| Autor: |
Xuan Wang, Benzhou Jin, Mingyang Jia, Gang Wu, Xiaofei Zhang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 112074-112084 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3442371 |
| Popis: |
To address the issue of model performance degradation in combat intention recognition caused by the long-tailed distribution of battlefield data and the neglect of the spatial dimension information of multivariate time series data, this paper proposes a class balanced spatio-temporal self-attention (CBSTSA) model. By incorporating spatial and temporal attention mechanisms, the model captures interdependencies among features and extracts salient information from both temporal and spatial dimensions. Furthermore, taking the long-tailed distribution of battlefield data into account, a re-weighted class balanced loss function is introduced to train the model. Experimental results show the superiority of our CBSTSA model, e.g. achieving approximately 95.67% accuracy in typical scenarios, surpassing benchmark schemes by 4–5%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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