Journal of Defense Management

Journal of Defense Management
Open Access

ISSN: 2167-0374


Journal of Defense Management : Citations & Metrics Report

Articles published in Journal of Defense Management have been cited by esteemed scholars and scientists all around the world. Journal of Defense Management has got h-index 9, which means every article in Journal of Defense Management has got 9 average citations.

Following are the list of articles that have cited the articles published in Journal of Defense Management.

  2020 2019 2018 2017 2016

Year wise published articles

4 5 9 14 20

Year wise citations received

120 71 71 45 19
Journal total citations count 406
Journal impact factor 8.57
Journal 5 years impact factor 6.27
Journal cite score 9.59
Journal h-index 9
Journal h-index since 2016 9
Important citations

Haldane, AG and Turrell AE. An interdisciplinary model for macroeconomics. Inst Econ Mag. 2019; 21 (40), 69-111.

Xinyi Z, Qun Z, Ruilong Z, Bailing T, Xiuyun Z, Cong F. Application of Brain-Like Intelligent Technology to Unmanned Systems. Control Theory and Applications. 2019; 36 (1): 1-2.

Xiaofeng H, Jiuyang T. AlphaGo's breakthrough and war chess deduction challenge. Sci Technol Rev. 2017; 35(21):49-60.

Haldane AG, Turrel AE. An interdisciplinary model for macroeconomics. Inst Econ Mag. 2019; 21(40): 69-110.

Haldane AG, Turrel AE. Un modelo interdisciplinary para la macroeconomia. Revista de Economía institucional. 2019; 21(40): 69-110.

Heydarian Pashakhanlou A.  AI, autonomy, and airpower: The end of pilots?. Defence Studies. 2019; 19(4): 337-352.

Shin H, Lee J, Kim H, Shim DH. An autonomous aerial combat framework for two-on-two engagements based on basic fighter maneuvers. Aerosp Sci Technol. 2018; 72: 305-315.

Ludwig J, Presnell B. Developing an adaptive opponent for tactical training. InInternational Conference on Human-Computer Interaction. Springer Cham. 2019; 11597: 532-541.

Zuluaga JA, Carrasco EP, Pabon JD, Leon KG, Montoya OL. A data fusion system for simulation of critical scenarios and decision-making. Neogranadine Sci Eng. 2020; 30(1).

Deng B, Collier T. Increasing relative angular velocity for air combat in zero gravity. Aerospace Systems. 2019; 2(2): 83-95.

Zhou Y, Tang Y, Zhao X. A novel uncertainty management approach for air combat situation assessment based on improved belief entropy. Entropy. 2019; 21(5): 495.

Wallace R. Cognitive instabilities under contention, friction, and the fog-of-war challenge the AI revolution. Connect Sci. 2019; 5: 1-6.

Krupiy T. Regulating a game changer: Using a distributed approach to develop an accountability framework for lethal autonomous weapon systems. Geo. J. Int'l L. 2018; 50:45.

Bauckhage C, Ojeda C, Schücker J, Sifa R, Wrobel S. Informed machine learning through functional composition. InLWDA. 2018; 33-37.

Zhang X, Liu G, Yang C, Wu J. Research on air combat maneuver decision-making method based on reinforcement learning. Elect. 2018; 7(279): 1-18.

Shi Pengfei. Development and challenges of autonomous control technology for drones. Sci Technol Rev. 2017; 35 (7): 32-38.

Sathyan A, Ma O. Collaborative control of multiple robots using genetic fuzzy systems. Robotica. 2019; 37(11):1922-1936.

Dong Y. Deep learning-based opponent aircraft attitude detection in autonomous air combat. J Aerosp Inf Syst. 2019;16(4): 162-167.

Zhang X, Liu G, Yang C, Wu J. Research on air confrontation maneuver decision-making method based on reinforcement learning. Elect. 2018; 7(11): 279.

Leuenberger G, Wiering MA. Actor-critic reinforcement learning with neural networks in continuous games. InICAART. 2018; 2: 53-60.

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