Guidance and firepower resource dynamic allocation method for air defense combat

Abstract

The invention discloses a guidance and firepower resource dynamic allocation method for air-defense combat, which is used for realizing effective utilization and efficient strike of our firepower resource and guidance resource in an air-defense combat scene. According to the method, elimination of target threat degrees as much as possible is taken as an optimization target, and actual factors such as ammunition constraint, weapon concurrency capability constraint, enemy target attack upper limit constraint, radar detectable constraint and weapon strikeability constraint in actual combat are fully considered at the same time. A heuristic algorithm is designed, and the process of generating an allocation scheme is influenced by constructing a decision function considering distance, time and resources. An improved particle swarm algorithm is designed, a compression factor method is adopted to improve the convergence speed of the algorithm, a crossover variation mechanism is introduced, and the optimization result of the algorithm is the weight of a heuristic algorithm decision function. According to the invention, the scheduling and utilization problem of our combat resources in an air defense combat scene can be effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of weapon target allocation, and aims at the guidance and firepower resource allocation problem in the air defense combat scenario. A solution method based on an improved particle swarm optimization algorithm to optimize the heuristic algorithm for generating strategies is adopted to realize the full utilization and efficient strike of our combat resources in the dynamic battlefield environment, and the algorithm has a fast convergence speed and high solution quality. Background Technique

[0002] The weapon target allocation (abbreviated as WTA) problem is an important topic in military operations research. The core problem of WTA is how to allocate weapons with different lethality and economic values to different targets to form an overall optimal fire strike system. Further considering the sensor problem based on the WTA problem is called the sensor - weapon - target allocation problem (S - WTA). Compared with the WTA problem, the S - WTA problem is more in line with the actual combat scenario. It has been proved that the S - WTA problem is an NP - complete problem. With the increase in the number of attacking party targets and our resources, the solution space will increase exponentially.

[0003] Currently, the methods for solving the S - WTA problem mainly adopt intelligent optimization methods and heuristic algorithms. Intelligent optimization algorithms mainly include genetic algorithms, ant colony algorithms, simulated annealing algorithms, etc. These algorithms have problems such as slow convergence speed and population premature convergence. The heuristic algorithm guides the generation of solutions by designing solution rules, with fast solution speed but unable to guarantee the optimality of the solutions. In addition, the existing solution algorithms usually only consider the kill probability of weapons against targets and the detection probability of radars or sensors against targets without considering other actual factors. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: to overcome the deficiencies of the prior art, and propose a dynamic allocation method for guidance and firepower resources for air defense combat. This method is a solution method based on an improved particle swarm optimization algorithm to optimize the heuristic algorithm for generating strategies, with the optimization goal of eliminating as many target threat degrees as possible, and at the same time considering ammunition constraints, weapon concurrency ability constraints, upper limit constraints on the strike of enemy targets, constraints on whether the radar can detect, and weapon strikeability constraints.

[0005] The technical solution of the present invention is as follows:

[0006] A dynamic allocation method for guidance and firepower resources for air defense combat, the steps of this method include:

[0007] Step 1, use the TOPSIS method to evaluate the threat degree of all incoming targets of the attacking party. When evaluating, consider three factors: target type, target speed, and target distance, obtain the threat degree evaluation result, and select the target with the highest threat degree according to the obtained threat degree evaluation result;

[0008] Step 2, screen the detectable radar set. The specific method is as follows: Calculate the Euclidean distance between the target with the highest threat level selected in Step 1 and all radars, and use the calculated Euclidean distance as the standard for screening detectable radars. Form the detectable radar set with all detectable radars that meet the screening criteria;

[0009] Step 3, screen the strikeable weapon set. The specific method is as follows: Use the current position and speed of the target with the highest threat level selected in Step 1, the positions of all weapons, and the speed of the missiles to calculate the encounter points of the missiles launched by all weapons with the target with the highest threat level. Let the distance between the encounter point and the weapon be d1, and the range of the missile launched by the weapon be d2; when d1 < d2 and the weapon still has unlaunched missiles, add the weapon to the strikeable weapon set, otherwise do not add it;

