Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat

Accurately identifying the tactical intention of the target can facilitate the prediction of the opponent’s behavior and improve the efficiency of collaborative decision. We have observed that traditional methods could achieve high recognition rate on conventional tactical intent. Nevertheless, thei...

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Autores principales:	Meng Guanglei, Zhao Runnan, Wang Biao, Zhou Mingzhe, Wang Yu, Liang Xiao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/d1b98a781b1a458782d8e9bb84d2d2cb
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id	oai:doaj.org-article:d1b98a781b1a458782d8e9bb84d2d2cb
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spelling	oai:doaj.org-article:d1b98a781b1a458782d8e9bb84d2d2cb2021-11-22T01:10:39ZTarget Tactical Intention Recognition in Multiaircraft Cooperative Air Combat1687-597410.1155/2021/9558838https://doaj.org/article/d1b98a781b1a458782d8e9bb84d2d2cb2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9558838https://doaj.org/toc/1687-5974Accurately identifying the tactical intention of the target can facilitate the prediction of the opponent’s behavior and improve the efficiency of collaborative decision. We have observed that traditional methods could achieve high recognition rate on conventional tactical intent. Nevertheless, their performance would deteriorate seriously when recognizing cooperative tactical intention in multiaircraft air combat environment. The main reason resides on key features that are difficult to extract for traditional methods. To this end, this paper proposes a novel approach to recognizing tactical intention of multiaircraft cooperative air combat. Specifically, we employ support vector machine (SVM) to forecast the attack intention based on 19 low correlation features. The purpose of the employment of SVM is to avoid local optimization and reduce data dimension. Moreover, we use three models, i.e., dynamic Bayesian network (DBN), radar model, and threat assessment model to extract crucial information regarding maneuver occupancy, silent penetration, and attack tendency. The extracted information would make great contribution to the recognition accuracy of six types of cooperative tactics. Finally, we learn a decision tree model on train samples processed by above two phases to classify different tactical intention. In order to verify the effectiveness of the proposed method, we use data sets from a loop simulation platform. The experimental results have approved the superiority of our method via the comparison to several baseline methods with respect to recognition rate and efficiency. In addition, we underline that our method also performs well on incomplete and uncertain information.Meng GuangleiZhao RunnanWang BiaoZhou MingzheWang YuLiang XiaoHindawi LimitedarticleMotor vehicles. Aeronautics. AstronauticsTL1-4050ENInternational Journal of Aerospace Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	Motor vehicles. Aeronautics. Astronautics TL1-4050 Meng Guanglei Zhao Runnan Wang Biao Zhou Mingzhe Wang Yu Liang Xiao Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat
description	Accurately identifying the tactical intention of the target can facilitate the prediction of the opponent’s behavior and improve the efficiency of collaborative decision. We have observed that traditional methods could achieve high recognition rate on conventional tactical intent. Nevertheless, their performance would deteriorate seriously when recognizing cooperative tactical intention in multiaircraft air combat environment. The main reason resides on key features that are difficult to extract for traditional methods. To this end, this paper proposes a novel approach to recognizing tactical intention of multiaircraft cooperative air combat. Specifically, we employ support vector machine (SVM) to forecast the attack intention based on 19 low correlation features. The purpose of the employment of SVM is to avoid local optimization and reduce data dimension. Moreover, we use three models, i.e., dynamic Bayesian network (DBN), radar model, and threat assessment model to extract crucial information regarding maneuver occupancy, silent penetration, and attack tendency. The extracted information would make great contribution to the recognition accuracy of six types of cooperative tactics. Finally, we learn a decision tree model on train samples processed by above two phases to classify different tactical intention. In order to verify the effectiveness of the proposed method, we use data sets from a loop simulation platform. The experimental results have approved the superiority of our method via the comparison to several baseline methods with respect to recognition rate and efficiency. In addition, we underline that our method also performs well on incomplete and uncertain information.
format	article
author	Meng Guanglei Zhao Runnan Wang Biao Zhou Mingzhe Wang Yu Liang Xiao
author_facet	Meng Guanglei Zhao Runnan Wang Biao Zhou Mingzhe Wang Yu Liang Xiao
author_sort	Meng Guanglei
title	Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat
title_short	Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat
title_full	Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat
title_fullStr	Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat
title_full_unstemmed	Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat
title_sort	target tactical intention recognition in multiaircraft cooperative air combat
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/d1b98a781b1a458782d8e9bb84d2d2cb
work_keys_str_mv	AT mengguanglei targettacticalintentionrecognitioninmultiaircraftcooperativeaircombat AT zhaorunnan targettacticalintentionrecognitioninmultiaircraftcooperativeaircombat AT wangbiao targettacticalintentionrecognitioninmultiaircraftcooperativeaircombat AT zhoumingzhe targettacticalintentionrecognitioninmultiaircraftcooperativeaircombat AT wangyu targettacticalintentionrecognitioninmultiaircraftcooperativeaircombat AT liangxiao targettacticalintentionrecognitioninmultiaircraftcooperativeaircombat
_version_	1718418369889173504

Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat

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