面向复杂环境的无人机通感一体化多阶段轨迹与资源联合优化

许文轩; 吴子君; 张海君; 马旭; 张晓奇

doi:10.12263/DZXB.20260176

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面向复杂环境的无人机通感一体化多阶段轨迹与资源联合优化

面向低空经济的通信感知一体化技术 | 更新时间：2026-10-16

- 面向复杂环境的无人机通感一体化多阶段轨迹与资源联合优化
- Multi-Stage Trajectory and Resource Joint Optimization for UAV-Enabled ISAC in Complex Environments
- 电子学报 2026年54卷第6期页码：2598-2608
- 作者机构：
  
  1.北京科技大学智能与通信融合北京市重点实验室，北京 100083
  2.北京科技大学河北省空天地智能通信重点实验室，北京 100083
- 作者简介：
  
  许文轩女，2003年1月出生于河北省保定市。现为北京科技大学计算机与通信工程学院硕士研究生。主要研究方向为无人机通感一体化、无线资源分配等。中国电子学会会员编号：E190201410M。E-mail: xuwenxuan@xs.ustb.edu.cn
  吴子君女，1999年1月出生于浙江省金华市。现为北京科技大学计算机与通信工程学院博士研究生。主要研究方向为6G无线网络、移动边缘智能、低空网络。中国电子学会会员编号：E190187257A。E-mail: wuzijun@xs.ustb.edu.cn
  张海君男，1986年10月出生于辽宁省朝阳市。现为北京科技大学人工智能学院教授、博士生导师。主要研究方向为智能通信、无人机通信、卫星通信。中国电子学会会员编号：E190020833S。E-mail: zhanghaijun@ustb.edu.cn
  马旭男，1996年7月出生于山东省菏泽市。现为北京科技大学计算机与通信工程学院博士研究生。主要研究方向为6G移动通信、空天地一体化网络。中国电子学会会员编号：E190186298A。E-mail: maxu@xs.ustb.edu.cn
  张晓奇女，1993年3月出生于河南省洛阳市。现为北京科技大学人工智能学院副教授。主要研究方向为智能超表面、通感一体化、无人机通信。中国电子学会会员编号：E190203572M。E-mail: zhangxiaoqi@ustb.edu.cn
- 基金信息：
  
  国家自然科学基金(62225103;U22B2003;U2441227;62561160097;62541102;U24A20211);北京市自然科学基金(L253003;L241008);中央高校基本科研业务费(FRF-BD-25-053)
- DOI：10.12263/DZXB.20260176
  中图分类号： TN929.532;
- 收稿：2026-02-28，
  
  录用：2026-03-31，
  
  纸质出版：2026-06-25
- 稿件说明：
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许文轩, 吴子君, 张海君, 等. 面向复杂环境的无人机通感一体化多阶段轨迹与资源联合优化[J]. 电子学报, 2026, 54(06): 2598-2608.

XU Wenxuan, WU Zijun, ZHANG Haijun, et al. Multi-Stage Trajectory and Resource Joint Optimization for UAV-Enabled ISAC in Complex Environments[J]. Acta Electronica Sinica, 2026, 54(06): 2598-2608.
许文轩, 吴子君, 张海君, 等. 面向复杂环境的无人机通感一体化多阶段轨迹与资源联合优化[J]. 电子学报, 2026, 54(06): 2598-2608. DOI：10.12263/DZXB.20260176

XU Wenxuan, WU Zijun, ZHANG Haijun, et al. Multi-Stage Trajectory and Resource Joint Optimization for UAV-Enabled ISAC in Complex Environments[J]. Acta Electronica Sinica, 2026, 54(06): 2598-2608. DOI：10.12263/DZXB.20260176

摘要

随着低空经济的蓬勃发展，无人机（Unmanned Aerial Vehicle，UAV）作为低空网络的核心节点，其通信感知一体化能力对于实现全天候泛在连接与环境重构至关重要。然而，在建筑物密集的复杂城市环境中，低空UAV面临视距链路频繁遮挡与信道质量剧烈波动的严峻挑战，传统基于理想自由空间模型的轨迹规划难以适配实际应用需求，亟需设计能够主动规避遮挡并动态适应环境变化的智能规划方法。针对这一问题，本文提出一种面向复杂环境的基于概率视距信道模型的多阶段轨迹与资源联合优化方法。首先，构建依赖仰角的概率视距模型以量化城市峡谷遮挡效应，将轨迹规划导向无线链路可靠性最优。其次，设计多阶段规划框架，通过分阶段更新目标状态估计动态调整飞行策略，以应对环境不确定性与状态估计误差。在此基础上，考虑运动学与能量约束，构建最大化预期加权通信速率与有效感知费希尔信息矩阵的联合优化问题，在通信与感知性能之间实现自适应权衡。针对问题中概率指数项引发的强非凸性，提出一种基于块坐标下降与逐次凸逼近的迭代算法。仿真结果表明，该方法在高密度城市场景下能引导无人机主动规避盲区，保持链路稳定与感知精度。与传统方案相比，系统通信总容量提升约80.8%，目标定位误差降低约81.4%，显著增强了低空通感网络的鲁棒性与效能。

Abstract

With the rapid development of the low-altitude economy

unmanned aerial vehicles (UAVs)

as core nodes in low-altitude networks

play a critical role in enabling ubiquitous connectivity and environmental reconstruction through integrated sensing and communication (ISAC) capabilities. However

in complex urban environments characterized by dense building layouts

low-altitude UAVs face severe challenges such as frequent line-of-sight (LoS) link blockages and drastic fluctuations in channel quality. Traditional trajectory planning methods based on ideal free-space models struggle to meet practical application requirements

necessitating the design of intelligent planning approaches capable of proactively avoiding blockages and dynamically adapting to environmental variations. To address this issue

this paper proposes a multi-stage trajectory and resource joint optimization method based on a probabilistic LoS channel model for complex environments. First

an elevation-dependent probabilistic LoS model is constructed to quantify the canyon effect in urban areas

steering trajectory planning toward the optimization of wireless link reliability. Second

a multi-stage planning framework is designed to dynamically adjust flight strategies through stage-wise updates of target state estimation

thereby addressing environmental uncertainty and state estimation errors. On this basis

considering kinematic and energy constraints

a joint optimization problem is formulated to maximize the expected weighted communication rate and the effective Fisher information matrix for sensing

achieving an adaptive trade-off between communication and sensing performance. To tackle the strong non-convexity induced by the probabilistic exponential terms

an iterative algorithm based on block coordinate descent and successive convex approximation is developed. Simulation results demonstrate that the proposed method effectively guides UAVs to actively avoid blind zones in high-density urban scenarios while maintaining stable links and high sensing accuracy. Compared with conventional approaches

the proposed method achieves an approximately 80.8% improvement in total communication capacity and an 81.4% reduction in target localization error

significantly enhancing the robustness and effectiveness of low-altitude ISAC networks.

关键词

Keywords

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