可旋转阵列辅助的低空通信网络方向感知方法与性能分析

蒋金兵; 束锋; 郑惟海; 邓斌; 李茂林; 白家彤; 王艳; 江浩; 王江舟

doi:10.11999/JEIT260580

可旋转阵列辅助的低空通信网络方向感知方法与性能分析

doi: 10.11999/JEIT260580 cstr: 32379.14.JEIT260580

蒋金兵¹,
束锋^{1, 2, ,},
郑惟海³,
邓斌¹,
李茂林¹,
白家彤¹,
王艳¹,
江浩^{4, 5},
王江舟⁴

1.
海南大学信息与通信工程学院海口 570228
2.
南京理工大学电子工程与光电技术学院南京 210094
3.
中国移动通信集团海南有限公司海口 571250
4.
东南大学移动通信国家重点实验室南京 210096
5.
南京信息工程大学人工智能学院南京 210044

基金项目: 海南省自然科学基金(626ZD0993, 526QN0542, 626QN0553)，海南省科技专项资助(ZDYF2024GXJS292)，国家自然科学基金(U22A2002), 国家重点研发计划项目(2023YFF0612900)

详细信息

作者简介:
蒋金兵：女，博士生，研究方向为IRS辅助的通信系统、方向感知

束锋：男，教授，研究方向为智能无线通信、信息安全、大规模MIMO测向等

郑惟海：男，研究方向为电磁波传播及其应用、移动通信等

邓斌：男，硕士生，研究方向为大规模 MIMO、波达方向估计以及无线通信的机器学习

李茂林：男，博士生，研究方向为IRS辅助的无线通信

白家彤：女，博士生，研究方向为基于大规模/超大规模MIMO的智能感知

王艳：女，博士生，研究方向为IRS辅助的通信系统

江浩：男，副教授，研究方向为低空无人机通感算一体化理论与关键技术

王江舟：男，教授，中国工程院外籍院士，英国皇家工程院院士、IEEE Fellow、IET Fellow，研究方向为移动通信

通讯作者:
束锋　shufeng0101@163.com

中图分类号: TN911.7
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出版历程
- 修回日期: 2026-09-14
- 录用日期: 2026-09-14
- 网络出版日期: 2026-09-19

Rotatable-Antenna-Array-Enhanced Direction Sensing for Low-Altitude Communication Networks: Method and Performance Analysis

1.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China
2.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
3.
China Mobile Group Hainan Co., Ltd., Haikou 571250, China
4.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
5.
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: Hainan Provincial Natural Science Foundation of China (626ZD0993, 526QN0542, 626QN0553), Hainan Province Science and Technology Special Fund (ZDYF2024GXJS292), The National Natural Science Foundation of China (U22A2002), National Key Research and Development Program of China under Grant (2023YFF0612900)

