Robust Anti-Jamming Method for Large-Array Radar Systems Using Deep Learning Based Null-Space Beamforming
With the increasing use of AI in radar signal processing, researchers have started combining minimum variance distortionless response (MVDR) with AI technology; however, the use of MVDR results in higher nonlinearity, making the learning process difficult. A combination of null-space beamforming (NS...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11036126/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850217663215697920 |
|---|---|
| author | Jaehyuk Lim Hogeun Yoo Euihyuk Lee Sunjin Oh Jaehoon Lee |
| author_facet | Jaehyuk Lim Hogeun Yoo Euihyuk Lee Sunjin Oh Jaehoon Lee |
| author_sort | Jaehyuk Lim |
| collection | DOAJ |
| description | With the increasing use of AI in radar signal processing, researchers have started combining minimum variance distortionless response (MVDR) with AI technology; however, the use of MVDR results in higher nonlinearity, making the learning process difficult. A combination of null-space beamforming (NSB) and AI technology may be a solution to this issue. However, inferring NSB weights requires inputting prior angle information, which necessitates an additional direction of arrival estimation, thereby increasing system complexity. To meet the requirements for applications in radar systems, we present a robust anti-jamming method for large-array radar systems using a deep neural network with NSB (DNN-NSB). The proposed method combines the computational simplicity of null-space beamforming with the adaptability of deep learning to effectively suppress interference and maintain a high signal-to-interference-plus-noise ratio (SINR). Unlike traditional methods, DNN-NSB eliminates the need for prior angle information, enabling efficient and scalable weight inference even in complex scenarios. The performance of DNN-NSB was validated through simulations across four scenarios by varying the number of interference sources, interference-to-signal ratio (ISR) conditions, and array sizes. The results showed that DNN-NSB consistently achieved near-optimum SINR within the training range and demonstrated superior performance compared to a convolutional neural network based on MVDR (CNN-MVDR) under multi-source interference conditions. To evaluate scalability, the model was further tested using a 32-element array, where it consistently achieved near-optimum interference suppression and maintained high spatial resolution. In conclusion, the study findings highlight the potential of DNN-NSB as a practical and effective solution for modern radar systems, particularly for applications that require large arrays and robust anti-jamming capabilities. |
| format | Article |
| id | doaj-art-ce5f4096540642ad96c1829009c09d08 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ce5f4096540642ad96c1829009c09d082025-08-20T02:07:59ZengIEEEIEEE Access2169-35362025-01-011310359910361210.1109/ACCESS.2025.357942211036126Robust Anti-Jamming Method for Large-Array Radar Systems Using Deep Learning Based Null-Space BeamformingJaehyuk Lim0https://orcid.org/0000-0002-3570-9533Hogeun Yoo1Euihyuk Lee2Sunjin Oh3Jaehoon Lee4https://orcid.org/0000-0001-7587-363XAgency for Defense Development, Daejeon, Republic of KoreaDepartment of Computer Science and Engineering, Korea University, Seoul, Republic of KoreaAgency for Defense Development, Daejeon, Republic of KoreaAgency for Defense Development, Daejeon, Republic of KoreaDepartment of Computer Science and Engineering, Korea University, Seoul, Republic of KoreaWith the increasing use of AI in radar signal processing, researchers have started combining minimum variance distortionless response (MVDR) with AI technology; however, the use of MVDR results in higher nonlinearity, making the learning process difficult. A combination of null-space beamforming (NSB) and AI technology may be a solution to this issue. However, inferring NSB weights requires inputting prior angle information, which necessitates an additional direction of arrival estimation, thereby increasing system complexity. To meet the requirements for applications in radar systems, we present a robust anti-jamming method for large-array radar systems using a deep neural network with NSB (DNN-NSB). The proposed method combines the computational simplicity of null-space beamforming with the adaptability of deep learning to effectively suppress interference and maintain a high signal-to-interference-plus-noise ratio (SINR). Unlike traditional methods, DNN-NSB eliminates the need for prior angle information, enabling efficient and scalable weight inference even in complex scenarios. The performance of DNN-NSB was validated through simulations across four scenarios by varying the number of interference sources, interference-to-signal ratio (ISR) conditions, and array sizes. The results showed that DNN-NSB consistently achieved near-optimum SINR within the training range and demonstrated superior performance compared to a convolutional neural network based on MVDR (CNN-MVDR) under multi-source interference conditions. To evaluate scalability, the model was further tested using a 32-element array, where it consistently achieved near-optimum interference suppression and maintained high spatial resolution. In conclusion, the study findings highlight the potential of DNN-NSB as a practical and effective solution for modern radar systems, particularly for applications that require large arrays and robust anti-jamming capabilities.https://ieeexplore.ieee.org/document/11036126/Anti-jammingnull-space beamforming (NSB)large-array radar systemsdeep learning |
| spellingShingle | Jaehyuk Lim Hogeun Yoo Euihyuk Lee Sunjin Oh Jaehoon Lee Robust Anti-Jamming Method for Large-Array Radar Systems Using Deep Learning Based Null-Space Beamforming IEEE Access Anti-jamming null-space beamforming (NSB) large-array radar systems deep learning |
| title | Robust Anti-Jamming Method for Large-Array Radar Systems Using Deep Learning Based Null-Space Beamforming |
| title_full | Robust Anti-Jamming Method for Large-Array Radar Systems Using Deep Learning Based Null-Space Beamforming |
| title_fullStr | Robust Anti-Jamming Method for Large-Array Radar Systems Using Deep Learning Based Null-Space Beamforming |
| title_full_unstemmed | Robust Anti-Jamming Method for Large-Array Radar Systems Using Deep Learning Based Null-Space Beamforming |
| title_short | Robust Anti-Jamming Method for Large-Array Radar Systems Using Deep Learning Based Null-Space Beamforming |
| title_sort | robust anti jamming method for large array radar systems using deep learning based null space beamforming |
| topic | Anti-jamming null-space beamforming (NSB) large-array radar systems deep learning |
| url | https://ieeexplore.ieee.org/document/11036126/ |
| work_keys_str_mv | AT jaehyuklim robustantijammingmethodforlargearrayradarsystemsusingdeeplearningbasednullspacebeamforming AT hogeunyoo robustantijammingmethodforlargearrayradarsystemsusingdeeplearningbasednullspacebeamforming AT euihyuklee robustantijammingmethodforlargearrayradarsystemsusingdeeplearningbasednullspacebeamforming AT sunjinoh robustantijammingmethodforlargearrayradarsystemsusingdeeplearningbasednullspacebeamforming AT jaehoonlee robustantijammingmethodforlargearrayradarsystemsusingdeeplearningbasednullspacebeamforming |