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Open Access

ARTICLE

KGTLIR: An Air Target Intention Recognition Model Based on Knowledge Graph and Deep Learning

Bo Cao1,*, Qinghua Xing2, Longyue Li2, Huaixi Xing1, Zhanfu Song1
1 Graduate School, Air Force Engineering University, Xi’an, 710051, China
2 Air Defense and Antimissile School, Air Force Engineering University, Xi’an, 710051, China
* Corresponding Author: Bo Cao. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.052842

Received 17 April 2024; Accepted 08 June 2024; Published online 08 July 2024

Abstract

As a core part of battlefield situational awareness, air target intention recognition plays an important role in modern air operations. Aiming at the problems of insufficient feature extraction and misclassification in intention recognition, this paper designs an air target intention recognition method (KGTLIR) based on Knowledge Graph and Deep Learning. Firstly, the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism. Meanwhile, the accuracy, recall, and F1-score after iteration are introduced to dynamically adjust the sample weights to reduce the probability of misclassification. After that, an intention recognition model based on Knowledge Graph is constructed to predict the probability of the occurrence of different intentions of the target. Finally, the results of the two models are fused by evidence theory to obtain the target’s operational intention. Experiments show that the intention recognition accuracy of the KGTLIR model can reach 98.48%, which is not only better than most of the air target intention recognition methods, but also demonstrates better interpretability and trustworthiness.

Keywords

Dilated causal convolution; graph attention mechanism; intention recognition; air targets; knowledge graph

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