[0010] Step 4, form a set of feasible triples with the detectable radar set screened in Step 2, the strikeable weapon set screened in Step 3, and the target with the highest threat level. Construct a decision function to evaluate all triples in the set of feasible triples, select the optimal triple, and use the weapon in the selected optimal triple to strike the target in the triple. After the strike is completed, enter Step 1 until any one of the conditions of running out of missiles in the weapon, saturation of the target strike upper limit constraint, and saturation of the weapon strike upper limit constraint is met, and complete the dynamic allocation of guidance and firepower resources for air defense operations.

[0011] In the above-mentioned Step 1, the method for threat assessment of all incoming targets of the attacking party using the TOPSIS method is specifically as follows:

[0012] Step 1.1, judge the current target type. It is stipulated that the threat level of target type 1 is greater than that of target type 2, and target type 2 is greater than target type 3. It is set that the faster the target speed, the greater the threat level, and the closer the target distance (the target distance is the distance between the target and the defense command center, abbreviated as target distance) to the defense command center, the greater the threat level. Record the current target type, target speed, and target distance and store this data in a matrix to obtain a data matrix;

[0013] Step 1.2, perform standardization processing on all elements (target type, target speed, target distance) in the data matrix obtained in Step 1.1 to ensure the same dimension of the three factors, and positiveize the target distance index to meet the rule requirements;

[0014] Step 1.3, respectively obtain the maximum and minimum values of the target type, target speed, and target distance after the standardization processing in Step 1.2, to obtain the target type maximum value, target type minimum value, target speed maximum value, target speed minimum value, target distance maximum value, and target distance minimum value;

[0015] Calculate the distance D1 between the target type in the i-th target and the maximum value of the target types;

[0016] Calculate the distance D2 between the target type in the i-th target and the minimum value of the target types;

[0017] Calculate the distance D3 between the target speed in the i-th target and the maximum value of the target speeds;

[0018] Calculate the distance D4 between the target speed in the i-th target and the minimum value of the target speeds;

[0019] Calculate the distance D5 between the target speed in the i-th target and the maximum value of the target distances;

[0020] Calculate the distance D6 between the target speed in the i-th target and the minimum value of the target distances;

[0021] Then the threat assessment result of the i-th target is N is the number of all incoming targets of the attacking party;

[0022] Wherein,

[0023] In the said step 4, all feasible triples are evaluated by constructing a decision function, and the method for constructing the decision function is:

[0024] 3.1, Construct a decision function to sort the generated set of triples. Based on the principles of intercepting as soon as possible, intercepting as far as possible, and saving resources, the decision function considers the following three factors:

[0025] The distance between the encounter point and the command center is relatively far;

[0026] The distance for the target to fly to the encounter point is relatively close;

[0027] The remaining ammunition of the weapon is relatively large;

[0028] For all feasible triples, the following calculations are performed for each pair of triples in the set of feasible triples:

[0029] Calculate and normalize the distance D between the encounter point and the defense command center z ;

[0030]

[0031] Where d zmin is the closest distance between the encounter points of all triples in the set of feasible triples and the command center, and d zmax is the farthest distance between the encounter points of all triples in the set of feasible triples and the command center;

[0032] Calculate and normalize the time Te for the target to fly to the encounter point;

[0033]

[0034] where t emin is the minimum time for the target to fly to the encounter point, and t emax is the maximum time for the target to fly to the encounter point;

[0035] 3) Calculate and normalize the remaining number of missiles W of the weapon i ;

[0036] W i = w i / w

[0037] w i is the remaining number of missiles of all triples in the set of feasible triples, and w is the maximum number of missiles of the weapon;

[0038] The decision function is:

[0039] J = w1D z + w2Te + w3W i

[0040] The larger the calculated J, the higher the priority of the feasible triple. Select the triple with the largest J value as the optimal triple, where (w1, w2, w3) are the corresponding normalized weights, used to measure the importance of the three decision criteria;

[0041] In step 4 described above, an improved particle swarm optimization algorithm is used to optimize the weights of the decision function, so as to optimize the strike effect of the allocation scheme.

[0042] The method for optimizing the weights of the decision function is:

[0043] The improved particle swarm optimization algorithm takes each weight (w1, w2, w3) as a variable to be optimized, and regards each weight (w1, w2, w3) as a particle in the solution space. The fitness function value of the improved particle swarm optimization algorithm is as follows:

[0044]

[0045] where x ij is the decision variable, indicating that the i-th weapon strikes the j-th target, and p ij is the kill probability, indicating the kill probability of the i-th weapon against the j-th target;

[0046] In the process of improving the evolutionary process of the particle swarm algorithm, in order to improve the convergence speed of the particle swarm algorithm and avoid the phenomenon of population premature convergence, the compression factor method is adopted to enhance the convergence speed of the algorithm. The positions and velocities of the particles are updated using the traditional particle swarm update formulas with a certain probability, and at the same time, the current position is crossed with its historical optimal position with a certain probability for updating, and the multi-point random crossover method is used for the crossover. Each weight is normalized and its value range is restricted between 0 and 1. This process is an optimization solution process, and the optimized weights are (w1, w2, w3). Ultimately, the strike effect of the entire allocation plan is optimized. The termination condition of the algorithm is that the number of iterations reaches the set requirement.

[0047] Beneficial effects

[0048] (1) The present invention provides a method for dynamic allocation of guidance and firepower resources for air defense operations. This method aims to eliminate as many threat levels of the attacking party as possible as the optimization goal, and guides the generation of solutions through heuristic rules. At the same time, it takes into account the ammunition constraints, weapon concurrency ability constraints, upper limit constraints on the strike of enemy targets, constraints on whether the radar can detect, and weapon strikeability constraints in actual combat, significantly improving the solution efficiency and quality.

[0049] (2) The present invention designs a weight for an improved particle swarm algorithm to optimize the heuristic algorithm for constructing an allocation plan. Through the cross-mutation mechanism and the compression factor method, the premature phenomenon of the particle swarm algorithm is avoided, and the objective function is used as the fitness function of the particle swarm algorithm to optimize the strike effect of the entire allocation plan.

[0050] (3) The present invention discloses a method for dynamic allocation of guidance and firepower resources for air defense operations, which is used to effectively utilize and efficiently strike our firepower resources and guidance resources in the air defense operation scenario. The present invention aims to eliminate as many target threat levels as possible as the optimization goal, and at the same time fully considers actual factors such as ammunition constraints, weapon concurrency ability constraints, upper limit constraints on the strike of enemy targets, constraints on whether the radar can detect, and weapon strikeability constraints in actual combat. A heuristic algorithm is designed to affect the process of generating an allocation plan by constructing a decision function considering distance, time, and resources. An improved particle swarm algorithm is designed, and the compression factor method is used to enhance the convergence speed of the algorithm, and the cross-mutation mechanism is introduced. The optimization result of the algorithm is the weight of the decision function of the heuristic algorithm. The present invention can effectively solve the problem of scheduling and utilization of our combat resources in the air defense operation scenario. Brief description of the drawings

[0051] Figure 1 Schematic diagram for screening detectable radars;

[0052] Figure 2 Schematic diagram for calculating encounter points;

[0053] Figure 3 Flow chart for constructing an allocation plan through a heuristic algorithm;

[0054] Figure 4 Flow chart for constructing an allocation plan by means of a method for optimizing the weight of a decision function through an improved particle swarm algorithm. Detailed implementation manners

[0055] The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0056] The present invention provides a dynamic allocation method for guidance and firepower resources for air defense operations. The present invention provides a dynamic allocation method for guidance and firepower resources for air defense operations. This method takes eliminating as many threat degrees of the attacking party as possible as the optimization goal and guides the generation of solutions through heuristic rules. Many constraints in the actual combat process are fully considered. An improved particle swarm algorithm is designed to optimize the weight of the heuristic algorithm for constructing an allocation plan. Through a crossover and mutation mechanism and a constriction factor method, the premature phenomenon of the particle swarm algorithm is avoided, and the objective function is used as the fitness function of the particle swarm algorithm to optimize the strike effect of the entire allocation plan.

