by x' are removed from the EP. If no individual dominates x 'in EP, and the Euclidean distance from the individual x' is not less than dmin，dminFor a self-set minimum distance, then x' is added to the EP.
And 11, judging whether the iteration step number is reached, and stopping and outputting the EP as an optimal distribution scheme set if the iteration step number is reached. Otherwise, returning to the step 5 for next iteration.
Through the steps, the method takes the weapon sensor feasibility matrix as an input variable of an optimization algorithm, and considers the maximization of eliminating the target threat and the minimization of the battle resource consumption as an optimization objective function. On the basis of a multi-objective optimization algorithm based on decomposition, an initialization method is improved, an initial individual is constructed by a heuristic method considering weight vectors, the individual is optimized in an iteration process, invalid combat tasks are reduced, an adaptive greedy method is adopted for processing constraint conditions, and an adaptive field matching strategy is adopted. The improved method considers the coupling characteristics of sensor weapons and the dynamic property of battles, improves the searching capacity of the algorithm by optimizing the population initialization, the constraint condition processing strategy and the invalid individuals, avoids the situation of local optimization possibly occurring in the traditional decomposition algorithm when the battle scale is large, and can still obtain an excellent solution set under the large-scale situation.
In summary, the above description is only a preferred example of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A sensor-weapon dynamic joint task multi-target allocation method is characterized by comprising the following steps:
calculating targets which can be detected by the sensors and the weapons in each fighting stage, and respectively obtaining initial feasibility matrixes of the weapons and the sensors;
constructing an initial population according to the initial feasibility matrix of the weapon and the sensor, carrying out constraint condition processing, and carrying out iterative search to obtain an external population;
and judging whether the iterative search reaches the iteration step number, and stopping and outputting an external population if the iterative search reaches the iteration step number, namely a group of task allocation.
2. The sensor-weapon dynamic joint task multi-target allocation method of claim 1, wherein the initial feasibility matrix of the weapon is represented as X ═ Xmst]M×S×TWherein x ismst∈{0,1}，xmst1 means that weapon m can attack target t in s phase; the initial feasibility matrix of the sensor is denoted as Y ═ Ynst]N×S×TWherein y isnst∈{0,1}，ynst1 means that the sensor n can detect the target t in s phase.
3. The sensor-weapon dynamic joint task multi-target allocation method according to claim 2, wherein the initial population is constructed by the following method:
setting p individuals X ═ X1,x2,...xnTaking the length N of each individual as S x (M + N) as an initial population, wherein S is the total number of stages, M is the total number of weapons, and N is the total number of sensors;
3.1 construct p weight vectors λ ═ w1,w2In which w1+w2＝1,
3.2 determining the probability of allocating tasks to combat resources according to the weight vector:
wi＝aw1+b
si＝cw1+d
wherein, w1Representing the weight, w, of the objective function 12Representing the weight of the objective function 2, wi being the probability of allocating tasks to weapons, si being the probability of allocating tasks to sensors, a, b, c, d being preset coefficients;
3.3 for each gene locus xiCalculating whether the task needs to be allocated to the fighting resource according to wi and si, and calculating the corresponding fighting stage s and different gene positions x by the formula x/(M + N) when the task needs to be allocated to the fighting resourceiThe corresponding combat resource may be calculated using the following equation:
wherein i represents the gene locus xiSubscript of (1), mi％(M+N)Denotes the x% (M + N) th weapon, Ni％(M+N)-MRepresenting the ith% (M + N) -M sensors.
3.4 when the calculated combat resources are weapons, acquiring targets which can be attacked by the weapons in the combat stage according to the corresponding weapons and the combat stage and the feasibility matrix X, and randomly selecting a feasible target as a distribution result; when the calculated fighting resources are sensors, according to corresponding weapons and fighting stages and the feasibility matrix Y, the targets which can be detected by the sensors in the fighting stages are obtained, and a feasible target is randomly selected as an allocation result.
4. The sensor-weapon dynamic joint task multi-object assignment method of claim 3, wherein a weapon is set to not perform a task at a stage when the weapon has been assigned a task at a previous stage or is not continuously directed by the sensor.
5. The sensor-weapon dynamic joint task multi-target allocation method according to claim 3 or 4, wherein one iterative search step is as follows:
step 1, crossing: randomly selecting two individuals x from TjAnd xkRandomly selecting continuous n gene sites to generate a new individual x in a crossed manner, and performing heuristic constraint processing of a fusion greedy algorithm on the x;
step 2, mutation: carrying out single-locus mutation on each individual in the population, firstly randomly selecting a locus, calculating a corresponding operation stage and an operation unit according to the position, selecting a feasibility target from the feasibility matrix, setting the probability of pi in the mutation process to not distribute the target, wherein pi is a preset coefficient, and finally carrying out constraint processing on a new individual x by adopting a heuristic method of a fusion greedy algorithm;
step 3, optimizing the individuals, eliminating invalid combat missions with probability, and obtaining new individuals x;
and 4, updating, namely updating the worst reference point z (z) of all the optimization targets j of {1,2} for the generated new individual x1,z2) Wherein z is1Minimum f in individuals representing the first optimization objective1(x)，z2Representing a second optimization objectiveIs smallest in the individual of (1)2(x) If f isj(x)＜zjSet up zj＝fj(x)；
And 5, updating a neighborhood solution and updating a neighborhood range, namely, a new individual x' generated by crossing, mutating or optimizing the individual corresponding to the vector lambda is subjected to all other individuals x in the neighborhoodiFor all targets j ═ 1,2, if
Then x is setiX', where i is the current algebra;
and 6, updating the external population EP according to the congestion degree relation, namely removing all the individuals dominated by x 'from the EP for each new individual x', and if no individual dominates x 'in the EP, keeping the Euclidean distance from the individual x' to be not less than dmin，dminFor a self-set minimum distance, then x' is added to the EP.
6. The sensor-weapon dynamic joint task multi-target allocation method according to claim 5, wherein the preset coefficient pi is 0.3-0.5.
7. The sensor-weapon dynamic joint task multi-target allocation method as claimed in claim 5, wherein the heuristic constraint processing of the fusion greedy algorithm adopts the following mode:
a) for the weapon fire-turning constraint, setting the probability of adopting a greedy strategy as pg, wherein the calculation formula is pg-I/(I + I), I is the current iteration frequency, and I is the total iteration frequency; when a certain weapon w violates the fire-turning constraint, eliminating the constraint conflict; respectively calculating the weighted sum of each objective function under different conditions by adopting a greedy strategy, and adopting a mode of maximizing the weighted sum;
b) when weapons and sensors fail to meet continuous guidance constraints, feasible idle sensors are randomly selected in the feasibility matrix to assign tasks to meet guidance constraints without changing sensors already assigned to tasks, and if no feasible idle sensors exist, the assigned weapons task fails.
8. The sensor-weapon dynamic joint task multi-objective allocation method according to claim 7, wherein said constraint conflict elimination can be implemented in three ways: the method comprises the steps of firstly, performing tasks on the W weapon in the previous stage, secondly, performing tasks on the W weapon in the current stage, and thirdly, performing tasks on the W weapon in neither stage.
9. The sensor-weapon dynamic joint task multi-objective allocation method of claim 5, wherein the invalid combat tasks include two of: one is that the target has already been assigned more weapons, at which time no weapons are assigned to the target; second, the tracking of the target by the sensor fails to successfully direct the weapon.
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