多基站协作的联合无人机定位与通信波束预测

Joint UAV Localization and Communication Beam Prediction with Cooperative Multibase Station

  • 摘要: 随着无人机应用的日益广泛,保障低空空域安全并为合作无人机提供可靠通信支持已成为一项紧迫挑战。传统无人机监测技术(如专用雷达、射频侦测及声学识别等)在部署成本与覆盖范围等方面存在固有局限。为此,通信感知一体化技术通过复用通信基础设施,为实现无人机监管提供了新途径。本文围绕低空无人机三维定位、速度估计以及动态通信链路优化问题,提出了一种基于多基站协作的联合无人机定位与通信波束预测算法。首先,在单基站信号处理层面,通过对高维接收信号张量进行平行因子(Parallel Factor, PARAFAC)分解,实现该基站观测视角下无人机距离、角度及多普勒频移等参数的初步估计;随后,在多基站数据融合层面,设计了基于空间邻近性判据的多目标关联算法。该算法结合无人机目标的三维空间位置一致性以及单基站张量分解固有的参数自动配对特性,实现了不同基站观测数据的关联融合,从而准确估计无人机的三维速度矢量;最后,在通信优化层面,构建了基于运动状态预测的动态波束形成机制,通过对无人机三维位置的连续预测实现动态波束的精准指向,优化基站与高速移动无人机间的通信链路质量。仿真结果表明,所提算法能实现对无人机目标的精确定位,并通过多基站协同动态波束形成显著提升通信性能。

     

    Abstract: The ever-expanding applications of unmanned aerial vehicles (UAVs) have made ensuring low-altitude airspace security and providing reliable communications support for cooperative UAVs a critical challenge. Traditional UAV surveillance technologies, such as radar, radio frequency, and acoustic monitoring, exhibit inherent limitations in deployment cost and coverage range. To address these challenges, integrated sensing and communication (ISAC) technology offers a new avenue for UAV surveillance, leveraging the advantage of reusing the existing communications infrastructure. This paper focused on the precise three-dimensional (3D) localization, velocity estimation, and dynamic communication link optimization of low-altitude UAVs. Specifically, a multibase station (BS) collaborative method was proposed for joint localization and communication beam prediction. First, at the single-BS signal processing level, we performed parallel factor decomposition on the high-dimensional received signal tensor to obtain preliminary estimates of UAV parameters, including range, angle, and Doppler shift. Then, to jointly utilize observational data from different BSs, a multitarget association strategy based on the spatial proximity criterion was designed. This strategy combined the consistency of the UAV target’s 3D position with the inherent automatic parameter pairing property of single-BS tensor decomposition, which enabled the association of velocity information from different BSs and the calculation of the 3D velocity vector of the UAV target. Finally, to optimize the communication link, a dynamic beamforming mechanism based on motion state prediction was proposed. This mechanism enabled precise beam steering by continuously predicting the 3D position of the UAV, thereby enhancing the communication link quality between the BS and high-speed UAV. Simulation results demonstrated that the proposed method achieved high UAV localization accuracy and significantly enhanced communication performance through multi-BS collaborative dynamic beamforming.

     

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