[0057] A dynamic allocation method for guidance and firepower resources for air defense operations includes the following steps:

[0058] Step 1, threat assessment of incoming targets of the attacking party: The TOPSIS method is a method for ranking according to the proximity of a finite number of evaluation objects to an ideal target, and it is to evaluate the relative advantages and disadvantages among existing objects. It is a commonly used and effective method in multi-objective decision-making analysis. Therefore, the present invention selects the TOPSIS method to evaluate the targets of the attacking party. When using the TOPSIS method for threat assessment, the following three factors are mainly considered: target type, target speed, and target distance.

[0059] Step 2, screening detectable radars: According to the threat degree evaluation result in Step 1, select the target with the largest threat degree among them, denoted as target j, and allocate radars to target j. First, calculate the distance between this target and all radars according to the current position of target j, and the calculation method selects the Euclidean distance. Denote it as D1. D1 = (d 1j , d 2j , d 3j ,..., d lj ), where l is the number of radars. Let the detectable radius of each radar be r l . If r l < d lj , then radar l can detect target j and add radar l to the set of feasible radars. If the feasible set of radars is empty, set the threat degree of this target to 0 and skip target j for the next cycle.

[0060] Step 3. Screen available weapons: First, based on the current position (x j , y j , z j ) of the target and the position (x i , y i , z i ) of weapon i, as well as the current speed v j of the target and the flight speed v d of the missile, calculate the encounter point of the missile and the target, and record the position of the encounter point as E. Calculate the Euclidean distance d ie from weapon i to the encounter point E. Denote the strike distance of weapon i as d i . If d ie < d i , and weapon i has remaining ammunition, then add weapon i to the set of feasible weapons. If the feasible set is empty, set the threat level of this target to 0, skip target j, and proceed to the next loop.

[0061] Step 4. Construct the optimal triple for a single target: The generated triple needs to include three elements: radar, weapon, and target. According to the set of detectable radars and the set of available weapons screened in Step 2 and Step 3, generate the set of all radar - weapon - target for target j, denoted as the feasible triple. Construct a decision function to sort the generated triple set. Based on the principles of intercepting as soon as possible, intercepting as far as possible, and saving resources, construct a decision function to evaluate all triples, select the optimal triple, and update the weapon ammunition constraint and the threat level of target j.

[0062] Step 5. Construct the allocation plan: Repeat Step 2, 3, and 4 until the constraints of ammunition constraint, weapon concurrency ability constraint, and the upper limit of enemy target strikes reach saturation.

[0063] Step 6. Optimize the weights through an improved particle swarm algorithm: Although the allocation plan generated by the heuristic rule has a fast generation speed, the strike effect of the generated allocation plan may not be good. The particle swarm algorithm has the defect of being prone to premature convergence. Therefore, the present invention uses an improved particle swarm algorithm to optimize the weights for evaluating triples by the heuristic algorithm, and uses the killing effect of the allocation plan constructed by the heuristic algorithm as the fitness function to optimize the strike effect of the allocation plan.

[0064] Embodiment

[0065] As shown in Table 1, the incoming targets of the attacking party are as shown in Table 1;

[0066]

[0067]

[0068] The steps for threat assessment of the incoming targets of the attacking party are as follows:

[0069] Step 1. Threat assessment of the attacking party's incoming targets: Therefore, the present invention selects the TOPSIS method to evaluate the attacking party's targets. When using the TOPSIS method for threat assessment, the following three factors are mainly considered: target type, target speed, and target distance. Three rules are adopted to measure the initial elements in the TOPSIS method:

[0070] The threat level of 4 types of targets is greater than that of 3 types of targets, which is greater than that of 2 types of targets, which is greater than that of 1 type of targets.

