A GRU-Based Method for Predicting Intention of Aerial Targets

Since a target’s operational intention in air combat is realized by a series of tactical maneuvers, its state presents the characteristics of temporal and dynamic changes. Depending only on a single moment to take inference, the traditional combat intention recognition method is neither scientific n...

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Autores principales:	Fei Teng, Yafei Song, Gang Wang, Peng Zhang, Liuxing Wang, Zongteng Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/8098a3a7eecf4397aa6f30622eec4d31
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id	oai:doaj.org-article:8098a3a7eecf4397aa6f30622eec4d31
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spelling	oai:doaj.org-article:8098a3a7eecf4397aa6f30622eec4d312021-11-15T01:19:26ZA GRU-Based Method for Predicting Intention of Aerial Targets1687-527310.1155/2021/6082242https://doaj.org/article/8098a3a7eecf4397aa6f30622eec4d312021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6082242https://doaj.org/toc/1687-5273Since a target’s operational intention in air combat is realized by a series of tactical maneuvers, its state presents the characteristics of temporal and dynamic changes. Depending only on a single moment to take inference, the traditional combat intention recognition method is neither scientific nor effective enough. Based on a gated recurrent unit (GRU), a bidirectional propagation mechanism and attention mechanism are introduced in a proposed aerial target combat intention recognition method. The proposed method constructs an air combat intention characteristic set through a hierarchical approach, encodes into numeric time-series characteristics, and encapsulates domain expert knowledge and experience in labels. It uses a bidirectional gated recurrent units (BiGRU) network for deep learning of air combat characteristics and adaptively assigns characteristic weights using an attention mechanism to improve the accuracy of aerial target combat intention recognition. In order to further shorten the time for intention recognition and with a certain predictive effect, an air combat characteristic prediction module is introduced before intention recognition to establish the mapping relationship between predicted characteristics and combat intention types. Simulation experiments show that the proposed model can predict enemy aerial target combat intention one sampling point ahead of time based on 89.7% intent recognition accuracy, which has reference value and theoretical significance for assisting decision-making in real-time intention recognition.Fei TengYafei SongGang WangPeng ZhangLiuxing WangZongteng ZhangHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Fei Teng Yafei Song Gang Wang Peng Zhang Liuxing Wang Zongteng Zhang A GRU-Based Method for Predicting Intention of Aerial Targets
description	Since a target’s operational intention in air combat is realized by a series of tactical maneuvers, its state presents the characteristics of temporal and dynamic changes. Depending only on a single moment to take inference, the traditional combat intention recognition method is neither scientific nor effective enough. Based on a gated recurrent unit (GRU), a bidirectional propagation mechanism and attention mechanism are introduced in a proposed aerial target combat intention recognition method. The proposed method constructs an air combat intention characteristic set through a hierarchical approach, encodes into numeric time-series characteristics, and encapsulates domain expert knowledge and experience in labels. It uses a bidirectional gated recurrent units (BiGRU) network for deep learning of air combat characteristics and adaptively assigns characteristic weights using an attention mechanism to improve the accuracy of aerial target combat intention recognition. In order to further shorten the time for intention recognition and with a certain predictive effect, an air combat characteristic prediction module is introduced before intention recognition to establish the mapping relationship between predicted characteristics and combat intention types. Simulation experiments show that the proposed model can predict enemy aerial target combat intention one sampling point ahead of time based on 89.7% intent recognition accuracy, which has reference value and theoretical significance for assisting decision-making in real-time intention recognition.
format	article
author	Fei Teng Yafei Song Gang Wang Peng Zhang Liuxing Wang Zongteng Zhang
author_facet	Fei Teng Yafei Song Gang Wang Peng Zhang Liuxing Wang Zongteng Zhang
author_sort	Fei Teng
title	A GRU-Based Method for Predicting Intention of Aerial Targets
title_short	A GRU-Based Method for Predicting Intention of Aerial Targets
title_full	A GRU-Based Method for Predicting Intention of Aerial Targets
title_fullStr	A GRU-Based Method for Predicting Intention of Aerial Targets
title_full_unstemmed	A GRU-Based Method for Predicting Intention of Aerial Targets
title_sort	gru-based method for predicting intention of aerial targets
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/8098a3a7eecf4397aa6f30622eec4d31
work_keys_str_mv	AT feiteng agrubasedmethodforpredictingintentionofaerialtargets AT yafeisong agrubasedmethodforpredictingintentionofaerialtargets AT gangwang agrubasedmethodforpredictingintentionofaerialtargets AT pengzhang agrubasedmethodforpredictingintentionofaerialtargets AT liuxingwang agrubasedmethodforpredictingintentionofaerialtargets AT zongtengzhang agrubasedmethodforpredictingintentionofaerialtargets AT feiteng grubasedmethodforpredictingintentionofaerialtargets AT yafeisong grubasedmethodforpredictingintentionofaerialtargets AT gangwang grubasedmethodforpredictingintentionofaerialtargets AT pengzhang grubasedmethodforpredictingintentionofaerialtargets AT liuxingwang grubasedmethodforpredictingintentionofaerialtargets AT zongtengzhang grubasedmethodforpredictingintentionofaerialtargets
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A GRU-Based Method for Predicting Intention of Aerial Targets

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