CN112926832A - Interference decision method based on directional mutation search artificial bee colony algorithm - Google Patents

Interference decision method based on directional mutation search artificial bee colony algorithm Download PDF

Info

Publication number
CN112926832A
CN112926832A CN202110111032.9A CN202110111032A CN112926832A CN 112926832 A CN112926832 A CN 112926832A CN 202110111032 A CN202110111032 A CN 202110111032A CN 112926832 A CN112926832 A CN 112926832A
Authority
CN
China
Prior art keywords
interference
radar
decision
matrix
bee colony
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110111032.9A
Other languages
Chinese (zh)
Inventor
叶方
赵彤
李一兵
孙骞
田园
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Harbin Engineering University
Original Assignee
Harbin Engineering University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Harbin Engineering University filed Critical Harbin Engineering University
Priority to CN202110111032.9A priority Critical patent/CN112926832A/en
Publication of CN112926832A publication Critical patent/CN112926832A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Strategic Management (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Game Theory and Decision Science (AREA)
  • Tourism & Hospitality (AREA)
  • Quality & Reliability (AREA)
  • Health & Medical Sciences (AREA)
  • Operations Research (AREA)
  • Marketing (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • General Business, Economics & Management (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Educational Administration (AREA)
  • Mathematical Physics (AREA)
  • Development Economics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention relates to an interference decision method based on a directional mutation search artificial bee colony algorithm, which comprises the following steps: firstly, one-to-one evaluation is carried out on an interference machine and a radar through five aspects of time, space, power, frequency and interference pattern, and an interference benefit matrix B is constructed by using benefit values obtained by evaluationij(ii) a Secondly, threat level assessment is carried out on the radar through the working state of the radar, the radar system and the actual combat task of the radar by the front reconnaissance aircraft pj(ii) a Then, a radar interference decision model and a target function are constructed by utilizing the interference benefit matrix, the radar threat level and the constraint condition; finally, solving by searching artificial bee colony algorithm based on directional mutation to obtain an optimal distribution scheme and an interference profit value; and then tasks are issued to all the jammers according to the optimal scheme. The method is suitable forThe interference resource task allocation under the condition of the multi-interference machine collaborative interference networking radar reduces the iteration times, simultaneously has larger average profit value, improves the optimization probability and has strong practicability.