[0071] The higher the target speed, the greater the target threat level. In the present invention, the target speed is used as an element in the TOPSIS method.

[0072] The closer the target distance, the greater the target threat level. In the present invention, the Euclidean distance between the target and the defense command center is used to calculate the target distance, and the distance data is normalized to meet the rule requirements.

[0073] As Figure 1 shown, the steps for screening the detectable radar set are as follows:

[0074] Step 2. Screening the detectable radar: According to the threat level evaluation result in Step 1, select the target with the greatest threat level, denoted as target j, and allocate the radar to target j. First, calculate the distance between this target and all radars based on the current position (x j , y j , z j ) of target j. Denote the position of radar l as (x l , y l , z l ), and the calculation method selects the Euclidean distance. Denote it as D1. D1 = (d 1j , d 2j , d 3j ,..., d lj ), where l is the number of radars. Assume the detectable radius of each radar is r l . If r l < d lj , then radar l can detect target j and add radar l to the feasible radar set. If the feasible set of radars is empty, skip target j and perform the next loop.

[0075] As Figure 2 shown, the steps for calculating the encounter point between the missile and the target and screening the available weapon set are as follows:

[0076] Step 3. Screening the available weapons: First, based on the current position (x j , y j , z j ) of the target and the position of weapon i (xi ,y i , z i ) and the target's current speed v j , the missile's flight speed v d Calculate the encounter point between the missile and the target, and record the position of the encounter point as E. According to the spatial geometric relationship:

[0077]

[0078] Among them, x e ,y e , z e is the coordinate of the encounter point in x, y, z components, v jx , v jy , v jz is the velocity of target j in x, y, and z components, and t is the flight time of target j. The Euclidean distance from weapon i to the encounter point E is d ie ,but From the conditions, we know that d ie Equal to the distance v that the missile flies in time t d t, the missile flight time t and the Euclidean distance d from weapon i to the encounter point E can be obtained immediately. ie . Let the attack distance of weapon i be d i , if d ie <d i , and weapon i has remaining ammunition, then weapon i is added to the feasible weapon set. If the feasible set is empty, skip target j and proceed to the next cycle.

[0079] Step 4: Construct the optimal triplet for a single target: The generated triplet needs to include three elements: radar, weapon, and target. Based on the detectable radar set and the strikeable weapon set selected in steps 2 and 3, generate a set of all radars-weapons-targets for target j, that is, a set of triples. Construct a decision function to sort the generated triplet set. Based on the principles of intercepting as quickly as possible, intercepting as far as possible, and saving resources, the decision function considers the following three factors:

[0080] The encounter point is far away from the command center.

[0081] The target flies a shorter distance to the encounter point.

[0082] Weapons have more ammo remaining.

[0083] For all feasible triples, the following calculation is performed for each pair of triples in the set of feasible triples:

[0084] Calculate and normalize the distance D between the first encounter point and the command center z ;

[0085]

[0086] where d zmin is the shortest distance from the encounter point of all triples in the set of feasible triples to the command center, and d zmax is the longest distance from the encounter point of all triples in the set of feasible triples to the command center. Calculate and normalize the time Te for the target to fly to the encounter point;

[0087]

[0088] where t emin is the minimum time for the target to fly to the encounter point, and t emax is the maximum time for the target to fly to the encounter point. 3) Calculate and normalize the remaining ammunition quantity W of the weapon i ;

[0089] W i = w i / w

[0090] w i is the remaining ammunition quantity of the weapon for all triples in the set of feasible triples, and w is the maximum ammunition quantity of the weapon;

[0091] Construct the decision function:

[0092] J = w1D z + w2Te + w3W i

[0093] The larger the calculated J, the higher the priority of the triple. Select the triple with the largest J value as the optimal triple, subtract one from the ammunition number of the weapon of the optimal triple, and the threat degree value v of target j j = v j × p ij , p ij is the kill probability of weapon i against target j. Among them, (w1, w2, w3) are the corresponding normalized weights, used to measure the importance of the three decision criteria. How to select a group of most suitable weights to maximize the kill effect of the allocation scheme is the problem to be solved later. The weights can be used as variables to be optimized, and by optimizing the weights, the selection of the optimal triple can be changed, thereby improving the performance of the entire allocation scheme.

