面向未知环境机动辐射源的单无人机具身追踪方法

梁宏韬; 付杨美子; 万奕尧; 刘晓东; 吴启晖

doi:10.12263/DZXB.20260273

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面向未知环境机动辐射源的单无人机具身追踪方法

更新时间：2026-06-09

- 面向未知环境机动辐射源的单无人机具身追踪方法
- An Embodied Intelligence-Based Method for Single-UAV Tracking of Maneuvering Radiation Sources in Unknown Environments
- 电子学报 2026年页码：1-15
- 作者机构：
  
  1.南京航空航天大学电子信息工程学院，江苏南京 211106
  2.南昌大学信息工程学院，江西南昌 330031
- 作者简介：
  
  [ "梁宏韬男，2000年10月出生于甘肃省白银市。现为南京航空航天大学电子信息工程学院博士研究生。主要研究方向为低空具身智能、多模态融合与电磁频谱具身智能。E-mail: ceie.lht@nuaa.edu.cn" ]
  [ "付杨美子女，2004年11月出生于江西省南昌市。现为南昌大学信息工程学院本科生，主要研究方向为频谱信号分析。E-mail： 6105122008@email.ncu.edu.cn" ]
  [ "万奕尧男，1997年9月出生于湖北省宜昌市。现为南京航空航天大学博士研究生。研究方向：深度学习、多模态融合。E-mail： yiyaowan@nuaa.edu.cn" ]
  [ "刘晓东男，1993年12月出生于江西省赣州市。现为南昌大学信息工程学院讲师。主要研究方向为大模型赋能电磁频谱管理，可见光通信感知照明一体化系统。E-mail： xiaodongliu@ncu.edu.cn" ]
  [ "吴启晖男，1970年12月出生于安徽省黄山市。现为南京航空航天大学特聘教授、副校长，教育部“长江学者”特聘教授，南京航空航天大学电子信息工程学院教授、博导。主要研究方向为认知科学与应用、电磁空间频谱认知智能管控、无人机认知集群。E-mail： wuqihui@nuaa.edu.cn" ]
- 基金信息：
  
  国家重点研发计划(2023YFB2904500);长三角科技创新共同体联合攻关(2024CSJZN00300)
- DOI：10.12263/DZXB.20260273
  中图分类号： TN929.5;TN911.7
- 收稿：2026-04-17，
  
  录用：2026-05-15，
  
  网络首发：2026-06-09，
- 稿件说明：
移动端阅览
梁宏韬, 付杨美子, 万奕尧, 等. 面向未知环境机动辐射源的单无人机具身追踪方法[J/OL]. 电子学报, 2026,1-15.

LIANG Hongtao, FU Yangmeizi, WAN Yiyao, et al. An Embodied Intelligence-Based Method for Single-UAV Tracking of Maneuvering Radiation Sources in Unknown Environments[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-15.
梁宏韬, 付杨美子, 万奕尧, 等. 面向未知环境机动辐射源的单无人机具身追踪方法[J/OL]. 电子学报, 2026,1-15. DOI： 10.12263/DZXB.20260273.

LIANG Hongtao, FU Yangmeizi, WAN Yiyao, et al. An Embodied Intelligence-Based Method for Single-UAV Tracking of Maneuvering Radiation Sources in Unknown Environments[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-15. DOI： 10.12263/DZXB.20260273.

摘要

在电子侦察、频谱监测与应急搜救等任务中，单架无人机仅凭接收信号强度指示（Received Signal Strength Indicator， RSSI）对非合作机动辐射源进行被动追踪具有重要的战略与工程意义。然而，现有RSSI定位方法高度依赖环境传播参数先验，且将参数估计、状态滤波与路径规划串行割裂，难以适应以发射功率与路径损耗指数为代表的核心传播参数未知且目标持续机动的动态场景；同时，单通道标量RSSI固有的低空间分辨力进一步加剧了探索与利用之间的结构性矛盾。为此，本文提出一种基于具身智能的单无人机闭环追踪方法，以结构化不确定度指标作为跨层信息流的统一载体，将感知、认知与决策通过不确定度的双向传递进行深度耦合，形成以闭环信息流驱动的协同追踪架构。在感知层，构建参数先验推断网络，从历史时空观测序列中提取传播参数的经验分布，为后续贝叶斯推断提供数据驱动的初始化依据。在认知层，针对未知传播参数与机动状态的联合估计难题，设计基于Rao-Blackwellized条件分解的双链解耦推断机制，外层粒子滤波在线估计大尺度衰落参数，内层自适应交互多模型滤波器精确推断目标机动状态，并引入有效样本量监测下的粒子复苏与信念继承机制，避免参数重采样对状态追踪连续性的破坏。在决策层，为缓解上述探索-利用矛盾，构建融合信息增益与模型预测控制的分层规划策略，并引入可学习的排序门控网络对追踪、探测与保守等候选策略进行实时效用评估，使无人机依据信念不确定度自适应地从广域探索转换为紧密伴飞。仿真结果表明，所提方法在传播参数完全未知的冷启动条件下各项核心指标均优于所对比的基线方法，且在无人工标定先验模式下的追踪性能接近乃至在部分指标上超越若干基线在参数已知条件下的理想表现，验证了所提架构在恶劣感知条件下的自适应能力与鲁棒性。

Abstract

Passive tracking of uncooperative maneuvering radiation sources by a single unmanned aerial vehicle (UAV) using only received signal strength indicator (RSSI) measurements is of considerable strategic and engineering value for missions such as electronic reconnaissance

spectrum monitoring

and emergency search and rescue. However

existing RSSI localization methods rely heavily on environmental propagation priors and treat parameter estimation

state filtering

and path planning as isolated serial processes

leaving them unable to handle dynamic scenarios in which core propagation parameters such as transmit power and path loss exponent are unknown and the target keeps maneuvering. The inherent low spatial resolution of scalar RSSI from a single channel further intensifies the structural conflict between exploration and exploitation. To address these challenges

this paper proposes an embodied intelligence-based closed-loop tracking method for a single UAV

which adopts structured uncertainty metrics as a unified carrier of cross-layer information flow and deeply couples perception

cognition

and decision-making through bidirectional propagation of uncertainty

forming a cooperative tracking architecture driven by closed-loop information flow. At the perception layer

a parameter prior inference network is built to extract the empirical distribution of propagation parameters from historical spatio-temporal observation sequences

providing data-driven initialization for the subsequent Bayesian inference. At the cognition layer

a dual-chain decoupled inference mechanism based on Rao-Blackwellized conditional decomposition is designed for the joint estimation of unknown propagation parameters and maneuvering states

in which the outer particle filter estimates large-scale fading parameters online while the inner adaptive interacting multiple model filter accurately infers maneuvering states of the target. A particle revival mechanism guided by effective sample size monitoring is further introduced together with a belief inheritance mechanism to prevent parameter resampling from disrupting the continuity of state tracking. At the decision layer

to mitigate the exploration-exploitation conflict noted above

a hierarchical planning strategy fusing information gain with model predictive control is constructed

and a learnable ranking gating network is introduced to perform real-time utility evaluation over candidate strategies including tracking

probing

and conservative options

allowing the UAV to adaptively transition from broad-area exploration to tight escort flying according to belief uncertainty. Simulation results show that the proposed method outperforms the compared baseline approaches across all core metrics under cold-start conditions with completely unknown propagation parameters

and the tracking performance under the mode without manual calibration priors approaches and in certain metrics surpasses the ideal performance of several baselines operating with known parameters

which verifies the adaptability and robustness of the proposed architecture under degraded sensing conditions.

关键词

Keywords

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