Analysis of airborne formation combat intent based on attention-based multi-level LSTM

Tianyu Ni; Yonghong Chen; Han Li; Gang Sun; Wenhao Liu; Hao Li; Yirui Wu; Qian Huang

doi:10.1117/12.3010618

1 December 2023 Analysis of airborne formation combat intent based on attention-based multi-level LSTM

Tianyu Ni, Yonghong Chen, Han Li, Gang Sun, Wenhao Liu, Hao Li, Yirui Wu, Qian Huang

Proceedings Volume 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023); 129400N (2023) https://doi.org/10.1117/12.3010618
Event: Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 2023, Sipsongpanna, China

Abstract

Air combat target tactical intent refers to the analysis and inference of the enemy's combat intentions in real-time, adversarial environments by extracting battlefield environmental information, static attributes, and real-time dynamic information of air combat targets, combined with knowledge from the military domain. To achieve this goal, many machine learning-based methods have been proposed to infer aircraft intentions. However, these methods are only applicable to individual aircraft and cannot predict the intentions of the entire formation. Therefore, we propose an attention-based multi-level LSTM model that incorporates multiple levels and attention mechanisms to enhance the focus on key information and improve prediction efficiency, resulting in promising experimental results.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tianyu Ni, Yonghong Chen, Han Li, Gang Sun, Wenhao Liu, Hao Li, Yirui Wu, and Qian Huang "Analysis of airborne formation combat intent based on attention-based multi-level LSTM", Proc. SPIE 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 129400N (1 December 2023); https://doi.org/10.1117/12.3010618

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