Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic

• Published in: Electronics (Basel) 2020-09, Vol.9 (9), p.1511
• Main Authors: Zhang, Kai, Zhou, Deyun, Yang, Zhen, Zhao, Yiyang, Kong, Weiren
• Format: Article
• Language: English
• Subjects: Algorithms; Command and control; Decision making; Game theory; Genetic algorithms; Heuristic; Heuristic methods; Operations research; Optimization; Probability distribution; Weapons
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics9091511

The weapon-target assignment problem is a crucial decision support in a Command and Control system. As a typical operational scenario, the major asset-based dynamic weapon target assignment (A-DWTA) models and solving algorithms are challenging to reflect the actual requirement of decision maker. Deriving from the “shoot–look–shoot” principle, an “observe–orient–decide–act” loop model for A-DWTA (OODA/A-DWTA) is established. Focus on the decide phase of the OODA/A-DWTA loop, a novel A-DWTA model, which is based on the receding horizon decomposition strategy (A-DWTA/RH), is established. To solve the A-DWTA/RH efficiently, a heuristic algorithm based on statistical marginal return (HA-SMR) is designed, which proposes a reverse hierarchical idea of “asset value-target selected-weapon decision.” Experimental results show that HA-SMR solving A-DWTA/RH has advantages of real-time and robustness. The obtained decision plan can fulfill the operational mission in the fewer stages and the “radical-conservative” degree can be adjusted adaptively by parameters.