Description

Interference decision method based on directional mutation search artificial bee colony algorithm
(I) technical field
The invention belongs to the technical field of collaborative interference task allocation decision-making in electronic countermeasure, and particularly relates to an interference decision-making method based on a directional mutation search artificial bee colony algorithm.
(II) background of the invention
With the increasing complexity of electromagnetic environment, multi-party cooperative interference in various modes becomes a main mode of electronic warfare attack, and the traditional point-to-point-based interference resource allocation algorithm is not suitable for the modern war environment due to low efficiency. With the development of intelligent decision making technology, cooperative interference will become a countermeasure trend. The proper decision scheme can provide sufficient guarantee for the next battle mission, and can also enable the existing interference resources to exert the maximum battle benefit, thereby playing a vital role. And an appropriate decision scheme is found, so that the fighting efficiency and the profit are directly influenced, the loss of fighting resources of the party is reduced to a certain extent, and the fighting efficiency of the whole cognitive countermeasure system is improved.
For a real battlefield environment, with the increase of the number of interference resources, the traditional optimization algorithm can not meet the battle requirements. Therefore, in the field of electronic countermeasure nowadays, the method has very important significance for the research of intelligent interference decision-making method. The method mainly comprises a genetic algorithm, an ant colony algorithm and a particle swarm algorithm in the aspect of solving the problem of distribution of interference resources. But still have to be optimized in terms of parameter selection, convergence speed, etc.
Disclosure of the invention
The invention aims to provide a novel interference decision method which aims at reducing iteration times and obtaining a higher profit value of an interference party in an interference decision process under the condition of cooperative interference of a multi-interference-machine pair network radar.
The purpose of the invention is realized as follows:
step 1: one-to-one evaluation is carried out on the jammer and the radar through five aspects of time, space, power, frequency and interference pattern, and an interference benefit matrix B is constructed by using benefit values obtained through evaluationij；
Step 2: radar by front scoutEvaluation p for threat level of radar by working state, radar system and actual combat task of radarj；
And step 3: constructing a radar interference decision model and a target function by utilizing the interference benefit matrix, the radar threat level and the constraint condition;
and 4, step 4: solving through an artificial bee colony algorithm based on directional mutation search to obtain an optimal distribution scheme and an interference profit value;
and 5: and issuing tasks to each jammer according to the optimal scheme.
The core technical content of the invention is that a profit matrix is constructed by carrying out one-to-one evaluation on an interference machine and a radar by utilizing a fuzzy comprehensive evaluation method in five aspects of time, space, power, frequency and interference pattern. And constructing an anti-interference model according to the obtained profitability matrix, the radar threat level and the constraint condition. And (4) carrying out decision making by utilizing a directional mutation search artificial bee colony algorithm to obtain an optimal scheme and a corresponding profit value.
The invention comprises a profit matrix construction method, which mainly comprises the following steps: and selecting results of evaluation in five different aspects, obtaining a fuzzy relation matrix according to the membership function, and giving corresponding weight by using an analytic hierarchy process. And obtaining a final profit matrix by using a fuzzy comprehensive evaluation method.
The invention comprises the following steps of making a decision by using an artificial bee colony algorithm for directional mutation search, wherein the main contents are as follows: and (3) performing directional mutation search in a neighborhood search part in the whole algorithm flow, and obtaining the interference benefit value of all the jammers to each radar at the moment according to the original decision matrix of the currently collected honeybees. And mutating the interference decision results of the two radars with the minimum benefit value to obtain a new decision matrix. When f of the new decision matrix is larger than f of the original matrix according to the greedy criterion, the old decision matrix X is replaced by the newly found decision matrix X'.
Compared with the prior art, the invention has the beneficial effects that:
the method is suitable for the interference resource task allocation under the condition of multi-interference machine cooperative interference networking radar, reduces the iteration times, has larger average profit value, improves the optimization probability and has strong practicability;
