Multi-beam interference resource allocation method based on improved artificial fish swarm algorithm

Abstract

The invention relates to a multi-beam interference resource allocation method based on an improved artificial fish swarm algorithm, which comprises the following steps of: firstly, constructing a hardware interference resource model of a multi-beam jammer, and analyzing interference hardware resources required by different interference styles so as to obtain required hardware resource types; designing a solution model and constraint conditions of resource allocation according to threat level differences and interference effect differences of different interference patterns on different radars, and constructing an optimization target and a constraint function of an interference resource allocation model, thereby obtaining a multi-beam interference resource allocation model; the interference resource allocation model is solved by adopting an artificial fish swarm algorithm, and the artificial fish swarm algorithm is optimized by adopting a linearly reduced step length, mutation operation and initialization of an infeasible solution; in a word, the method has the advantages that the constructed hardware interference resource simulation effect is good, the algorithm is easy to implement, the sensitivity to an initial value is low, the earlier-stage optimization speed is high, and the global search capability is high.

Description

Technical Field

[0001] The invention belongs to the technical field of radar and electronic warfare reconnaissance, is applied to the rapid allocation of interference resources of a multi-beam jammer, and specifically relates to a multi-beam interference resource allocation method based on an improved artificial fish swarm algorithm. Background Art

[0002] Jamming resource allocation is a combinatorial optimization method that aims to reasonably allocate the available resources in the jammer to complete the jamming task based on the number and threat level of the enemy radars and the jamming strategy design to be selected by the enemy. Common jamming resource allocation models include traditional combinatorial optimization algorithms based on 0-1 planning and computational intelligence algorithms that combine swarm intelligence with evolutionary algorithms. Computational intelligence algorithms achieve the advantages of optimizing the solution of problems by simulating the characteristics of group cooperation and individual evolution in nature, and can better solve complex jamming resource allocation problems. However, the existing jamming resource allocation models are mostly targeted at multi-function radars, and the model design is too simple to be applied in real combat environments.

[0003] Swarm intelligence algorithms simulate the cooperation and competition between individuals in a population to form a dynamic group so that effective information can be transmitted between individuals, thereby achieving the purpose of optimization. Among them, the particle swarm algorithm is a typical swarm intelligence algorithm. Since each particle has only two characteristics, speed and position, all particles search for the optimal solution in the hypothesis space by following the current individual optimal particle and the global optimal particle. Therefore, when the particle swarm algorithm is applied to interference resource allocation, due to the particularity of the solution space, scholars often introduce operations such as mutation and crossover to achieve individual updates. The existing algorithms and models have many shortcomings; first, the construction of the interference scenario model is too simple, and the existing algorithms are mostly aimed at the interference resources of multiple jammers to multiple radars. Allocation, and the allocation content is mostly limited to parameters such as interference style, beam pointing, power and angle, without considering the specific parameter allocation of the interference style. However, in actual use, the setting of interference parameters largely determines the quality of the interference effect, so the setting of interference parameters must be considered. It is of great significance to reasonably allocate interference parameters under limited interference resources. Secondly, the existing algorithms are prone to fall into the calculation of local optimal solutions, and the algorithm efficiency is not high. The particle swarm algorithm considering the optimal individual is difficult to jump out of the local optimal solution. At the same time, the completely random operation of the optimal solution direction leads to a large number of infeasible solutions in the particles, thereby reducing the efficiency of the algorithm. The monotonous constraints cannot fully explore the characteristics of the environment, resulting in a decrease in the algorithm performance.

[0004] Therefore, there is an urgent need for a multi-beam interference resource allocation method based on an improved artificial fish school algorithm, which can analyze the types of resources occupied by interference parameters on the hardware, design a hardware resource allocation model of the multi-beam jammer, and use the improved artificial fish school algorithm to solve the multi-beam interference resource allocation model. Summary of the invention

[0005] In view of this, the present invention proposes a multi-beam interference resource allocation method based on an improved artificial fish swarm algorithm, which is applied to the field of radar and electronic warfare reconnaissance technology, and can solve the technical problems of the existing interference scenario model being too simple to construct, the algorithm being inefficient, and the algorithm being degraded in performance.

[0006] In order to achieve the above technical objectives, the specific technical solutions adopted by the present invention are:

[0007] A multi-beam interference resource allocation method based on an improved artificial fish swarm algorithm comprises the following steps:

[0008] S1. Build a hardware jammer resource model for multi-beam jammers, analyze the jammer hardware resources required for different jammer styles, and thus obtain the required hardware resource types;

[0009] S2. Design the resource allocation solution model and constraint conditions according to the difference in threat levels and the difference in interference effects of different interference styles on different radars, and construct the optimization objective and constraint function of the interference resource allocation model to obtain the multi-beam interference resource allocation model;

[0010] S3. The artificial fish swarm algorithm is used to solve the interference resource allocation model, and the artificial fish swarm algorithm is optimized by linearly reducing the step size, mutation operation and initializing infeasible solutions.

[0011] Furthermore, the interference patterns in step S1 include two-dimensional deception interference of distance and speed, distance suppression, dense false targets, intermittent sampling interference, comb spectrum interference, blocking noise, swept frequency noise, aiming noise and gated noise, and the interference hardware resources include a delay module, a frequency shift module, a Gaussian random number / band-limited noise generation module, a convolution noise generation module and an amplitude control module, which are allocated.

[0012] Furthermore, in step S1, the values ​​of different hardware resources occupied by each interference pattern are different, the delay module, frequency shift module, amplitude control module are not more than 64, the Gaussian random number / band-limited noise generation module is not more than 2, and the convolution noise generation module is not more than 1.

[0013] Furthermore, in order to construct the optimization objective and constraint function of the interference resource allocation model in step S2, it is necessary to input a multi-objective interference resource allocation problem. The input of the multi-objective interference resource allocation problem includes:

[0014] (1) Alternative interference patterns for each waveform to be interfered with: Assume that there are M interference waveforms and N interference patterns.

[0015] (2) The interference effect of each waveform to be interfered when a given candidate interference pattern is used. Let the interference effect of the i-th waveform to be interfered when the j-th interference pattern is used for interference be U i,j .

[0016] (3) The resources occupied by each waveform to be interfered when using a given candidate interference pattern. Suppose there are K resources in total. Suppose the amount of the kth resource occupied by the i-th waveform to be interfered when using the j-th interference pattern is c i,j,k .

[0017] (4) The total amount of each resource. Let the total amount of the kth resource be V k .

[0018] (5) The threat level of each waveform to be interfered with. The role of the threat level is to assign different weights to different waveforms to be interfered with, thereby determining the final utility function. Let the threat level of the i-th waveform to be interfered be w i , w i ∈[0,1].

[0019] c i,j,k and V k All are positive integers.

[0020] Furthermore, in step S2, x i,j Mark whether the i-th waveform to be interfered adopts the j-th candidate interference style for interference, x i,j =1 means that the i-th waveform to be interfered adopts the j-th candidate interference pattern for interference, x i,j =0 means that the i-th waveform to be interfered does not use the j-th candidate interference pattern for interference. i,j, i=1,2…,M;j=1,2,…,N,satisfies

[0021]

[0022] The final utility function takes the form of weighted average:

[0023]

[0024] The resource constraints are expressed as

[0025]

[0026] The interference resource allocation is modeled as an optimization problem, where the optimization objective function of the interference resource allocation model is:

[0027]

[0028] The constraint function is

[0029]

[0030] Furthermore, in step S3, the interference resource model is combined with the artificial fish swarm algorithm, and an M×N dimensional matrix is ​​constructed according to M interference waveforms and N interference styles to construct the most basic interference resource allocation model, wherein the values ​​in the matrix are the resource allocation schemes of the specified jammer to the specified radar, and the rationality of the interference resource allocation scheme is defined by the M×N dimensional interference effectiveness matrix, and the optimal interference resource allocation scheme under the optimal interference effectiveness result is solved under given constraints.

[0031] Furthermore, in step S3, the definitions and symbols of the basic parameters of the artificial fish swarm algorithm are set, the population size N represents the number of available interference resource allocation schemes; the individual fish X represents an array containing the interference resource allocation schemes corresponding to each radar waveform to be interfered; the distance d represents the number of unequal values ​​in the two individual arrays; the field of view r represents the maximum distance of all nodes adjacent to the individual fish; the step size μ represents the number of values ​​in the array that are changed each time the individual fish is updated; the crowding factor δ represents the degree of crowding of the fish school in the current field of view; the number of attempts m represents the number of times the individual attempts to perform the basic behavior in each iteration; the number of iterations n represents the number of iterations for the algorithm to find the optimal solution.

[0032] Furthermore, in step S3, the artificial fish school algorithm adopts any one of the three behaviors including foraging behavior, flocking behavior and tail-chasing behavior. The artificial fish school algorithm is first initialized, and then the profit Y of each individual in the initial fish school is calculated for each behavior, and the maximum profit Y in the population is taken. max , choose the appropriate behavior based on the comparison of calculation results.

[0033] Furthermore, in the foraging behavior, each individual fish freely explores the space. When a better solution is sensed within the field of vision, the individual tends to this state. In the clustering behavior, the number of fish schools in the neighborhood is explored and the center position of the partner is calculated. The benefit value of the newly obtained center position is compared with the benefit of the current position. If the benefit of the center position is higher and the neighborhood space is not crowded, the fish moves closer to the center position. In the tail-chasing behavior, the position of the best individual within the field of vision is explored. When the benefit of the best individual is greater than that of the current individual and the field of vision is not crowded, the fish moves closer to the position of the best individual.

[0034] Furthermore, the linear reduction step size in step S3 is gradually reduced from 3 to 1.

[0035] By adopting the above technical solution, the present invention can also bring the following beneficial effects:

[0036] 1. The present invention mentions a multi-beam interference resource allocation method based on an improved artificial fish school algorithm, constructs a hardware resource allocation model of a multi-beam jammer and an optimal interference style model of different radars, and can obtain the best interference effect of the multi-beam jammer when interference resources are insufficient, optimizes the allocation of hardware resources, and thus guides the optimal interference waveform. At the same time, the hardware resource allocation model of the multi-beam jammer and the optimal interference style model of different radars are designed, which is more practical and convenient for application in real scenarios.

[0037] 2. The multi-beam interference resource allocation method based on the improved artificial fish school algorithm mentioned in the present invention introduces an improved artificial fish school algorithm to allocate interference resources, improves the algorithm's solution efficiency and the ability to reach the global optimal solution, has the advantages of easy implementation, low sensitivity to initial values, fast early optimization speed and strong global search capability, combined with the interference resource allocation model, uses mutation and initialization of infeasible solutions to increase the diversity of the population, and prevents the algorithm from falling into the local optimal solution too early; and uses a linearly decreasing learning factor to achieve fast and smooth convergence of the optimization algorithm.

[0038] 3. The multi-beam interference resource allocation method based on the improved artificial fish school algorithm mentioned in the present invention is applied to the rapid allocation of interference resources when the multi-beam jammer faces multiple radars. It belongs to the construction and optimization of the hardware interference resource model in the radar and electronic warfare fields. It can also be widely used in scenarios where hardware resource model construction and optimization are required in the radar and electronic warfare fields, and can promote the development of the entire radar and electronic warfare hardware equipment model technology, which has positive significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 This is the convergence curve of the artificial fish swarm algorithm in this example;

[0041] Figure 2 is the optimal jamming pattern for each radar target in this example;

[0042] Figure 3 is the total amount of hardware jammer resources of the multi-beam jammer in this example;

[0043] Figure 4 is the threat level of different targets in this instance;

[0044] Figure 5is the parameter value of the artificial fish swarm algorithm set in this example;

[0045] Figure 6 This is the interference resource allocation result of the artificial fish swarm algorithm in this example. DETAILED DESCRIPTION

[0046] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0047] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0048] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present invention, it should be understood by those skilled in the art that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this device and / or practice this method.

[0049] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention. The drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0050] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0051] Example 1

[0052] In one embodiment of the present invention, Figure 1 As shown, the present invention mentions a multi-beam interference resource allocation method based on an improved artificial fish swarm algorithm, and the steps are as follows:

[0053] Step S1 constructs a hardware jammer resource model for multi-beam jammers, and analyzes the jammer hardware resources required for different jammer styles in the field programmable gate array (FPGA), where suppression jammers require frequency shift modules, amplitude control modules, and noise generation modules, etc.; distance and speed deception jammers require delay modules, frequency shift modules, convolution modules, and amplitude control modules, etc. Comprehensively consider multiple jammer styles such as distance and speed two-dimensional deception jammers, dense false target jammers, intermittent sampling jammers, comb spectrum jammers, blocking noise jammers, sweeping noise jammers, and aiming noise jammers, so as to obtain the required hardware resource types and construct a resource table occupied by different jammer styles, as shown in Table 1 below:

[0054]

[0055] Step B constructs the optimization objective and constraint function of the multi-beam interference resource allocation model. The input of the multi-objective interference resource allocation problem includes:

[0056] (1) Alternative interference patterns for each waveform to be interfered with: Assume that there are M interference waveforms and N interference patterns.

[0057] (2) The interference effect of each waveform to be interfered when a given candidate interference pattern is used. Let the interference effect of the i-th waveform to be interfered when the j-th interference pattern is used for interference be U i,j .

[0058] (3) The resources occupied by each waveform to be interfered when using a given candidate interference pattern. Suppose there are K resources in total. Suppose the amount of the kth resource occupied by the i-th waveform to be interfered when using the j-th interference pattern is c i,j,k .

[0059] (4) The total amount of each resource. Let the total amount of the kth resource be V k .

[0060] (5) The threat level of each waveform to be interfered with. The role of the threat level is to assign different weights to different waveforms to be interfered with, thereby determining the final utility function. Let the threat level of the i-th waveform to be interfered be w i , w i ∈[0,1].

[0061] Generally speaking, c i,j,k and V k All are positive integers.

[0062] Use x i,jMark whether the i-th waveform to be interfered adopts the j-th candidate interference style for interference, x i,j =1 means that the i-th waveform to be interfered adopts the j-th candidate interference pattern for interference, x i,j =0 means that the i-th waveform to be interfered does not use the j-th candidate interference pattern for interference. i,j, i=1,2…,M;j=1,2,…,N,satisfies

[0063]

[0064] The final utility function can have many forms, a commonly used form is the weighted average form:

[0065]

[0066] Resource constraints can be expressed as

[0067]

[0068] Therefore, the interference resource allocation is modeled as the following optimization problem:

[0069]

[0070] Step S3 uses an optimized artificial fish swarm algorithm to solve the model. In the fast interference resource scheduling model based on artificial fish swarms, it is necessary to consider combining the interference resource model with the artificial fish swarm algorithm. Usually, an M×N dimensional matrix is ​​constructed based on M interference waveforms and N interference styles to construct the most basic interference resource allocation model, where the values ​​in the matrix are the resource allocation schemes of the specified jammer to the specified radar. Then, the rationality of the interference resource allocation scheme is defined by the M×N dimensional interference effectiveness matrix, and the optimal interference resource allocation scheme under the optimal interference effectiveness result is solved under given constraints.

[0071] First, the definitions and symbolic representations of the basic parameters of the artificial fish swarm algorithm are given. The individual fish X represents the array containing the interference resource allocation scheme for each radar corresponding to the waveform to be interfered; the distance d represents the number of unequal values ​​in the two individual arrays; the field of view r represents the maximum distance of all nodes adjacent to the individual fish; the step size μ represents the number of values ​​in the array that are changed each time the individual fish is updated; the crowding factor δ represents the degree of crowding of the fish swarm in the current field of view; the number of attempts m represents the number of times the individual attempts to perform the basic behavior in each iteration; and the number of iterations n represents the number of iterations for the algorithm to find the optimal solution.

[0072] The artificial fish school algorithm adopts one of the foraging behavior, swarming behavior and tail-chasing behavior. The specific algorithm is as follows:

[0073] For foraging behavior, each individual fish freely explores the space. When a better solution is perceived within the field of vision, the individual tends to this state. The specific code in this embodiment is:

[0074]

[0075] For the swarming behavior, explore the number of fish schools in the neighborhood and calculate the center position of the partner, then compare the benefit value of the newly obtained center position with the benefit of the current position. If the center position has a higher benefit and the neighborhood space is not crowded, move closer to the center position. The specific algorithm is as follows:

[0076]

[0077] For tailgating behavior, explore the location of the best individual within the field of view. When the benefit of the best individual is greater than that of the current individual and the field of view is not crowded, move closer to the location of the best individual. The specific algorithm is as follows:

[0078]

[0079]

[0080] The simplified code of the entire artificial fish swarm algorithm is as follows:

[0081] Initialize, calculate the profit Y of each individual in the initial fish group, and take the maximum profit Y in the group max for i=1:m

[0082] Execute the individual behavior of artificial fish, update the fish group and calculate the benefit Y of each individual in the new fish group new , take the maximum benefit Y in the population nmax

[0083] if Y max <Y nmax

[0084] Y max ←Y nmax

[0085] end

[0086] End

[0087] Finally, the appropriate behavior is selected based on the comparison of the calculation results. At the same time, in order to further improve the convergence speed of the artificial fish swarm algorithm, the interference resource allocation model is combined, and the method of mutation and initialization of infeasible solutions is used to increase the diversity of the population, prevent the algorithm from falling into the local optimal solution too early, and use a linearly decreasing learning factor to achieve fast and smooth convergence of the optimization algorithm. Specifically, the step size is gradually reduced from 3 to 1. In this embodiment, a simulation environment is constructed and the technical solution of the present invention is experimentally analyzed. The effectiveness of the interference resource allocation method is illustrated by examples of typical combat scenarios. First, a scenario is set with 1 airborne early warning radar, 2 shipborne air defense radars, and 1 ground-based early warning radar, for a total of 4 targets. The interference pattern of each target is ranked as follows: Figure 2 As shown in Figure 3 As shown, the threat level of each target is as follows Figure 4 As shown in the figure, the setting values ​​of the parameters of the artificial fish school are as follows Figure 5 shown.

[0088] Using the above model for simulation analysis, the interference resource allocation results are as follows: Figure 6 As shown, the convergence curve of the algorithm is Figure 1 As shown, it is shown that the hardware resource allocation model of the multi-beam jammer constructed by the present invention and the optimal interference style model of different radars can better simulate the usage of hardware interference resources in PFGA in real scenarios, thereby helping to guide the application of multi-beam jammers in real scenarios. In short, the artificial fish swarm algorithm adopted by the present invention has the advantages of easy implementation, low sensitivity to initial values, fast early optimization speed, strong global search capability, etc. The improved algorithm based on this has faster algorithm solving efficiency and the ability to reach the global optimal solution.

[0089] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A multi-beam interference resource allocation method based on an improved artificial fish swarm algorithm, characterized in that: The following steps are involved: S1. Build a hardware jammer resource model for multi-beam jammers, analyze the jammer hardware resources required for different jammer styles, and thus obtain the required hardware resource types; S2. Design the resource allocation solution model and constraint conditions according to the difference in threat levels and the difference in interference effects of different interference styles on different radars, and construct the optimization objective and constraint function of the interference resource allocation model to obtain the multi-beam interference resource allocation model; S3. The artificial fish swarm algorithm is used to solve the interference resource allocation model, and the artificial fish swarm algorithm is optimized by linearly reducing the step size, mutation operation and initializing infeasible solutions.

2. The multi-beam interference resource allocation method based on the improved artificial fish school algorithm according to claim 1, characterized in that: The interference patterns in step S1 include two-dimensional deception interference of distance and speed, distance suppression, dense false targets, intermittent sampling interference, comb spectrum interference, blocking noise, swept frequency noise, aiming noise and gated noise. The interference hardware resources include a delay module, a frequency shift module, a Gaussian random number / band-limited noise generation module, a convolution noise generation module and an amplitude control module.

3. The multi-beam interference resource allocation method based on the improved artificial fish school algorithm according to claim 2, characterized in that: In the step S1, the values ​​of different hardware resources occupied by each interference pattern are different, the delay module, frequency shift module, amplitude control module are not more than 64, the Gaussian random number / band-limited noise generation module is not more than 2, and the convolution noise generation module is not more than 1.

4. The multi-beam interference resource allocation method based on the improved artificial fish school algorithm as claimed in claim 3, characterized in that: In order to construct the optimization objective and constraint function of the interference resource allocation model in step S2, it is necessary to input a multi-objective interference resource allocation problem. The input of the multi-objective interference resource allocation problem includes: (1) Alternative interference patterns for each waveform to be interfered with: Assume that there are M interference waveforms and N interference patterns. (2) The interference effect of each waveform to be interfered when a given candidate interference pattern is used. Let the interference effect of the i-th waveform to be interfered when the j-th interference pattern is used for interference be U i,j . (3) The resources occupied by each waveform to be interfered when using a given candidate interference pattern. Suppose there are K resources in total. Suppose the amount of the kth resource occupied by the i-th waveform to be interfered when using the j-th interference pattern is c i,j, k. (4) The total amount of each resource. Let the total amount of the kth resource be V k . (5) The threat level of each waveform to be interfered with. The role of the threat level is to assign different weights to different waveforms to be interfered with, thereby determining the final utility function. Let the threat level of the i-th waveform to be interfered be w i , w i ∈[0,1]. c i,j,k and V k All are positive integers.

5. The multi-beam interference resource allocation method based on the improved artificial fish school algorithm according to claim 4, characterized in that: In step S2, x i,j Mark whether the i-th waveform to be interfered adopts the j-th candidate interference style for interference, x i,j =1 means that the i-th waveform to be interfered adopts the j-th candidate interference pattern for interference, x i,j =0 means that the i-th waveform to be interfered does not use the j-th candidate interference pattern for interference. i,j ,i=1,2…,M;j=1,2,…,N,satisfying The final utility function takes the form of weighted average: The resource constraints are expressed as The interference resource allocation is modeled as an optimization problem, where the optimization objective function of the interference resource allocation model is: The constraint function is 6. The multi-beam interference resource allocation method based on the improved artificial fish school algorithm according to claim 5, characterized in that: In step S3, the interference resource model is combined with the artificial fish swarm algorithm, and an M×N dimensional matrix is ​​constructed according to M interference waveforms and N interference patterns to construct the most basic interference resource allocation model, wherein the values ​​in the matrix are the resource allocation schemes of the specified jammer to the specified radar, and the rationality of the interference resource allocation scheme is defined by the M×N dimensional interference effectiveness matrix, and the optimal interference resource allocation scheme under the optimal interference effectiveness result is solved under given constraints.

7. The multi-beam interference resource allocation method based on the improved artificial fish school algorithm according to claim 6, characterized in that: In step S3, the definitions and symbols of the basic parameters of the artificial fish swarm algorithm are set, the population size N represents the number of interference resource allocation schemes available for selection; the individual fish X represents an array containing the interference resource allocation schemes corresponding to each radar waveform to be interfered; the distance d represents the number of unequal values ​​in the two individual arrays; the field of view r represents the maximum distance of all nodes adjacent to the individual fish; the step size μ represents the number of values ​​in the array that are changed each time the individual fish is updated; the crowding factor δ represents the degree of crowding of the fish swarm in the current field of view; The number of attempts m represents the number of times the individual attempts to perform the basic behavior in each iteration; The number of iterations n represents the number of iterations the algorithm takes to find the optimal solution.

8. The multi-beam interference resource allocation method based on the improved artificial fish school algorithm according to claim 7, characterized in that: In step S3, the artificial fish swarm algorithm adopts any one of the three behaviors including foraging behavior, flocking behavior and tail-chasing behavior. The artificial fish swarm algorithm is first initialized, and then the profit Y of each individual in the initial fish swarm is calculated for each behavior, and the maximum profit Y in the swarm is taken. max , choose the appropriate behavior based on the comparison of calculation results.

9. The multi-beam interference resource allocation method based on the improved artificial fish school algorithm according to claim 8, characterized in that: In the foraging behavior, each individual fish freely explores the space. When a better solution is sensed within the field of vision, the individual tends to this state. In the herding behavior, the number of fish schools in the neighborhood is explored and the center position of the partner is calculated. The benefit value of the newly obtained center position is compared with the benefit of the current position. If the benefit of the center position is higher and the neighborhood space is not crowded, the fish moves closer to the center position. In the tail-chasing behavior, the position of the best individual within the field of vision is explored. When the benefit of the best individual is greater than that of the current individual and the field of vision is not crowded, the fish moves closer to the position of the best individual.

10. The multi-beam interference resource allocation method based on the improved artificial fish school algorithm according to claim 9, characterized in that: The linear reduction step size in step S3 is gradually reduced from 3 to 1.

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