Preferable selection of offensive mine-laying plans for unmanned aerial vehicles based on set pair analysis

doi:10.3724/SP.J.1087.2011.02818

Journal of Computer Applications ›› 2011, Vol. 31 ›› Issue (10): 2818-2820.DOI: 10.3724/SP.J.1087.2011.02818

• Artificial intelligence • Previous Articles Next Articles

Preferable selection of offensive mine-laying plans for unmanned aerial vehicles based on set pair analysis

DONG Wen-hong¹, YI Bo², LIN Qiang²

1.Department of Command, Naval Aeronautical and Astronautical University, Yantai Shandong 264001, China
2.Graduate Students' Brigade, Naval Aeronautical and Astronautical University, Yantai Shandong 264001, China

Received:2011-04-06 Revised:2011-06-04 Online:2011-10-11 Published:2011-10-01
Contact: Bo YI

基于集对分析的无人机攻势布雷方案优选

董文洪¹,易波²,林强²

1.海军航空工程学院指挥系,山东烟台 264001
2.海军航空工程学院研究生管理大队,山东烟台 264001

通讯作者: 易波
作者简介:董文洪(1967-),男,山东淄博人,副教授,主要研究方向:海军作战运筹;易波(1987-),男,湖南益阳人,硕士研究生,主要研究方向:海军航空兵作战运筹;林强(1981-),男,山东栖霞人,硕士研究生,主要研究方向:海军航空兵战术。

Abstract

Abstract: In order to solve the preferable selection problem of offensive mine-laying plans for unmanned aerial vehicles, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on connection number was adopted. With reference to the traditional essential factors for evaluation of mine-laying plans, the synthesized parameter of Unmanned Aerial Vehicle (UAV) was also selected as the main index to scale the effect of mine-laying platform. Then the connection number was used to describe the identity-contrary degree based on integrated plan, while the information entropy weight of each factor was calculated. For the reliability of the ranking process, the relative certainty probability power was improved with the stabilization coefficient, which could be used to evaluate and rank the mine-laying plans. The simulation results indicate the availability of this algorithm.

Key words: Unmanned Aerial Vehicle (UAV), mine-laying, Set Pair Analysis (SPA), connection number, ideal plan

摘要： 为解决无人机攻势布雷方案优选问题,采用基于联系度的逼近理想解的排序法(TOPSIS)进行求解。在传统评估要素基础上提出无人机综合作战系数来衡量布雷平台影响,并利用联系度刻画备选方案与综合理想方案组成的集对在相对接近程度意义下的同一对立程度。根据特征属性值计算信息熵权,并改进相对确定可能势,以实现无人机攻势布雷方案的综合评估和排序,算例验证了该方法的有效性。

关键词: 无人机, 布雷, 集对分析, 联系度, 理想方案

CLC Number:

TP182

DONG Wen-hong YI Bo LIN Qiang. Preferable selection of offensive mine-laying plans for unmanned aerial vehicles based on set pair analysis[J]. Journal of Computer Applications, 2011, 31(10): 2818-2820.

董文洪易波林强. 基于集对分析的无人机攻势布雷方案优选[J]. 计算机应用, 2011, 31(10): 2818-2820.

[1]	Lingxia MU, Zhengjun ZHOU, Ban WANG, Youmin ZHANG, Xianghong XUE, Kaikai NING. Formation obstacle-avoidance and reconfiguration method for multiple UAVs [J]. Journal of Computer Applications, 2024, 44(9): 2938-2946.
[2]	Tianyu HUANG, Yuanxing LI, Hao CHEN, Zijia GUO, Mingjun WEI. User cluster partitioning method based on weighted fuzzy clustering in ground-air collaboration scenarios [J]. Journal of Computer Applications, 2024, 44(5): 1555-1561.
[3]	Zhiqiang ZHENG, Haibin DUAN. Short-range UAV air combat maneuver decision-making via finite tolerance pigeon-inspired optimization [J]. Journal of Computer Applications, 2024, 44(5): 1401-1407.
[4]	Rui TANG, Shibo YUE, Ruizhi ZHANG, Chuan LIU, Chuanlin PANG. Energy efficiency optimization mechanism for UAV-assisted and non-orthogonal multiple access-enabled data collection system [J]. Journal of Computer Applications, 2024, 44(4): 1209-1218.
[5]	Meiyu CAI, Runzhe ZHU, Fei WU, Kaiyu ZHANG, Jiale LI. Cross-view matching model based on attention mechanism and multi-granularity feature fusion [J]. Journal of Computer Applications, 2024, 44(3): 901-908.
[6]	Huzhen GAO, Changping DU, Yao ZHENG. Gimbal system control algorithm of unmanned aerial vehicle based on extended state observer [J]. Journal of Computer Applications, 2024, 44(2): 604-610.
[7]	Lin WANG, Jingliang LIU, Wuwei WANG. Small target detection method in UAV images based on fusion of dilated convolution and Transformer [J]. Journal of Computer Applications, 2024, 44(11): 3595-3602.
[8]	Jian SUN, Baoquan MA, Zhuiwei WU, Xiaohuan YANG, Tao WU, Pan CHEN. Joint optimization of UAV swarm path planning and task allocation balance in earthquake scenarios [J]. Journal of Computer Applications, 2024, 44(10): 3232-3239.
[9]	Zhongyuan ZHANG, Wei DAI, Guangyu LI, Xiaoqing CHEN, Qibo DENG. Cooperative obstacle avoidance algorithm based on improved artificial potential field and consensus protocol [J]. Journal of Computer Applications, 2023, 43(8): 2644-2650.
[10]	Yu WANG, Tianjun REN, Zilin FAN. Air combat maneuver decision-making of unmanned aerial vehicle based on guided Minimax-DDQN [J]. Journal of Computer Applications, 2023, 43(8): 2636-2643.
[11]	Xiaolin LI, Yusang JIANG. Task offloading algorithm for UAV-assisted mobile edge computing [J]. Journal of Computer Applications, 2023, 43(6): 1893-1899.
[12]	Chaoshuai QI, Wensi HE, Yi JIAO, Yinghong MA, Wei CAI, Suping REN. Survey on anomaly detection algorithms for unmanned aerial vehicle flight data [J]. Journal of Computer Applications, 2023, 43(6): 1833-1841.
[13]	Xianlan WANG, Jinkun ZHOU, Nan MU, Chen WANG. Cross-view geo-localization method based on multi-task joint learning [J]. Journal of Computer Applications, 2023, 43(5): 1625-1635.
[14]	Yanan SUN, Jiehong WU, Junling SHI, Lijun GAO. Multi-UAV collaborative task assignment method based on improved self-organizing map [J]. Journal of Computer Applications, 2023, 43(5): 1551-1556.
[15]	Longbao WANG, Yinqi LUAN, Liang XU, Xin ZENG, Shuai ZHANG, Shufang XU. Route planning method of UAV swarm based on dynamic cluster particle swarm optimization [J]. Journal of Computer Applications, 2023, 43(12): 3816-3823.

Preferable selection of offensive mine-laying plans for unmanned aerial vehicles based on set pair analysis

基于集对分析的无人机攻势布雷方案优选

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics