Autor: |
Zihan Wu, Jun Wang, Zhiquan Zhou |
Jazyk: |
angličtina |
Rok vydání: |
2024 |
Předmět: |
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Zdroj: |
Remote Sensing, Vol 16, Iss 21, p 3977 (2024) |
Druh dokumentu: |
article |
ISSN: |
2072-4292 |
DOI: |
10.3390/rs16213977 |
Popis: |
Addressing the issue of inadequate convergence and suboptimal accuracy in classical data-driven algorithms for coherent polarization–direction-of-arrival (DOA) estimation, a novel high-precision two-dimensional coherent polarization–DOA estimation method utilizing a sequence-embedding fusion (SEF) transformer is proposed for the first time. Drawing inspiration from natural language processing (NLP), this approach employs transformer-based multitasking text inference to facilitate joint estimation of polarization and DOA. This method leverages the multi-head self-attention mechanism of the transformer to effectively capture the multi-dimensional features within the spatial-polarization domain of the covariance matrix data. Additionally, an SEF module was proposed to fuse the spatial-polarization domain features from different dimensions. The module is a combination of a convolutional neural network (CNN) with local information extraction capabilities and a feature dimension transformation function, serving to improve the model’s ability to fuse information about features in the spatial-polarization domain. Moreover, to enhance the model’s expressive capacity, we designed a multi-task parallel output mode and a multi-task weighted loss function. Simulation results demonstrate that our method outperforms classical data-driven approaches in both accuracy and generalization, and the estimation accuracy of our method is improved relative to the traditional model-driven algorithm. |
Databáze: |
Directory of Open Access Journals |
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