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

JIANG Jinbing; SHU Feng; ZHENG Weihai; DENG Bin; LI Maolin; BAI Jiatong; WANG Yan; JIANG Hao; WANG Jiangzhou

doi:10.11999/JEIT260580

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JIANG Jinbing, SHU Feng, ZHENG Weihai, DENG Bin, LI Maolin, BAI Jiatong, WANG Yan, JIANG Hao, WANG Jiangzhou. Rotatable-Antenna-Array-Enhanced Direction Sensing for Low-Altitude Communication Networks: Method and Performance Analysis[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260580

Citation:

JIANG Jinbing, SHU Feng, ZHENG Weihai, DENG Bin, LI Maolin, BAI Jiatong, WANG Yan, JIANG Hao, WANG Jiangzhou. Rotatable-Antenna-Array-Enhanced Direction Sensing for Low-Altitude Communication Networks: Method and Performance Analysis[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260580

Citation:

JIANG Jinbing, SHU Feng, ZHENG Weihai, DENG Bin, LI Maolin, BAI Jiatong, WANG Yan, JIANG Hao, WANG Jiangzhou. Rotatable-Antenna-Array-Enhanced Direction Sensing for Low-Altitude Communication Networks: Method and Performance Analysis[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260580

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

doi: 10.11999/JEIT260580 cstr: 32379.14.JEIT260580

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)

Accepted Date: 2026-09-14
Rev Recd Date: 2026-09-14

Available Online: 2026-09-19

Abstract

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)

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References(21)

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