Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat. (9th November 2021)

• Record Type: Journal Article
• Authors: Guanglei, Meng; Runnan, Zhao; Biao, Wang; Mingzhe, Zhou; Yu, Wang; Xiao, Liang
• Other Names: He Shaoming Academic Editor.
• Is Part Of: International journal of aerospace engineering. Volume 2021(2021)
• Publication Date: 2021-11-09
• Subjects: Aerospace engineering -- Periodicals; 629.105
• DOI: 10.1155/2021/9558838
• Languages: English
• ISSNs: 1687-5966

Abstract

Abstract : 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.