基于改进离散布谷鸟算法的干扰资源分配研究

李东生; 高杨; 雍爱霞

doi:10.11999/JEIT150726

基于改进离散布谷鸟算法的干扰资源分配研究

doi: 10.11999/JEIT150726

基金项目:

国家自然科学基金(61179036)

计量
- 文章访问数: 1558
- HTML全文浏览量: 113
- PDF下载量: 385
- 被引次数: 0
出版历程
- 收稿日期: 2015-06-15
- 修回日期: 2015-11-27
- 刊出日期: 2016-04-19

Jamming Resource Allocation via Improved Discrete Cuckoo Search Algorithm

Funds:

The National Natural Science Foundation of China (61179036)

摘要

摘要: 该文对二次雷达机制的协同式敌我识别系统的干扰资源分配问题进行研究，将离散布谷鸟算法(Discrete Cuckoo Search, DCS)引入敌我识别系统的干扰资源分配问题。首先给出协同式敌我识别系统的干扰效果评估指标，建立干扰资源分配模型，将模型简化为一对一、多对少模型，使用DCS算法求解模型。针对Levy飞行后期出现搜索速度慢和精度低的问题，将遗传算法中的交叉与变异操作引入DCS算法得到改进的离散布谷鸟算法(Improved Discrete Cuckoo Search, DCS)，用以求解分配模型。仿真分析表明：所提干扰效果评估指标可以合理地评估干扰效果；IDCS算法比IDCS算法收敛更快、耗时更短；IDCS算法与做出相应改进的遗传算法(Improved Genetic Algorithm, IGA)相比具有更好的寻优能力。
- 协同式敌我识别 /
- 干扰资源 /
- 资源分配 /
- 改进离散布谷鸟算法
Abstract: Jamming resource distribution of cooperative identification friend or foe via secondary radar is researched by introducing Discrete Cuckoo Search (DCS) algorithm. The jamming effect evaluation rules and indexes are given, and the aim function and distribution model are given. According to the analysis, distribution models can be changed into one-to-one model and much-to-little model, which can be solved by DCS algorithm. Owing to the slow searching speed and low precision in the Levy flights later stage, the crossover and variation are introduced into DCS algorithm, which gets Improved Discrete Cuckoo Search (IDCS) algorithm. The simulation results show that the jamming effect judging index is effective, the IDCS algorithm has a faster convergence speed than the DCS algorithm, and it has a better searching optimization speed than Improved Genetic Algorithm (IGA).
- Cooperation identification friend or foe /
- Jamming resource /
- Resource distribution /
- Improved Discrete Cuckoo Search (IDCS) algorithm

HTML全文

参考文献(27)

但波, 姜永华, 李敬军, 等. 基于空时融合隐马尔科夫模型的舰艇编队目标识别方法[J].电子与信息学报, 2015, 37(4): 926-932. doi: 10.11999/JEIT140589.

DAN Bo, JIANG Yonghua, LI Jingjun, et al. Ship formation target recognition based on spatial and temporal fusion hidden Markov model[J].Journal of Electronics Information Technology, 2015, 37(4): 926-932. doi: 10.11999/ JEIT140589.

孟庆昕, 杨士莪, 于盛齐. 基于波形结构特征和支持向量机的水面目标识别[J]. 电子与信息学报, 2015, 37(9): 2117-2123. doi: 10.11999/JEIT150139.

MENG Qingxin, YANG Shie, and YU Shengqi. Recognition

of marine acoustic target signals based on wave structure and support vector machine[J]. Journal of Electronics Information Technology, 2015, 37(9): 2117-2123. doi: 10. 11999/JEIT150139.

胡莹, 黄永明, 俞菲, 等. 多用户大规模MIMO系统能效资源分配算法[J]. 电子与信息学报, 2015, 37(9): 2198-2203. doi: 10.11999/JEIT150088.

HU Ying, HUANG Yongming, YU Fei, et al. Energy-efficient resource allocation based on multi-user massive MIMO system[J]. Journal of Electronics Information Technology, 2015, 37(9): 2198-2203. doi: 10.11999/JEIT150088.

