A Closed-loop Feedback Adaptive Beam Alignment Algorithm for Shipborne Low Earth Orbit Satellite Communication Terminals

CHEN Haotian; MA Zixian; XIE Xinhong; LI Nayu; LI Baozhu; SONG Chunyi; XU Zhiwei

doi:10.11999/JEIT251324

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CHEN Haotian, MA Zixian, XIE Xinhong, LI Nayu, LI Baozhu, SONG Chunyi, XU Zhiwei. A Closed-loop Feedback Adaptive Beam Alignment Algorithm for Shipborne Low Earth Orbit Satellite Communication Terminals[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251324

Citation:

CHEN Haotian, MA Zixian, XIE Xinhong, LI Nayu, LI Baozhu, SONG Chunyi, XU Zhiwei. A Closed-loop Feedback Adaptive Beam Alignment Algorithm for Shipborne Low Earth Orbit Satellite Communication Terminals[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251324

Citation:

CHEN Haotian, MA Zixian, XIE Xinhong, LI Nayu, LI Baozhu, SONG Chunyi, XU Zhiwei. A Closed-loop Feedback Adaptive Beam Alignment Algorithm for Shipborne Low Earth Orbit Satellite Communication Terminals[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251324

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A Closed-loop Feedback Adaptive Beam Alignment Algorithm for Shipborne Low Earth Orbit Satellite Communication Terminals

doi: 10.11999/JEIT251324 cstr: 32379.14.JEIT251324

CHEN Haotian^{1, 3},
MA Zixian^{1, 3},
XIE Xinhong²,
LI Nayu^{1, 2},
LI Baozhu²,
SONG Chunyi^{1, 2, 3
,
,},
XU Zhiwei^{1, 2, 3}

1.
The State Key Laboratory of Ocean Sensing and Ocean College, Zhejiang University, Zhoushan 316021, China
2.
Donghai Laboratory, Zhoushan 316021, China
3.
The Engineering Research Center of Oceanic Sensing Technology and Equipment, Ministry of Education, Zhoushan 316021, China

Funds: The Key R&D Program of Zhejiang Province (2024SSYS0095)

Received Date: 2025-12-15
Accepted Date: 2026-03-03
Rev Recd Date: 2026-02-28

Available Online: 2026-03-14

Abstract

Abstract

Objective The 6G-based SATellite COMmunication (SATCOM) network has become a primary solution for ubiquitous and oceanic communications. Compared with traditional Geostationary Earth Orbit (GEO) satellites, the latest generation of Low Earth Orbit (LEO) satellites offers higher throughput, lower end-to-end latency, and lower deployment cost. Phased arrays are therefore widely used in LEO SATCOM because of their beam agility. However, maritime wind-wave disturbances cause nonlinear relative motion between shipborne terminals and LEO satellites, which creates major challenges for high-precision satellite acquisition and tracking. To address this issue, a new beam alignment algorithm is required for LEO SATCOM systems. Such an algorithm should first obtain the instantaneous target state and motion characteristics through target acquisition, and then use a multi-target tracking method to predict satellite trajectories on the basis of the target states, thereby compensating for estimation errors caused by severe coupled motions. Methods The proposed closed-loop feedback adaptive beam alignment algorithm consists of two tightly coupled components: target acquisition and target state updating. In the target acquisition stage, a RAnk Reduction Estimator(RARE) is first used to decompose the array factor matrix and convert the original two-dimensional Direction Of Arrival(DOA) estimation problem into two sequential one-dimensional estimation problems. This process greatly reduces the computational complexity of each Sparse Bayesian Learning(SBL) iteration. On the basis of the coarse grid generated by RARE, an Adaptive Newton Sparse Bayesian Learning(ANSBL) method is developed. ANSBL uses block-sparse Bayesian learning to achieve initial target acquisition on the coarse grid, and then performs two-stage Newton refinement to reduce off-grid mismatch. This strategy provides high-accuracy DOA estimation in both $ \theta $ and $ \varphi $ and improves angular observation precision. In the target state updating stage, an Unscented Kalman Filter(UKF)-based ternary joint prediction mechanism is proposed. The UKF simultaneously predicts the target motion state, signal variance, and noise variance for the next target acquisition process. These predicted probability distributions are then used to update the initial grid and hyperparameters of the subsequent SBL acquisition stage, providing more consistent and comprehensive initial values. Through this closed-loop interaction, target acquisition and state tracking are deeply integrated, which substantially reduces the number of SBL iterations required for convergence. This advantage is particularly evident under high sea-state conditions, where reduced beam alignment time is critical. Results and Discussions The proposed closed-loop feedback adaptive beam alignment algorithm first uses on-grid DOA estimation to reduce array factor correlation and improve target acquisition efficiency, and then uses Newton iteration to achieve higher off-grid accuracy (Fig. 3). The proposed method is subsequently validated using real ship attitude data collected from a 28,000-DWT bulk carrier under actual sea conditions (Fig. 4). The UKF refines the DOA results through state updating. Its predictions of signal position, signal variance, and noise variance provide accurate initial values for the hyperparameters, thereby reducing the number of iterations and enabling faster convergence than other algorithms (Fig. 5). Under low sea-state conditions, the proposed method not only achieves satellite alignment in less than 0.2 s, but also reduces the satellite position estimation error from ±1°$ \sim $±0.5° (Fig. 6(a)). Under high sea-state conditions, the UKF effectively predicts satellite positions and reduces the satellite position estimation error from ±2.5°$ \sim $±0.65°, which verifies the robust tracking accuracy and error mitigation capability of the proposed method in harsh marine environments (Fig. 6(b)). Conclusions To meet the performance requirements of beam alignment algorithms for LEO communication satellites, this paper proposes a closed-loop feedback adaptive beam alignment algorithm. The algorithm first uses a block-based SBL algorithm to obtain grid-based DOA estimation results, and then achieves super-resolution direction estimation under off-grid conditions through adaptive Newton iteration. Through the UKF, the estimation results are dynamically calibrated in real time. The UKF further predicts the target motion state, signal variance, and noise variance for the next target acquisition process, thereby improving tracking continuity and alignment accuracy. Numerical simulations show that the proposed algorithm outperforms traditional beam alignment methods in both numerical accuracy and robustness, and effectively mitigates severe terminal shaking under complex sea conditions.
- LEO SATellite COMmunication (SATCOM),
- Direction Of Arrival (DOA),
- Multi-target tracking,
- Compressed sensing,
- Sparse Bayesian learning

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References(25)

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