Reduction of Decision-Making Time in the Air Defense Management

Abstract : The importance of air defense systems increased during the Second World War as they showed they could reduce the efficiency of strong offensive capabilities. Research and development since that time has caused the rapid evolution of air defense systems. Along with the inclusion of network-enabled capability, there has become a mass of information. As a result of this, evaluation of information which is dynamic at battlefield has become harder and there has been a requirement for usage of computer-based systems that could make the process effective and shorten it. In this study, detailed literature scan has been done and has been used value focus thinking as an analysis method, the information that has been obtained from network enabled capability couldn't be used effectively in air defense management. It has been stated that studies on these deficiencies is continuing and countries such as U.S. Navy and China is in advance in this field. To eliminate deficiencies that mentioned above, some suggestions have been made in this study. It has been evaluated that the process of decision-making is going to be shortened and it will become more effective and economic.

[1]  Gao Xiao-guang,et al.  Target assignment for multi-fighters coordinated air combat based on VLSN , 2011, 2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC).

[2]  Fredrik Johansson,et al.  A Bayesian network approach to threat evaluation with application to an air defense scenario , 2008, 2008 11th International Conference on Information Fusion.

[3]  Li Yongxin,et al.  Index Weight Technology in Threat Evaluation Based on Improved Grey Theory , 2008, 2008 International Symposium on Intelligent Information Technology Application Workshops.

[4]  Zhong Liu,et al.  Particle Swarm Optimization Based on Genetic Operators for Sensor-Weapon-Target Assignment , 2012, 2012 Fifth International Symposium on Computational Intelligence and Design.

[5]  Greg L. Zacharias,et al.  Air combat tactics optimization using stochastic genetic algorithms , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[6]  Arvind Kumar,et al.  Exact and Heuristic Algorithms for the Weapon-Target Assignment Problem , 2007, Oper. Res..

[7]  David M. Jurk.  Decision Analysis with Value Focused Thinking as a Methodology to Select Force Protection Initiatives for Evaluation , 2012 .

[8]  R Séguin,et al.  Real-time decision problems: an operational research perspective , 1997 .

[9]  Michael J. Liebhaber,et al.  Naval Air Defense Threat Assessment: Cognitive Factors and Model , 2000 .

[10]  Atulya K. Nagar,et al.  Maximization of Expected Target Damage Value Using Quantum Particle Swarm Optimization , 2009, 2009 Second International Conference on Developments in eSystems Engineering.

[11]  Chou-Yuan Lee,et al.  Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Zou Feng-xing,et al.  A novel approach to solving weapon-target assignment problem based on hybrid particle swarm optimization algorithm , 2011, Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology.

[13]  Thomas K. Adams.  Future Warfare and the Decline of Human Decisionmaking , 2001, The US Army War College Quarterly: Parameters.

[14]  Michael J. Liebhaber,et al.  Air Threat Assessment: Research, Model, and Display Guidelines , 2002 .

[15]  Fredrik Johansson,et al.  Real-time Allocation of Firing Units To Hostile Targets , 2011, J. Adv. Inf. Fusion.

[16]  M. Azak,et al.  A new approach for Threat Evaluation and Weapon Assignment problem, hybrid learning with multi-agent coordination , 2008, 2008 23rd International Symposium on Computer and Information Sciences.

[17]  Shaolei Wang,et al.  Solving Weapon-Target Assignment Problems by Cultural Particle Swarm Optimization , 2012, 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[18]  Xiaohui Zhang,et al.  Analysis of Aerial Targets Threat Degree in Air Defense System , 2010, 2010 International Conference on Computational Intelligence and Software Engineering.

[19]  E.M. Hamed,et al.  A flexible fuzzy threat evaluation computer system , 2004, International Conference on Electrical, Electronic and Computer Engineering, 2004. ICEEC '04..

[20]  Asif Masood,et al.  A two-stage dynamic decision support based optimal threat evaluation and defensive resource scheduling algorithm for multi air-borne threats: Asset-based dynamic weapon scheduling using artificial intelligence techinques , 2010, 2010 IEEE 9th International Conference on Cyberntic Intelligent Systems.

[21]  L. Niklasson.  Evaluating the Performance of TEWA Systems , 2010 .

[22]  J. Freeman,et al.  Integrated Critical Thinking Training and Decision Support for Tactical Anti-Air Warfare , 2002 .

[23]  Wang Jun,et al.  A solving algorithm for Target Assignment optimization model based on ACO , 2010, 2010 Sixth International Conference on Natural Computation.

[24]  Qing Xue,et al.  Threat evaluation model of targets based on information entropy and fuzzy optimization theory , 2011, 2011 IEEE International Conference on Industrial Engineering and Engineering Management.

[25]  A Benaskeur,et al.  Combat Resource Allocation Planning in Naval Engagements , 2007 .