沈阳, 陈永光, 李修和. 基于0-1规划的雷达干扰资源优化分配研究[J]. 兵工学报, 2007, 28(5): 528-532.

SHEN Yang, CHEN Yongguang, and LI Xiuhe. Research on optimal distribution of radar jamming resource based on zero-one programming[J]. Acta Armamentarii, 2007, 28(5): 528-532.

吕永胜, 王树宗, 王向伟, 等. 基于贴近度的雷达干扰资源分配策略研究[J]. 系统工程与电子技术, 2005, 27(11): 1893-1894.

LV Yongsheng, WANG Shuzong, WANG Xiangwei, et al. Study on the allocation tactics for radar jamming resources based on close degree[J]. Systems Engineering and Electronics, 2005, 27(11): 1893-1894.

刘以安, 倪天权, 张秀辉, 等. 模拟退火算法在雷达干扰资源优化分配中的应用[J]. 系统工程与电子技术, 2009, 31(8): 1914-1917.

LIU Yian, NI Tianquan, ZHANG Xiuhui, et al. Application of simulated annealing algorithm in optimizing allocation of radar jamming resources[J]. Systems Engineering and Electronics, 2009, 31(8): 1914-1917.

张养瑞, 李云杰, 高梅国. 协同干扰资源优化分配模型及算法[J]. 系统工程与电子技术, 2014, 36(9): 1744-1749.

ZHANG Yangrui, LI Yunjie, and GAO Meiguo. Optimal assignment model and solution of cooperative jamming resources[J]. Systems Engineering and Electronics, 2014, 36(9): 1744-1749.

ZHAI X F and ZHUANG Y. IIGA based algorithm for cooperative jamming resource allocation[C]. Asia Pacific Conference on Postgraduate Research, Shanghai, China, 2009: 368-371.

XUE Y, ZHUANG Y, NI T Q, et al. One improved genetic algorithm applied in the problem of dynamic jam resource scheduling with multi-objective and multi-constraint[C]. IEEE 5th International Conference on Bio-inspired Computing: Theories and Applications, Shanghai, China, 2010: 708-712.

XUE Y, ZHUANG Y, NI T Q, et al. Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem[J]. Journal of Systems Engineering and Electronics, 2014, 25(1): 59-68.

YANG X S and DEB S. Cuckoo search via levy flights[C]. Proceedings of IEEE World Congress on Nature Biological Inspired Computing, India, 2009: 210-214.

YANG X S and DEB S. Multi objective cuckoo search for design optimization[J]. Computers Operations Research, 2011, 10(9): 1-9.

ZHENG H Q and ZHOU Y Q. A discrete binary version of cuckoo search for knapsack problems[J]. Advances in Information Science and Service Sciences, 2012, 4(18): 331-339.

OUYANG X X, ZHOU Y Q, LUO Q F, et al. A novel discrete cuckoo search algorithm for spherical traveling salesman problem[J]. Applied Mathematical Information Sciences, 2013, 7(2): 777-784.

丁锋, 黄建冲, 施运山. 基于脉冲信息的敌我识别信号快速分选识别研究[J]. 舰船电子对抗, 2014, 37(1): 5-10.

DING Feng, HUANG Jianchong, and SHI Yunshan. Research into fast sorting and recognition of IFF signal based on pulse information[J]. Shipboard Electronic Countermeasure, 2014, 37(1): 5-10.

宋海方, 吴华, 程嗣怡, 等. 多波束干扰系统干扰资源综合管理算法[J]. 兵工学报, 2013, 34(3): 332-338.

SONG Haifang, WU Hua, CHENG Siyi, et al. Integrated management algorithm of jamming resource in multi-beam jamming systems[J]. Acta Armamentarii, 2013, 34(3): 332-338.

KENNEDY J and EBERHART R C. A discrete version of the particle swarm algorithm[C]. IEEE International Conference on Systems, Man, and Cybernetics, Piscataway, 1997: 4104-4109.

施引文献

资源附件(0)

访问统计