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CHEN Xiaolong, LIU Jia, WANG Xinghai, et al. Digital array radar lss-target detection dataset (LSS-DAUR-1.0) and graph network-based target classification[J]. Journal of Radars, in press. doi: 10.12000/JR25240
Citation: CHEN Xiaolong, LIU Jia, WANG Xinghai, et al. Digital array radar lss-target detection dataset (LSS-DAUR-1.0) and graph network-based target classification[J]. Journal of Radars, in press. doi: 10.12000/JR25240
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Digital Array Radar LSS-target Detection Dataset (LSS-DAUR-1.0) and Graph Network-based Target Classification

DOI: 10.12000/JR25240 CSTR: 32380.14.JR25240
  • 1.

    Naval Aviation University, Yantai 264001, China

  • 2.

    School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

Funds:  The National Natural Science Foundation of China (U25B2016), National Key Research and Development Program of China (2024YFB3909800), Shandong Natural Science Foundation (ZR2024JQ003)
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    Abstract
  • To address issues such as insufficient feature extraction, limited spatiotemporal correlation modeling, and poor classification performance in radar classification of low, slow, and small targets, this paper investigates on graph network-based feature extraction and classification methods. First, focusing on digital array ubiquitous radar, a radar detection dataset for LSS targets, named LSS-DAUR-1.0, is constructed; it contains Doppler and track data for six types of targets: passenger ships, speedboats, helicopters, rotor drones, birds, and fixed-wing drones. Second, based on this dataset, the multidomain and multidimensional characteristics of the targets are analyzed, and the complementarity between Doppler and physical motion features is verified through correlation and cosine similarity analyses. On this basis, a Graph Convolutional Network with Dynamic Graph Construction (DG-GCN) classification method fusing dual features is proposed. An adaptive window adjustment, a hybrid attenuation function, and a dynamic threshold mechanism are designed to construct an adaptive dynamic graph based on spatiotemporal correlation. Combined with graph convolution–based feature learning and classification modules, this approach achieves refined classification of low, slow, and small targets. Validation on the LSS-DAUR-1.0 dataset shows that the DG-GCN achieves 99.66% classification accuracy, which is 6.78% and 17.97% higher than that of ResNet and Transformer models, respectively. The total processing time is only 4.98 ms, which is more than 80% lower than that of the aforementioned comparison models. Hence, the DG-GCN achieves both high accuracy and efficiency. In addition, noise environment tests show good robustness. Ablation experiments verify that the dynamic edge weight mechanism compensates for the lack of spatial feature correlation in purely temporal connections and improves the model’s generalizability.

     

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