the average iteration times of the artificial bee colony algorithm for directional mutation search is less than that of the artificial bee colony algorithm, the probability of obtaining the optimal decision is greater than that of the artificial bee colony algorithm, the optimizing capability, the optimizing probability and other performances are improved, and the robustness is better.
Drawings
FIG. 1 is a flow chart of an artificial bee colony algorithm based on directed mutation search;
FIG. 2 is a comparison graph of the number of iterations of different algorithms;
FIG. 3 is a comparison graph of multiple decision average optimization profit values for different algorithms;
FIG. 4 is a comparison graph of the average convergence iteration number of multiple decisions of different algorithms.
(IV) detailed description of the preferred embodiments
The embodiment provides an interference decision method based on a directional mutation search artificial bee colony algorithm, which mainly comprises the following steps:
step 1: the interference machine and the radar are evaluated in a one-to-one mode by utilizing a fuzzy comprehensive evaluation method in five aspects of time, space, power, frequency and interference patterns, and a profit matrix is constructed.
Step 1.1: the calculation method of each evaluation index is as follows:
time: the time domain description is described by adopting an interference opportunity index, and the scheme adopts a suppression time benefit function to express. Let radar R bejThe pulse width of the signal (j ═ 1, 2.. times.n) is [ t [ ]r1,tr2]Interference unit ui(i 1, 2.. said., m) effective interference with radar is carried out for a period of time [ t [j1,tj2]. The time benefit function is expressed as follows:
Figure BDA0002919356060000021
space: the description of the spatial domain uses the degree of overlap of the jammer antenna and the radar antenna beam in space, where θ1Denotes the half-power beamwidth, theta, of the antenna of the jammer2Representation radarThe antenna half-power beamwidth. The size of θ reflects the spatial overlapping degree of the antenna, and the expression of the spatial domain influence benefit function is as follows:
q2＝10lg(θ) (2)
power: description of power domain uses power squashing benefit function to represent jammer ui(i ═ 1, 2.. said., m) for radar Rj(j ═ 1, 2.., n) interfere with the pressing effect. The power domain expression is as follows:
Figure BDA0002919356060000031
wherein, PsAnd PjRespectively representing radar RjAnd jammer uiTransmit power of GtIs the radar main lobe gain, GAIs a jammer uiσ is the scattering area of the radar, a is the effective receiving area of the radar antenna, and R is the distance between the radar and the jammer. Radar RjReceived interference signal power PrjAnd effective echo signal power PrsRespectively as follows:
Figure BDA0002919356060000032
frequency: the degree of alignment between the interference frequency and the interfered radar is judged by adopting a frequency alignment benefit function, and the interference frequency and the interfered radar represent an interference machine ui(i ═ 1, 2.. said., m) for radar Rj(j ═ 1, 2.., n) interfere with the pressing effect. If used (f)i1,fi2) Representing the operating frequency of the jammer by (f)j1,fj2) Indicating the radar operating frequency. Frequency alignment benefit function EfThe expression of (a) is as follows:
Figure BDA0002919356060000033
interference pattern: for the index of the interference pattern, the interference pattern benefit function is used for describing the jammer ui(i＝1,2,...,m) For radar Rj(j ═ 1, 2.., n). Let m effective interference patterns exist for each target radar (possibly belonging to different system types), and the interference patterns are sorted according to the superiority and inferiority of theoretical interference effect. The expression of the interference pattern benefit function is:
Figure BDA0002919356060000034
step 1.2: obtaining a fuzzy relation matrix according to the membership function
Figure BDA0002919356060000035
Step 1.3: giving the corresponding weight A ═ A by using analytic hierarchy process1,A2,A3,…,Am}；
Step 1.4: b is A R, wherein 'O' is Zadeh operator, and a size-taking and size-taking algorithm (M (V, V) is adopted in the original judgment, namely, the algorithm is
Figure BDA0002919356060000041
And (4) performing operation, and making a final judgment result according to the obtained group of membership values by using the maximum membership principle to establish a profit matrix.
Step 2: threat level assessment p for radar through front reconnaissance aircraft on radar working state, radar system and actual combat task of radarj；
And step 3: constructing a radar interference decision model and a target function by utilizing the interference benefit matrix, the radar threat level and the constraint condition;
an objective function:
Figure BDA0002919356060000042
wherein x isijIs a decision variable, x ij0 means that jammer i does not interfere with radar j, xij1 indicates that jammer i interferes with radar j.
Constraint conditions are as follows:
Figure BDA0002919356060000043
wherein, (7-1) represents whether the jammer i interferes with the radar j; (7-2) indicating that a radar is interfered by at least one interference machine; (7-3) shows that one jammer i simultaneously interferes at most with alphaiAnd (4) radar.
And 4, step 4: obtaining an optimal distribution scheme and an interference profit value by searching an artificial bee colony algorithm based on directional mutation;
step 4.1: initializing algorithm parameters including the initial honey source number N, the maximum number limit of neighborhood searching and the maximum iteration number maxCycle.
Step 4.2: each interference decision matrix is represented as a fitness function value. And taking the interference benefit f obtained according to different interference decision matrixes as a basis, wherein the larger half of the N bees becomes the 'collecting bees' and the other half becomes the 'observing bees'.
Step 4.3: and all the bees are adopted to carry out directional mutation search on the original decision matrix, and the interference benefit values of all the interference machines to each radar at the moment are obtained according to the original decision matrix of the current bees. And mutating the interference decision results of the two radars with the minimum benefit value to obtain a new decision matrix. When f of the new decision matrix is larger than f of the original matrix according to the greedy criterion, the old decision matrix X is replaced by the newly found decision matrix X'. The search formula is as follows:
Figure BDA0002919356060000044
step 4.4: and all observation bees determine the honey collection bees to follow according to the probability, and a new decision matrix is obtained by changing the interference strategy of the matrix corresponding to the honey collection bees based on the directional mutation. The formula for the selection probability is as follows:
Figure BDA0002919356060000045
step 4.5: and if the decision matrix X is searched for the limit times, a larger new decision matrix f is not found, and the maximum value f and the corresponding decision matrix at the moment are saved. And returning to the step 4.2, after the operation is repeated until maxCycle is met, selecting a decision matrix X which enables the f value to be maximum, wherein the matrix X is a decision scheme capable of obtaining the optimal f value.
And 5: and issuing tasks to each jammer according to the optimal scheme.
To evaluate the effectiveness of the present invention, two sets of experiments were designed with the above-described model of interference rejection.
In the experiment, an artificial bee colony algorithm and a directional mutation search artificial bee colony algorithm are used for interference decision simulation respectively, and the optimization searching capability of the algorithm is verified;
experiment two, in order to avoid the contingency of single optimization, a Monte Carlo method is introduced to respectively operate the two algorithms for 100 times, and the average value of the iteration times of the optimal value obtained after each operation is recorded. And verifying the robustness of the algorithm through the average optimal value after each operation and the iteration number of the obtained optimal solution. The number of population N in the two experiments is 60, the maximum number of search limit is 100, and the maximum number of iterations maxCycle is 100.
Experiment one: suppose that the number m of my jammers is 6 and the number n of enemy radars is 10. And each jammer can simultaneously interfere 2 radars at most. Interference solving machine ui(i ═ 1, 2.. said., m) for radar RjA one-to-one interference benefit matrix F of (j ═ 1, 2.. times, n)1The following were used:
Figure BDA0002919356060000051
according to the cognitive reconnaissance result, a radar importance degree matrix omega is assumedj1Comprises the following steps:
pj＝[0.4 0.8 0.6 0.9 0.7 0.4 0.7 0.3 0.6 0.8]
the simulation result pairs of the two algorithms are shown in fig. 2.
Experiment two: and (5) performing the decision process for 100 times, and recording the average optimization profit value and the average iteration times for reaching the optimal value after each decision of the two algorithms. The simulation results are shown in fig. 3 and 4.
The comparison of the algorithm performances before and after the improvement of the simulation results is shown in table 1:
TABLE 1 comparison of the Performance of the two algorithms
Figure BDA0002919356060000052
As can be seen from table 1, the average iteration number of the artificial bee colony algorithm for directional mutation search is less than that of the artificial bee colony algorithm, the probability of obtaining the optimal decision is greater than that of the artificial bee colony algorithm, the optimizing capability and the optimizing probability are improved, and the robustness is better. The method of the invention is therefore very practical and efficient.
Finally, it should be noted that the above examples are only intended to describe the technical solutions of the present invention and not to limit the technical methods, the present invention can be extended in application to other modifications, variations, applications and embodiments, and therefore all such modifications, variations, applications, embodiments are considered to be within the spirit and teaching scope of the present invention.

