A 3D Localization Algorithm for Unmanned Aerial Vehicles in Distributed Air-Ground Integrated Sensing and Communication Networks

HUANG Yi; ZOU Ruizhuo; SHI Yunmei

doi:10.11999/JEIT241152

Volume 47 Issue 4

Apr. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(4): 1085-1092

HUANG Yi, ZOU Ruizhuo, SHI Yunmei. A 3D Localization Algorithm for Unmanned Aerial Vehicles in Distributed Air-Ground Integrated Sensing and Communication Networks[J]. Journal of Electronics & Information Technology, 2025, 47(4): 1085-1092. doi: 10.11999/JEIT241152

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HUANG Yi, ZOU Ruizhuo, SHI Yunmei. A 3D Localization Algorithm for Unmanned Aerial Vehicles in Distributed Air-Ground Integrated Sensing and Communication Networks[J]. Journal of Electronics & Information Technology, 2025, 47(4): 1085-1092. doi: 10.11999/JEIT241152

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A 3D Localization Algorithm for Unmanned Aerial Vehicles in Distributed Air-Ground Integrated Sensing and Communication Networks

doi: 10.11999/JEIT241152 cstr: 32379.14.JEIT241152

Department of Information and Communication Engineering, Tongji University, Shanghai 201804, China

Funds: The National Natural Science Foundation of China (62201391, 62101386)

Received Date: 2024-12-30
Rev Recd Date: 2025-04-15

Available Online: 2025-04-17

Publish Date: 2025-04-01

Abstract

Abstract

Objective The low-altitude economy, driven by the widespread adoption of drones and other unmanned aerial vehicles (UAVs), supports a range of applications across industries such as aerial imaging, precision agriculture, disaster response, and logistics. Precise Three-Dimensional (3D) positioning is essential to ensure the safety and efficiency of UAV operations in these scenarios. However, conventional cellular-based positioning approaches rely heavily on dedicated pilot signals, which impose significant overhead and limit 3D positioning accuracy. Integrated sensing and communication (ISAC) technology offers a promising alternative by enabling receivers to extract positioning information from reflected communication signals, thereby reducing dependence on pilot signals. Furthermore, a distributed network of ISAC transceivers can enhance sensing coverage and data diversity, improving localization performance. Building on these principles, this study proposes a 3D positioning algorithm based on distributed ISAC networks. The algorithm achieves high positioning accuracy without additional pilot signal overhead, demonstrating strong potential to support UAV applications within the low-altitude economy. Methods Motivated by the principles of distributed radar systems, this study proposes a cooperative 3D positioning method for UAVs within a distributed ISAC network comprising both ground base stations (BSs) and UAV-mounted BSs. Firstly, each ISAC receiver—whether ground-based or UAV-mounted—independently collects communication signals reflected by the target UAV from multiple ISAC transmitters. Secondly, each ISAC receiver serves as an edge computing node and derives a coarse estimate of the UAV’s 3D coordinates. Specifically, the receiver applies the MUltiple SIgnal Classification (MUSIC) algorithm to estimate the time delays of Orthogonal Frequency-Division Multiplexing (OFDM) signals refracted by the UAV. This is accomplished by exploiting the common delay steering vector structure across different ISAC transmitters. The resulting time-delay estimates are input into an ellipse-based positioning algorithm to obtain the initial 3D position of the UAV. By processing signals from at least three BSs, the UAV’s position can be triangulated via the intersection of ellipses. The coarse 3D position estimates are then transmitted to a central computing unit, where a weighted averaging method refines them to achieve higher accuracy. This hierarchical approach ensures robust localization performance, even when individual edge estimates are degraded due to noise, interference, or geometric limitations that prevent reliable ellipse-based estimation. To evaluate the proposed algorithm, the Cramér–Rao Lower Bound (CRLB) of the 3D positioning error is derived under the assumption of circularly symmetric complex Gaussian noise in the communication channel model. Results and Discussions The combined use of ground and aerial BSs yields lower position estimation errors compared to networks consisting solely of ground BSs (Fig. 2). This improvement arises from the additional orientation information provided by UAV-mounted BSs, which enhances the accuracy of height estimation and thereby improves overall 3D localization performance. In low Signal-to-Noise Ratio (SNR) conditions, positioning accuracy declines significantly due to the failure to distinguish the noise subspace from the signal subspace. This impairs the formation of spatial spectrum peaks, degrading the performance of the MUSIC algorithm (Fig. 3). Under such conditions, errors in time delay estimation become the primary source of positioning inaccuracy. At high SNR, despite reduced noise influence, the hyperbolic positioning algorithm may still converge to a local optimum due to suboptimal initial position selection, resulting in persistent estimation errors. In comparison, the proposed algorithm maintains superior performance across both low and high SNR regimes (Fig. 3). As the path gain increases, the Position Error Bound(PEB) of the estimation error decreases, indicating improved theoretical positioning accuracy(Fig. 4). Moreover, increasing the number of receiving stations significantly enhances localization performance. When SNR variation among BSs exceeds differences in geometric distribution, SNR-based weighted averaging yields better positioning results than Geometric Dilution of Precision (GDOP)-based averaging (Fig. 5). Conclusions Monte Carlo simulations confirm that the proposed algorithm achieves high-accuracy 3D positioning without requiring dedicated pilot signals. The results further indicate that UAV-mounted BSs, when functioning as ISAC transceivers and edge computing centers, can effectively support ground BSs in estimating the 3D position of target UAVs. Compared with positioning algorithms that use only ground BSs, this configuration notably improves altitude estimation accuracy.
- Integrated sensing and communication network,
- Air-ground integrated network,
- 3D localization,
- Distributed sensing

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References(18)

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