An Enhanced Algorithm Based on Dual-Input Feature Fusion ShuffleNet for Synthetic Aperture Radar Operating Mode Recognition
Synthetic aperture radar (SAR) operating mode recognition plays a crucial role in SAR countermeasures and serves as the foundation for effective SAR interference. To address the limitations of current SAR operating mode recognition algorithms, such as low recognition rates, poor generalization, and...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/9/1523 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850136526990606336 |
|---|---|
| author | Haiying Wang Wei Lu Yingying Wu Qunying Zhang Xiaojun Liu Guangyou Fang |
| author_facet | Haiying Wang Wei Lu Yingying Wu Qunying Zhang Xiaojun Liu Guangyou Fang |
| author_sort | Haiying Wang |
| collection | DOAJ |
| description | Synthetic aperture radar (SAR) operating mode recognition plays a crucial role in SAR countermeasures and serves as the foundation for effective SAR interference. To address the limitations of current SAR operating mode recognition algorithms, such as low recognition rates, poor generalization, and limited engineering applicability under low signal-to-noise ratio (SNR) conditions, an enhanced algorithm named dual-input feature fusion ShuffleNet (DIFF-ShuffleNet) based on intercepted SAR signal data is proposed. First, the SAR signal is processed by combining pulse compression and time–frequency analysis technology to enhance anti-noise robustness. Then, an improved lightweight ShuffleNet architecture is designed to fuse range pulse compression (RPC) maps and azimuth time–frequency features, significantly improving recognition accuracy in low-SNR environments while maintaining practical deployability. Moreover, an improved coarse-to-fine search fractional Fourier transform (CFS-FRFT) algorithm is proposed to address the chirp rate estimation required for RPC. Simulations demonstrate that the proposed SAR operating mode recognition algorithm achieves over 95.00% recognition accuracy for SAR operating modes (stripmap, spotlight, sliding spotlight, and scan) at an SNR greater than −8 dB. Finally, four sets of measured SAR data are used to validate the algorithm’s effectiveness, with all recognition results being correct, demonstrating the algorithm’s practical applicability. |
| format | Article |
| id | doaj-art-bcac9b43cf30480397a99ca748c022c4 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-bcac9b43cf30480397a99ca748c022c42025-08-20T02:31:08ZengMDPI AGRemote Sensing2072-42922025-04-01179152310.3390/rs17091523An Enhanced Algorithm Based on Dual-Input Feature Fusion ShuffleNet for Synthetic Aperture Radar Operating Mode RecognitionHaiying Wang0Wei Lu1Yingying Wu2Qunying Zhang3Xiaojun Liu4Guangyou Fang5Aerospace Imformation Research Institude, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Imformation Research Institude, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Imformation Research Institude, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Imformation Research Institude, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Imformation Research Institude, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Imformation Research Institude, Chinese Academy of Sciences, Beijing 100190, ChinaSynthetic aperture radar (SAR) operating mode recognition plays a crucial role in SAR countermeasures and serves as the foundation for effective SAR interference. To address the limitations of current SAR operating mode recognition algorithms, such as low recognition rates, poor generalization, and limited engineering applicability under low signal-to-noise ratio (SNR) conditions, an enhanced algorithm named dual-input feature fusion ShuffleNet (DIFF-ShuffleNet) based on intercepted SAR signal data is proposed. First, the SAR signal is processed by combining pulse compression and time–frequency analysis technology to enhance anti-noise robustness. Then, an improved lightweight ShuffleNet architecture is designed to fuse range pulse compression (RPC) maps and azimuth time–frequency features, significantly improving recognition accuracy in low-SNR environments while maintaining practical deployability. Moreover, an improved coarse-to-fine search fractional Fourier transform (CFS-FRFT) algorithm is proposed to address the chirp rate estimation required for RPC. Simulations demonstrate that the proposed SAR operating mode recognition algorithm achieves over 95.00% recognition accuracy for SAR operating modes (stripmap, spotlight, sliding spotlight, and scan) at an SNR greater than −8 dB. Finally, four sets of measured SAR data are used to validate the algorithm’s effectiveness, with all recognition results being correct, demonstrating the algorithm’s practical applicability.https://www.mdpi.com/2072-4292/17/9/1523synthetic aperture radaroperating mode recognitionfeature fusionparameter estimation |
| spellingShingle | Haiying Wang Wei Lu Yingying Wu Qunying Zhang Xiaojun Liu Guangyou Fang An Enhanced Algorithm Based on Dual-Input Feature Fusion ShuffleNet for Synthetic Aperture Radar Operating Mode Recognition Remote Sensing synthetic aperture radar operating mode recognition feature fusion parameter estimation |
| title | An Enhanced Algorithm Based on Dual-Input Feature Fusion ShuffleNet for Synthetic Aperture Radar Operating Mode Recognition |
| title_full | An Enhanced Algorithm Based on Dual-Input Feature Fusion ShuffleNet for Synthetic Aperture Radar Operating Mode Recognition |
| title_fullStr | An Enhanced Algorithm Based on Dual-Input Feature Fusion ShuffleNet for Synthetic Aperture Radar Operating Mode Recognition |
| title_full_unstemmed | An Enhanced Algorithm Based on Dual-Input Feature Fusion ShuffleNet for Synthetic Aperture Radar Operating Mode Recognition |
| title_short | An Enhanced Algorithm Based on Dual-Input Feature Fusion ShuffleNet for Synthetic Aperture Radar Operating Mode Recognition |
| title_sort | enhanced algorithm based on dual input feature fusion shufflenet for synthetic aperture radar operating mode recognition |
| topic | synthetic aperture radar operating mode recognition feature fusion parameter estimation |
| url | https://www.mdpi.com/2072-4292/17/9/1523 |
| work_keys_str_mv | AT haiyingwang anenhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition AT weilu anenhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition AT yingyingwu anenhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition AT qunyingzhang anenhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition AT xiaojunliu anenhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition AT guangyoufang anenhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition AT haiyingwang enhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition AT weilu enhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition AT yingyingwu enhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition AT qunyingzhang enhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition AT xiaojunliu enhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition AT guangyoufang enhancedalgorithmbasedondualinputfeaturefusionshufflenetforsyntheticapertureradaroperatingmoderecognition |