[0094] Step 5: Construction of the allocation scheme: Repeat steps 2, 3, and 4. Until the constraints of ammunition constraint, weapon concurrency ability constraint, and upper limit of enemy target strikes reach saturation, the complete construction process is as shown in Figure 3As shown below: Threat assessment is carried out for all incoming targets of the attacking side, the target with the highest threat level is selected, the distances between the current target and all radars are calculated, and the encounter points are calculated with all weapons, and all feasible radar-target-weapon combinations are screened out. All feasible radar-target-weapon combinations are evaluated through the constructed decision function, the optimal triple is selected, the weapon ammunition constraint and the threat level of the target are updated, and the above process is repeated until the weapon ammunition constraint reaches saturation or the weapon concurrency ability constraint and the constraint of the upper limit of enemy target strikes reach saturation.

[0095] Step 6. Optimize the weights through an improved particle swarm algorithm: Although the distribution plan generated by the heuristic rule has a fast generation speed, the strike effect of the generated distribution plan may not be good. The particle swarm algorithm has the defect of being prone to premature convergence. Therefore, the present invention adopts an improved particle swarm algorithm to optimize the weights of the triples generated by the heuristic algorithm, so as to optimize the strike effect of the distribution plan. As Figure 4 shown below: The algorithm takes each weight (w1, w2, w3) as a variable to be optimized, and each weight (w1, w2, w3) is regarded as a particle in the solution space. The fitness function value of the improved particle swarm algorithm is as follows:

[0096]

[0097] where x ij is a decision variable, indicating that the i-th weapon strikes the j-th target. p ij is the kill probability, indicating the kill probability of the i-th weapon against the j-th target.

[0098] In the evolution process of the improved particle swarm algorithm, in order to improve the convergence speed of the particle swarm algorithm and avoid the phenomenon of population premature convergence, the compression factor method is adopted to improve the convergence speed of the algorithm. The positions and velocities of the traditional particle swarm are updated with a certain probability for each particle, and at the same time, its current position and its historical optimal position are crossed with a certain probability for update, and the cross method adopts multi-point random crossing. Each weight is normalized and its value range is restricted between 0 and 1. This process is an optimization solution process, and the optimized weights are (w1, w2, w3), and finally the strike effect of the entire distribution plan is optimized. The termination condition of the algorithm is that the number of iterations reaches the set requirement.

[0099] The above-disclosed embodiments are implemented on the premise of the technical solution of the present invention, and detailed implementation manners and specific operation processes are given. However, the protection scope of the present invention is not limited to the described embodiments. As can be seen from the above, many contents in the present invention can be modified and replaced. The fixed values in this embodiment are only for better explaining the principle and application of the present invention, so as to be more easily understood and applied. Any local modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention shall be included within the protection scope of the present invention.

Claims

1. A dynamic allocation method for guidance and firepower resources for air defense operations, characterized in that The steps of this method include: Step 1: Conduct a threat degree assessment on all incoming targets of the attacking party to obtain a threat degree assessment result, and select the target with the highest threat degree according to the obtained threat degree assessment result; Step 2: Screen the set of detectable radars; Step 3: Screen the set of strike weapons; Step 4: Combine the set of detectable radars screened in Step 2, the set of strike weapons screened in Step 3, and the target with the highest threat degree selected in Step 1 to form a set of feasible triples, and construct a decision function to evaluate all triples in the set of feasible triples, select the optimal triple, and use the weapon in the selected optimal triple to strike the target in the triple. After the strike is completed, enter Step 1 until any one of the conditions of the missile in the weapon being exhausted, the target strike upper limit constraint being saturated, and the weapon strike upper limit constraint being saturated is met, and the dynamic allocation of guidance and firepower resources for air defense operations is completed.