Claims (4)

1. An interference decision method based on a directional mutation search artificial bee colony algorithm is characterized by comprising the following steps:
step 1: one-to-one evaluation is carried out on the jammer and the radar through five aspects of time, space, power, frequency and interference pattern, and an interference benefit matrix B is constructed by using benefit values obtained through evaluationij；
Step 2: threat level assessment p for radar through front reconnaissance aircraft on radar working state, radar system and actual combat task of radarj；
And step 3: constructing a radar interference decision model and a target function by utilizing the interference benefit matrix, the radar threat level and the constraint condition;
and 4, step 4: obtaining an optimal distribution scheme and an interference profit value by searching an artificial bee colony algorithm based on directional mutation;
and 5: and issuing tasks to each jammer according to the optimal scheme.
2. The interference decision method based on the directional mutation search artificial bee colony algorithm as claimed in claim 1, which is characterized in that: the specific one-to-one evaluation step in the step 1 is as follows:
step 1.1: selecting five aspects of time, space, power, frequency and interference pattern as evaluation indexes to evaluate;
step 1.2: obtaining a fuzzy relation matrix according to the membership function
Figure FDA0002919356050000011
Step 1.3: giving the corresponding weight A ═ A by using analytic hierarchy process1,A2,A3,…,Am}；
Step 1.4: the following operations are performed:
Figure FDA0002919356050000012
wherein
Figure FDA0002919356050000013
For Zadeh operator, the original judgment adopts a large-to-small algorithm (M (A, V-shaped) which is
Figure FDA0002919356050000014
And (4) performing operation, and making a final judgment result according to the obtained group of membership values by using the maximum membership principle to establish a profit matrix.
3. The interference decision method based on the directional mutation search artificial bee colony algorithm as claimed in claim 1, which is characterized in that: the interference decision model in the step 3 is as follows:
an objective function:
Figure FDA0002919356050000015
wherein x isijIs a decision variable, xij0 means that jammer i does not interfere with radar j, xij1 represents that the jammer i interferes the radar j;
constraint conditions are as follows:
Figure FDA0002919356050000016
wherein (1-1) represents whether the jammer i interferes with the radar j; (1-2) indicating that a radar is interfered by at least one interference machine; (1-3) shows that one jammer i simultaneously interferes at most with alphaiAnd (5) stopping the radar to ensure the interference effect of the jammer on the radar networking.
4. The interference decision method based on the directional mutation search artificial bee colony algorithm as claimed in claim 1, which is characterized in that: in the step 4:
step 4.1: initializing algorithm parameters including an initial honey source number N, a maximum neighborhood search frequency limit and a maximum iteration frequency maxCycle;
step 4.2: each interference decision matrix is represented as a fitness function value; taking the interference benefit f obtained according to different interference decision matrixes as a basis, wherein the larger half of f of N bees becomes the collected bees, and the other half becomes the observed bees;
step 4.3: all the bees are adopted to carry out directional mutation search on the original decision matrix, and the interference benefit values of all the interference machines to each radar at the moment are obtained according to the original decision matrix of the current bees; mutating interference decision results of the two radars with the minimum benefit value to obtain a new decision matrix; when f of the new decision matrix is larger than f of the original matrix according to a greedy criterion, the old decision matrix X is replaced by the newly found decision matrix X'; the search formula is as follows:
Figure FDA0002919356050000021
step 4.4: determining the honey collection bees to be followed by all observation bees according to the probability, and changing the interference strategy of the matrix corresponding to the honey collection bees based on the directional mutation to obtain a new decision matrix; the formula for the selection probability is as follows:
Figure FDA0002919356050000022
step 4.5: if the decision matrix X is searched for limit times, a larger new decision matrix f is not found, and the maximum value f and the corresponding decision matrix at the moment are stored; and returning to the step 4.2, after the operation is repeated until maxCycle is met, selecting a decision matrix X which enables the f value to be maximum, wherein the matrix X is a decision scheme capable of obtaining the optimal f value.
CN202110111032.9A 2021-01-27 2021-01-27 Interference decision method based on directional mutation search artificial bee colony algorithm Pending CN112926832A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110111032.9A CN112926832A (en) 2021-01-27 2021-01-27 Interference decision method based on directional mutation search artificial bee colony algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110111032.9A CN112926832A (en) 2021-01-27 2021-01-27 Interference decision method based on directional mutation search artificial bee colony algorithm

Publications (1)

Publication Number Publication Date
CN112926832A true CN112926832A (en) 2021-06-08

Family

ID=76167012

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110111032.9A Pending CN112926832A (en) 2021-01-27 2021-01-27 Interference decision method based on directional mutation search artificial bee colony algorithm

Country Status (1)

Country Link
CN (1) CN112926832A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116359855A (en) * 2023-04-07 2023-06-30 哈尔滨工程大学 NGO-RF-based radar interference effect evaluation method

