Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite Communications
Satellite communication systems, as a core component of global information infrastructure, have undergone unprecedented development. However, the open nature of satellite channels renders them vulnerable to electromagnetic interference, making anti-jamming techniques a persistent research focus in t...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3743 |
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| author | Xiya Sun Changqing Li Jiong Li Qi Su |
| author_facet | Xiya Sun Changqing Li Jiong Li Qi Su |
| author_sort | Xiya Sun |
| collection | DOAJ |
| description | Satellite communication systems, as a core component of global information infrastructure, have undergone unprecedented development. However, the open nature of satellite channels renders them vulnerable to electromagnetic interference, making anti-jamming techniques a persistent research focus in this domain. Satellite transponders contain various power-sensitive components that exhibit nonlinear characteristics under interference conditions, yet conventional anti-jamming approaches typically neglect the nonlinear distortion in transponders when suppressing interference. To address this challenge, this paper proposes a kernel-method-optimized FastICA algorithm (Kernel-FastICA) that establishes a post-nonlinear mixing model to precisely characterize signal transmission and reception processes. The algorithm transforms nonlinear separation tasks into high-dimensional, linear independent-component-analysis problems through kernel learning methodology. Furthermore, we introduce a regularized pre-whitening strategy to mitigate potential ill-conditioned issues arising from dimensional expansion, thereby enhancing numerical stability and separation performance. The simulation results demonstrate that the proposed algorithm exhibits superior robustness against interference and enhanced generalization capabilities in nonlinear jamming environments compared with existing solutions. |
| format | Article |
| id | doaj-art-b30a88b4ccca43a09c446f0c54fda48d |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b30a88b4ccca43a09c446f0c54fda48d2025-08-20T03:29:43ZengMDPI AGSensors1424-82202025-06-012512374310.3390/s25123743Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite CommunicationsXiya Sun0Changqing Li1Jiong Li2Qi Su3Graduate School, Space Engineering University, Beijing 101416, ChinaSchool of Space Information, Space Engineering University, Beijing 101416, ChinaSchool of Space Information, Space Engineering University, Beijing 101416, ChinaSchool of Space Information, Space Engineering University, Beijing 101416, ChinaSatellite communication systems, as a core component of global information infrastructure, have undergone unprecedented development. However, the open nature of satellite channels renders them vulnerable to electromagnetic interference, making anti-jamming techniques a persistent research focus in this domain. Satellite transponders contain various power-sensitive components that exhibit nonlinear characteristics under interference conditions, yet conventional anti-jamming approaches typically neglect the nonlinear distortion in transponders when suppressing interference. To address this challenge, this paper proposes a kernel-method-optimized FastICA algorithm (Kernel-FastICA) that establishes a post-nonlinear mixing model to precisely characterize signal transmission and reception processes. The algorithm transforms nonlinear separation tasks into high-dimensional, linear independent-component-analysis problems through kernel learning methodology. Furthermore, we introduce a regularized pre-whitening strategy to mitigate potential ill-conditioned issues arising from dimensional expansion, thereby enhancing numerical stability and separation performance. The simulation results demonstrate that the proposed algorithm exhibits superior robustness against interference and enhanced generalization capabilities in nonlinear jamming environments compared with existing solutions.https://www.mdpi.com/1424-8220/25/12/3743Kernel-FastICAnonlinear blind source separationsatellite communication anti-jamminginterference suppression |
| spellingShingle | Xiya Sun Changqing Li Jiong Li Qi Su Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite Communications Sensors Kernel-FastICA nonlinear blind source separation satellite communication anti-jamming interference suppression |
| title | Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite Communications |
| title_full | Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite Communications |
| title_fullStr | Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite Communications |
| title_full_unstemmed | Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite Communications |
| title_short | Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite Communications |
| title_sort | kernel fastica based nonlinear blind source separation for anti jamming satellite communications |
| topic | Kernel-FastICA nonlinear blind source separation satellite communication anti-jamming interference suppression |
| url | https://www.mdpi.com/1424-8220/25/12/3743 |
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