2. A method for dynamic allocation of guidance and firepower resources for air defense operations according to claim 1, characterized in that: In the said Step 1, the TOPSIS method is used to conduct a threat degree assessment on all incoming targets of the attacking party.

3. A method for dynamic allocation of guidance and firepower resources for air defense operations according to claim 2, characterized in that: The evaluation factors include target type, target speed, and target distance.

4. A method for dynamic allocation of guidance and firepower resources for air defense operations according to any one of claims 1-3, characterized in that: In the said Step 1, the method for conducting a threat degree assessment on all incoming targets of the attacking party is specifically as follows: Step 1.1: Store the current target type, target speed, and target distance in a matrix to obtain a data matrix; Step 1.2: Perform standardization processing on all elements in the data matrix obtained in Step 1.1 to make the dimensions of the three factors consistent, and positiveize the target distance index; Step 1.3: Respectively obtain the maximum and minimum values of the target type, target speed, and target distance after the standardization processing in Step 1.2 to obtain the target type maximum value, target type minimum value, target speed maximum value, target speed minimum value, target distance maximum value, and target distance minimum value; Then the threat assessment result of the i-th target is N is the total number of incoming targets of the attacker; Among them, Calculate the distance D1 between the target type in the i-th target and the target type maximum value; Calculate the distance D2 between the target type in the i-th target and the target type minimum value; Calculate the distance D3 between the target speed in the i-th target and the target speed maximum value; Calculate the distance D4 between the target speed in the i-th target and the target speed minimum value; Calculate the distance D5 between the target speed in the i-th target and the target distance maximum value; Calculate the distance D6 between the target speed in the i-th target and the target distance minimum value.

5. A method for dynamic allocation of guidance and firepower resources for air defense operations according to claim 4, characterized in that: In the said Step 4, the factors for constructing the decision function include the distance between the encounter point and the command center, the distance of the target flying to the encounter point, and the remaining missile quantity of the weapon.

6. A method for dynamic allocation of guidance and firepower resources for air defense operations according to claim 5, characterized in that: The method for constructing the decision function is as follows: Each feasible triple in the set of feasible triples is calculated as follows: Calculate and normalize the distance D between the encounter point and the defender's command center z ; where d zmin is the shortest distance from the encounter point of all triples in the set of feasible triples to the command center, and d zmax is the farthest distance from the encounter point of all triples in the set of feasible triples to the command center; Calculate and normalize the time Te for the target to fly to the encounter point; where t emin is the minimum time for the target to fly to the encounter point, and t emax is the maximum time for the target to fly to the encounter point; 3) Calculate and normalize the remaining number of missiles of the weapon W i ; W i = w i / w w i is the remaining missile quantity of all triples in the set of feasible triples, and w is the maximum missile quantity of the weapon; The decision function is: J = w1D z + w2Te + w3W i The larger the calculated J, the higher the priority of the feasible triple. Select the triple with the largest J value as the optimal triple, where (w1, w2, w3) are the corresponding normalized weights.

7. A method for dynamic allocation of guidance and firepower resources for air defense operations according to claim 5, characterized in that: In step 4, an improved particle swarm optimization algorithm is used to optimize the weights of the decision function.

8. A method for dynamic allocation of guidance and firepower resources for air defense operations according to claim 7, characterized in that: The method for optimizing the weights of the decision function is: The improved particle swarm optimization algorithm takes each weight (w1, w2, w3) as a variable to be optimized, regards each weight (w1, w2, w3) as a particle in the solution space, and the fitness function value of the improved particle swarm optimization algorithm is as follows: where x ij is a decision variable, indicating that the i-th weapon strikes the j-th target, and p ij is the kill probability, representing the kill probability of the i-th weapon against the j-th target.

Images