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109444832A (en) * 2018-10-25 2019-03-08 哈尔滨工程大学 Colony intelligence interfering well cluster method based on more jamming effectiveness values
US20190080270A1 (en) * 2017-09-11 2019-03-14 Hefei University Of Technology Production scheduling method and system based on improved artificial bee colony algorithm and storage medium
CN110769436A (en) * 2018-07-26 2020-02-07 深圳市白麓嵩天科技有限责任公司 Wireless communication anti-interference decision-making method based on mutation search artificial bee colony algorithm
CN111538950A (en) * 2020-04-17 2020-08-14 中国人民解放军91550部队 Multi-unmanned platform interference resource allocation method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190080270A1 (en) * 2017-09-11 2019-03-14 Hefei University Of Technology Production scheduling method and system based on improved artificial bee colony algorithm and storage medium
CN110769436A (en) * 2018-07-26 2020-02-07 深圳市白麓嵩天科技有限责任公司 Wireless communication anti-interference decision-making method based on mutation search artificial bee colony algorithm
CN109444832A (en) * 2018-10-25 2019-03-08 哈尔滨工程大学 Colony intelligence interfering well cluster method based on more jamming effectiveness values
CN111538950A (en) * 2020-04-17 2020-08-14 中国人民解放军91550部队 Multi-unmanned platform interference resource allocation method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116359855A (en) * 2023-04-07 2023-06-30 哈尔滨工程大学 NGO-RF-based radar interference effect evaluation method

Similar Documents

Publication Publication Date Title
CN111538950B (en) Multi-unmanned platform interference resource allocation method
CN106650058B (en) Cooperative electronic interference task scheduling method based on improved artificial bee colony algorithm
CN112507622B (en) Anti-unmanned aerial vehicle task allocation method based on reinforcement learning
CN109444832B (en) Group intelligent interference decision method based on multiple interference effect values
CN111311049B (en) Multi-agent cooperative task allocation method
CN111784135B (en) System combat capability quantitative analysis method based on hyper-network and OODA (object oriented data acquisition) ring theory
CN113109770B (en) Interference resource allocation method and system
CN110768745A (en) Interference decision method under tabu search artificial bee colony algorithm
CN109946673B (en) Networked radar node selection method based on Monte Carlo tree search
CN115567353B (en) Interference multi-beam scheduling and interference power combined optimization method for radar networking system
Qingwen et al. Cooperative jamming resource allocation of UAV swarm based on multi-objective DPSO
CN112926832A (en) Interference decision method based on directional mutation search artificial bee colony algorithm
Gao et al. Multi-objective weapon target assignment based on D-NSGA-III-A
CN114519190A (en) Multi-target network security dynamic evaluation method based on Bayesian network attack graph
CN112284188A (en) Unmanned aerial vehicle attack and defense decision method based on self-adaptive step discrete wolf cluster algorithm
Liang et al. Transmission power optimization for reducing sidelobe via bat-chicken swarm optimization in distributed collaborative beamforming
Ye et al. Multiobjective Cognitive Cooperative Jamming Decision‐Making Method Based on Tabu Search‐Artificial Bee Colony Algorithm
CN114879195A (en) Unmanned aerial vehicle cluster radar communication integration method based on resource scheduling
CN114143890B (en) Method and system for optimizing transmission power in unmanned aerial vehicle communication based on overlapping channels
CN113376594B (en) STAP radar distributed interference method based on MOEA/D-GAS
Hasnan et al. Analysis of gradient-based cuckoo search for the large scale optimal RFID network planning
Ye et al. Cognitive cooperative-jamming decision method based on bee colony algorithm
CN116359858A (en) Collaborative interference resource scheduling method based on improved sparrow search algorithm
Wang et al. A multi-agent reinforcement learning-based collaborative jamming system: Algorithm design and software-defined radio implementation
Wang et al. Task decision-making for UAV swarms based on robustness evaluation

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination