2023
DOI: 10.3390/e25040671
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Air Combat Intention Recognition with Incomplete Information Based on Decision Tree and GRU Network

Abstract: Battlefield information is generally incomplete, uncertain, or deceptive. To realize enemy intention recognition in an uncertain and incomplete air combat information environment, a novel intention recognition method is proposed. After repairing the missing state data of an enemy fighter, the gated recurrent unit (GRU) network, supplemented by the highest frequency method (HFM), is used to predict the future state of enemy fighter. An intention decision tree is constructed to extract the intention classificati… Show more

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Cited by 3 publications
(1 citation statement)
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“…Zhou et al [24] combined the advantages of the long short-term memory (LSTM) networks and decision tree developing an intention prediction method. Xia et al [25] utilized the gated recurrent unit (GRU) network supplemented by the highest frequency method (HFM) to realize enemy intention recognition. However, deep learning only adaptively learns the expression from the original data, ignoring the guidance of prior knowledge to the training.…”
Section: Deep Learning-based Methodsmentioning
confidence: 99%
“…Zhou et al [24] combined the advantages of the long short-term memory (LSTM) networks and decision tree developing an intention prediction method. Xia et al [25] utilized the gated recurrent unit (GRU) network supplemented by the highest frequency method (HFM) to realize enemy intention recognition. However, deep learning only adaptively learns the expression from the original data, ignoring the guidance of prior knowledge to the training.…”
Section: Deep Learning-based Methodsmentioning
confidence: 99%