Nov 30, 2023 · The recognition performance of 1DCNN for ballistic targets is effectively improved by using layer-wise auxiliary classifiers to fuse the outputs ...
Nov 19, 2023 · Ballistic missile defense systems require accurate target recognition technology. Effective feature extraction is crucial for this purpose.
Group-Fusion One-Dimensional Convolutional Neural Network for Ballistic Target High-Resolution Range Profile Recognition with Layer-Wise Auxiliary Classifiers.
To address the issue of computational complexity in HRRP recognition based on the standard one-dimensional CNN (1DCNN), we propose a lightweight network called ...
In this paper, we propose QDSFCNN as a solution to address the issues of high model complexity and limited-sample ballistic target recognition in standard CNN.
Group-Fusion One-Dimensional Convolutional Neural Network for Ballistic Target High-Resolution Range Profile Recognition with Layer-Wise Auxiliary Classifiers.
Group-Fusion One-Dimensional Convolutional Neural Network for Ballistic Target High-Resolution Range Profile Recognition with Layer-Wise Auxiliary Classifiers.
Group-Fusion One-Dimensional Convolutional Neural Network for Ballistic Target High-Resolution Range Profile Recognition with Layer-Wise Auxiliary Classifiers.
Group-Fusion One-Dimensional Convolutional Neural Network for Ballistic Target High-Resolution Range Profile Recognition with Layer-Wise Auxiliary Classifiers.
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This paper proposes a method based on feature fusion to recognize ballistic targets. The proposed method takes two types of data as input.