Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic
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. De...
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doaj-10b008c20a4545839be12e39d04218c02020-11-25T01:25:59ZengMDPI AGElectronics2079-92922020-09-0191511151110.3390/electronics9091511Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return HeuristicKai Zhang0Deyun Zhou1Zhen Yang2Yiyang Zhao3Weiren Kong4School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, ChinaThe 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.https://www.mdpi.com/2079-9292/9/9/1511weapon target assignmentOODAheuristic algorithmcombinatorial optimizationdecision support system |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kai Zhang Deyun Zhou Zhen Yang Yiyang Zhao Weiren Kong |
spellingShingle |
Kai Zhang Deyun Zhou Zhen Yang Yiyang Zhao Weiren Kong Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic Electronics weapon target assignment OODA heuristic algorithm combinatorial optimization decision support system |
author_facet |
Kai Zhang Deyun Zhou Zhen Yang Yiyang Zhao Weiren Kong |
author_sort |
Kai Zhang |
title |
Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic |
title_short |
Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic |
title_full |
Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic |
title_fullStr |
Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic |
title_full_unstemmed |
Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic |
title_sort |
efficient decision approaches for asset-based dynamic weapon target assignment by a receding horizon and marginal return heuristic |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2020-09-01 |
description |
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. |
topic |
weapon target assignment OODA heuristic algorithm combinatorial optimization decision support system |
url |
https://www.mdpi.com/2079-9292/9/9/1511 |
work_keys_str_mv |
AT kaizhang efficientdecisionapproachesforassetbaseddynamicweapontargetassignmentbyarecedinghorizonandmarginalreturnheuristic AT deyunzhou efficientdecisionapproachesforassetbaseddynamicweapontargetassignmentbyarecedinghorizonandmarginalreturnheuristic AT zhenyang efficientdecisionapproachesforassetbaseddynamicweapontargetassignmentbyarecedinghorizonandmarginalreturnheuristic AT yiyangzhao efficientdecisionapproachesforassetbaseddynamicweapontargetassignmentbyarecedinghorizonandmarginalreturnheuristic AT weirenkong efficientdecisionapproachesforassetbaseddynamicweapontargetassignmentbyarecedinghorizonandmarginalreturnheuristic |
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