BiLSTM-Attention: An Air Target Tactical Intention Recognition Model

In the traditional air target tactical intention recognition process, reasoning analysis is only based on a single moment, but in the actual battlefield, the target tactical intention is realized by a series of actions, so the target state presents the characteristics of dynamic and temporal changes...

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Autor principal:	Teng Fei, Liu Shu, Song Yafei
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Aero Weaponry 2021
Materias:	\|intention recognition\|attention mechanism\|bi-directional long-term and short-term memory network\|air target\|temporal feature Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/f8d226ac30d7407e869271679e9331b9
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:f8d226ac30d7407e869271679e9331b9
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spelling	oai:doaj.org-article:f8d226ac30d7407e869271679e9331b92021-11-30T00:13:23ZBiLSTM-Attention: An Air Target Tactical Intention Recognition Model1673-504810.12132/ISSN.1673-5048.2020.0245https://doaj.org/article/f8d226ac30d7407e869271679e9331b92021-10-01T00:00:00Zhttps://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/1636698843694-2047015364.pdfhttps://doaj.org/toc/1673-5048In the traditional air target tactical intention recognition process, reasoning analysis is only based on a single moment, but in the actual battlefield, the target tactical intention is realized by a series of actions, so the target state presents the characteristics of dynamic and temporal changes. In order to solve this problem, the bi-directional(Bidirectional) propagation mechanism and attention(Attention) mechanism are introduced on the basis of long-term and short-term memory network (LSTM), and a target tactical intention recognition model based on BiLSTM-Attention is proposed. The hierarchical method is used to construct the feature set of air combat intention and encode it into time series feature. The decision maker’s experience is encapsulated into a label. The deep information in the feature vector of air combat intention is learned by BiLSTM neural network, and the network weight is adaptively assigned by attention mechanism. Finally, the air combat feature information with different weights is put into Softmax function layer for intention recognition. Through the comparison with the traditional air tactical target intention recognition model and the analysis of ablation experimental, the proposed model effectively improves the recognition efficiency of air target tactical intention. It has important theoretical significance and reference value for auxiliary combat system.Teng Fei, Liu Shu, Song YafeiEditorial Office of Aero Weaponryarticle\|intention recognition\|attention mechanism\|bi-directional long-term and short-term memory network\|air target\|temporal featureMotor vehicles. Aeronautics. AstronauticsTL1-4050ZHHangkong bingqi, Vol 28, Iss 5, Pp 24-32 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	\|intention recognition\|attention mechanism\|bi-directional long-term and short-term memory network\|air target\|temporal feature Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	\|intention recognition\|attention mechanism\|bi-directional long-term and short-term memory network\|air target\|temporal feature Motor vehicles. Aeronautics. Astronautics TL1-4050 Teng Fei, Liu Shu, Song Yafei BiLSTM-Attention: An Air Target Tactical Intention Recognition Model
description	In the traditional air target tactical intention recognition process, reasoning analysis is only based on a single moment, but in the actual battlefield, the target tactical intention is realized by a series of actions, so the target state presents the characteristics of dynamic and temporal changes. In order to solve this problem, the bi-directional(Bidirectional) propagation mechanism and attention(Attention) mechanism are introduced on the basis of long-term and short-term memory network (LSTM), and a target tactical intention recognition model based on BiLSTM-Attention is proposed. The hierarchical method is used to construct the feature set of air combat intention and encode it into time series feature. The decision maker’s experience is encapsulated into a label. The deep information in the feature vector of air combat intention is learned by BiLSTM neural network, and the network weight is adaptively assigned by attention mechanism. Finally, the air combat feature information with different weights is put into Softmax function layer for intention recognition. Through the comparison with the traditional air tactical target intention recognition model and the analysis of ablation experimental, the proposed model effectively improves the recognition efficiency of air target tactical intention. It has important theoretical significance and reference value for auxiliary combat system.
format	article
author	Teng Fei, Liu Shu, Song Yafei
author_facet	Teng Fei, Liu Shu, Song Yafei
author_sort	Teng Fei, Liu Shu, Song Yafei
title	BiLSTM-Attention: An Air Target Tactical Intention Recognition Model
title_short	BiLSTM-Attention: An Air Target Tactical Intention Recognition Model
title_full	BiLSTM-Attention: An Air Target Tactical Intention Recognition Model
title_fullStr	BiLSTM-Attention: An Air Target Tactical Intention Recognition Model
title_full_unstemmed	BiLSTM-Attention: An Air Target Tactical Intention Recognition Model
title_sort	bilstm-attention: an air target tactical intention recognition model
publisher	Editorial Office of Aero Weaponry
publishDate	2021
url	https://doaj.org/article/f8d226ac30d7407e869271679e9331b9
work_keys_str_mv	AT tengfeiliushusongyafei bilstmattentionanairtargettacticalintentionrecognitionmodel
_version_	1718406900716929024

BiLSTM-Attention: An Air Target Tactical Intention Recognition Model

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