Convolutional Mixed Multi-Attention Encoder-Decoder Network for Radar Signal Sorting

CHANG Huaizhao; GU Yingyan; HAN Yunzhi; JIN Benzhou

doi:10.11999/JEIT251031

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CHANG Huaizhao, GU Yingyan, HAN Yunzhi, JIN Benzhou. Convolutional Mixed Multi-Attention Encoder-Decoder Network for Radar Signal Sorting[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251031

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CHANG Huaizhao, GU Yingyan, HAN Yunzhi, JIN Benzhou. Convolutional Mixed Multi-Attention Encoder-Decoder Network for Radar Signal Sorting[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251031

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Convolutional Mixed Multi-Attention Encoder-Decoder Network for Radar Signal Sorting

doi: 10.11999/JEIT251031 cstr: 32379.14.JEIT251031

1.
School of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
Jiangsu Automation Research Institute, Lianyungang, Jiangsu 222061, China

Funds: The National Natural Science Foundation of China (62371230)

Received Date: 2025-09-30
Accepted Date: 2025-12-29
Rev Recd Date: 2025-12-26

Available Online: 2026-01-05

Abstract

Abstract

Objective Radar signal sorting is a fundamental technology for electromagnetic environment awareness and electronic warfare systems. The objective of this study is to develop an effective radar signal sorting method that accurately separates intercepted pulse sequences and assigns them to different radiation sources in complex electromagnetic environments. With the increasing complexity of modern radar systems, intercepted pulse sequences are severely affected by pulse overlap, pulse loss, false pulses, and pulse arrival time measurement errors, which substantially reduce the performance of conventional sorting approaches. Therefore, a robust signal sorting framework that maintains high accuracy under non-ideal conditions is required. Methods Radar signal sorting in complex electromagnetic environments is formulated as a pulse-level time-series semantic segmentation problem, where each pulse is treated as the minimum processing unit and classified in an end-to-end manner. Under this formulation, sorting is achieved through unified sequence modeling and label prediction without explicit pulse subsequence extraction or iterative stripping procedures, which reduces error accumulation. To address this task, a convolutional mixed multi-attention encoder-decoder network is proposed (Fig. 1). The network consists of an encoder-decoder backbone, a local attention module, and a feature selection module. The encoder-decoder backbone adopts a symmetric structure with progressive downsampling and upsampling to aggregate contextual information while restoring pulse-level temporal resolution. Its core component is a dual-branch dilated bottleneck module (Fig. 2), in which a 1*1 temporal convolution is applied for channel projection. Two parallel dilated convolution branches with different dilation rates are then employed to construct multi-scale receptive fields, which enable simultaneous modeling of short-term local variations and long-term modulation patterns across multiple pulses and ensure robust temporal representation under pulse time shifts and missing pulses. To enhance long-range dependency modeling beyond convolutional operations, a local Transformer module is inserted between the encoder and the decoder. By applying local self-attention to temporally downsampled feature maps, temporal dependencies among pulses are captured with reduced computational complexity, whereas the influence of false and missing pulses is suppressed during feature aggregation. In addition, a feature selection module is integrated into skip connections to reduce feature redundancy and interference (Fig. 3). Through hybrid attention across temporal and channel dimensions, multi-level features are adaptively filtered and fused to emphasize discriminative information for radiation source identification. During training, focal loss is applied to alleviate class imbalance and improve the discrimination of difficult and boundary pulses. Results and Discussions Experimental results demonstrate that the proposed network achieves pulse-level fine-grained classification for radar signal sorting and outperforms mainstream baseline methods across various complex scenarios. Compared with existing approaches, an average sorting accuracy improvement of more than 6% is obtained under moderate interference conditions. In MultiFunctional Radar (MFR) overlapping scenarios, recall rates of 88.30%, 85.48%, 86.89%, and 86.48% are achieved for four different MFRs, respectively, with an overall average accuracy of 86.82%. For different pulse repetition interval modulation types, recall rates exceed 90% for fixed patterns and remain above 85% for jittered, staggered, and group-varying modes. In staggered and group-varying cases, performance improvements exceeding 3.5% relative to baseline methods are observed. Generalization experiments indicate that high accuracy is maintained under parameter distribution shifts of 5% and 15%, which demonstrates strong robustness to distribution perturbations (Fig. 8). Ablation studies confirm the effectiveness of each proposed module in improving overall performance (Table 7). Conclusions A convolutional mixed multi-attention encoder-decoder network is proposed for radar signal sorting in complex electromagnetic environments. By modeling radar signal sorting as a pulse-level time-series semantic segmentation task and integrating multi-scale dilated convolutions, local attention modeling, and adaptive feature selection, high sorting accuracy, robustness, and generalization capability are achieved under severe interference conditions. The experimental results indicate that the proposed approach provides an effective and practical solution for radar signal sorting in complex electromagnetic environments.
- Radar signal sorting,
- Convolutional mixed multi-attention,
- Encoder-decoder network

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References(21)

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