KDFE: Robust KNN-Driven Fusion Estimator for LEO-SoOP Under Multi-Beam Phased-Array Dynamics
Accurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals th...
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2025-07-01
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| author | Jiaqi Yin Ruidan Luo Xiao Chen Linhui Zhao Hong Yuan Guang Yang |
| author_facet | Jiaqi Yin Ruidan Luo Xiao Chen Linhui Zhao Hong Yuan Guang Yang |
| author_sort | Jiaqi Yin |
| collection | DOAJ |
| description | Accurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals that phased-array beamforming generates three-tiered SNR fluctuation patterns during unpredictable beam handovers, rendering conventional single-algorithm solutions fundamentally inadequate. To address this limitation, we propose KDFE (KNN-Driven Fusion Estimator)—an adaptive framework integrating the Rife–Vincent algorithm and MLE via intelligent switching. Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. This decision model overcomes the accuracy-robustness trade-off by matching algorithmic strengths to beam-specific dynamics, ensuring optimal performance during abrupt SNR transitions and high Doppler rates. Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. MLE), and 3D positioning accuracy improved by 13.6% (vs. Rife–Vincent) and 49.7% (vs. MLE). The study establishes a pioneering framework for adaptive LEO-SoOP positioning, delivering a methodological breakthrough for LEO navigation. |
| format | Article |
| id | doaj-art-6b44e36a7ad040028b58974a6fe6dea6 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-6b44e36a7ad040028b58974a6fe6dea62025-08-20T04:00:51ZengMDPI AGRemote Sensing2072-42922025-07-011715256510.3390/rs17152565KDFE: Robust KNN-Driven Fusion Estimator for LEO-SoOP Under Multi-Beam Phased-Array DynamicsJiaqi Yin0Ruidan Luo1Xiao Chen2Linhui Zhao3Hong Yuan4Guang Yang5Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100080, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100080, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100080, ChinaSchool of Land Science and Technology, China University of Geosciences Beijing, Beijing 100080, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100080, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100080, ChinaAccurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals that phased-array beamforming generates three-tiered SNR fluctuation patterns during unpredictable beam handovers, rendering conventional single-algorithm solutions fundamentally inadequate. To address this limitation, we propose KDFE (KNN-Driven Fusion Estimator)—an adaptive framework integrating the Rife–Vincent algorithm and MLE via intelligent switching. Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. This decision model overcomes the accuracy-robustness trade-off by matching algorithmic strengths to beam-specific dynamics, ensuring optimal performance during abrupt SNR transitions and high Doppler rates. Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. MLE), and 3D positioning accuracy improved by 13.6% (vs. Rife–Vincent) and 49.7% (vs. MLE). The study establishes a pioneering framework for adaptive LEO-SoOP positioning, delivering a methodological breakthrough for LEO navigation.https://www.mdpi.com/2072-4292/17/15/2565Low Earth OrbitDoppler frequency estimationsignal of opportunity processingIridium NEXTDoppler positioning |
| spellingShingle | Jiaqi Yin Ruidan Luo Xiao Chen Linhui Zhao Hong Yuan Guang Yang KDFE: Robust KNN-Driven Fusion Estimator for LEO-SoOP Under Multi-Beam Phased-Array Dynamics Remote Sensing Low Earth Orbit Doppler frequency estimation signal of opportunity processing Iridium NEXT Doppler positioning |
| title | KDFE: Robust KNN-Driven Fusion Estimator for LEO-SoOP Under Multi-Beam Phased-Array Dynamics |
| title_full | KDFE: Robust KNN-Driven Fusion Estimator for LEO-SoOP Under Multi-Beam Phased-Array Dynamics |
| title_fullStr | KDFE: Robust KNN-Driven Fusion Estimator for LEO-SoOP Under Multi-Beam Phased-Array Dynamics |
| title_full_unstemmed | KDFE: Robust KNN-Driven Fusion Estimator for LEO-SoOP Under Multi-Beam Phased-Array Dynamics |
| title_short | KDFE: Robust KNN-Driven Fusion Estimator for LEO-SoOP Under Multi-Beam Phased-Array Dynamics |
| title_sort | kdfe robust knn driven fusion estimator for leo soop under multi beam phased array dynamics |
| topic | Low Earth Orbit Doppler frequency estimation signal of opportunity processing Iridium NEXT Doppler positioning |
| url | https://www.mdpi.com/2072-4292/17/15/2565 |
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