摘要

摘要: 在实际的多天线接收机中，天线方向图通常具有各向异性。当信号入射方向大幅偏离阵列法线或靠近方向图零陷区域时，阵列的接收功率将严重衰减，导致传统固定阵列难以满足低空通信网络极端入射场景下的感知需求，而该问题在学术界和工业界尚未得到充分的探索。针对单个低空无人机的波达方向估计问题，本文构建了一种基于可旋转阵列的接收系统框架，并设定各阵元具有相同方向增益特性。随后推导了对应的克拉美罗下界(CRLB)。最后，为实现高精度的方向感知，提出了一种基于迭代旋转的求根多重信号分类(RR-Root-MUSIC)算法，并以推导的CRLB作为性能基准。在所考虑的理想仿真条件下，所提方法所需迭代次数较少，并具有较好的收敛稳定性；与固定阵列下的Root-MUSIC算法相比，所提方法的估计性能显著提升，且更逼近理想对准下的CRLB。进一步采用非理想Patch天线方向图的补充验证表明，阵列旋转改善大偏角观测条件并提高DOA估计性能的基本结论仍然成立。
- 波达方向估计 /
- 可旋转阵列 /
- 低空感知 /
- 克拉美罗下界
Abstract: Objective In practical multi-antenna receiving systems, antenna elements usually exhibit anisotropic radiation patterns. However, the impact of such pattern characteristics on direction sensing remains insufficiently explored in both academia and industry. Particularly in extreme scenarios, when the emitter direction significantly deviates from the array boresight or is close to a null of the antenna pattern, the received signal power at the array will be seriously attenuated. Consequently, traditional fixed antenna arrays struggle to meet the dynamic sensing and coverage demands of low-altitude networks. To address this issue, a rotatable antenna array system is developed by accounting for the directional radiation pattern of each antenna element. This research offers a solution for high-precision Direction of Arrival (DOA) estimation of Unmanned Aerial Vehicles (UAVs). Methods To achieve high-precision direction sensing, a Recursive Rotation Root-MUltiple SIgnal Classification (RR-Root-MUSIC) algorithm based on a rotatable array architecture is proposed. Specifically, the Root-MUSIC method is first utilized to obtain an initial DOA estimate of the target. Subsequently, the array is rotated toward this estimated direction, and the Root-MUSIC algorithm is applied once more to obtain an updated estimate. This sensing-rotation-resensing loop is repeated iteratively until the difference between adjacent estimates falls below a predefined convergence threshold. Furthermore, to evaluate the performance of the algorithm, the closed-form Cramér-Rao Lower Bound (CRLB) accounting for the directional antenna gain is derived as a theoretical benchmark. Results and Discussions To evaluate the proposed RR-Root-MUSIC algorithm, its sensing performance is compared with the derived CRLB under various Signal-to-Noise Ratios (SNRs) and incident angles. Simulation results demonstrate that the proposed method achieves a convergence probability of at least 99.8%, and the required number of iterations decreases as the SNR increases (Figs. 4-6). When the target direction significantly deviates from the array boresight, the proposed method effectively improves the subsequent observation conditions through iterative array rotation. Additional simulations using a non-ideal microstrip patch antenna pattern show that the proposed method maintains relatively stable estimation performance under antenna-pattern variations (Fig. 7). Along the considered UAV flight trajectories, the estimation error remains low and varies smoothly, indicating that array-orientation adjustment can mitigate the effect of UAV position variations on direction-sensing performance (Fig. 8). Conclusions This paper develops a rotatable antenna array system for direction sensing of a single UAV in low-altitude communication networks. Based on a sensing-rotation-resensing process, the proposed RR-Root-MUSIC method iteratively adjusts the array orientation according to the current DOA estimate, thereby improving the subsequent observation conditions. Simulation results show that the proposed method alleviates the DOA estimation performance degradation caused by directional-gain attenuation, especially when the target direction significantly deviates from the array boresight. Additional simulations using a non-ideal microstrip patch antenna pattern further verify the effectiveness of the proposed method under the considered non-ideal antenna pattern. The proposed method also exhibits stable convergence performance and maintains relatively low estimation errors along the considered UAV trajectories. These results indicate that rotatable antenna arrays provide a promising approach for low-altitude UAV direction sensing.
- Direction of Arrival (DOA) estimation /
- Rotatable antenna array /
- Low-altitude sensing /
- Cramér-Rao Lower Bound (CRLB)

HTML全文

图 1 低空通信网络的可旋转阵列系统

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图 2 可旋转阵列与信号源的几何关系示意图

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图 3 不同状态下天线增益示意图

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图 4 SNR = –10 dB 时所提RR-Root-MUSIC方法的收敛曲线

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图 5 SNR = 10 dB 时所提RR-Root-MUSIC方法的收敛曲线

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图 6 SNR = 30 dB 时所提RR-Root-MUSIC方法的收敛曲线

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图 7 不同天线方向图下固定阵列与RR-Root-MUSIC的RMSE随仰角变化曲线

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图 8 UAV绕基站的运动轨迹及所提RR-Root-MUSIC方法的RMSE随轨迹变化的曲线

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表 1 主要仿真参数设置

参数	取值	参数	取值
阵列规模$ M\times N $	$ 7\times 7 $	阵元间距$ {d}_{x}, {d}_{z} $	$ \lambda /2, \lambda /2 $
载波波长$ \lambda $	0.125 m	方向性因子p	2
快拍数K	100	最大迭代次数	30
固定方位角$ \phi $	90^o	收敛阈值$ \varepsilon $	0.1^o
噪声功率$ {\sigma }^{2} $	–100 dBm	阵元等效接收孔径A	$ {\lambda }^{2}/4\text{π} $

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