基于智能体交互的无线电地图构建与非合作辐射源定位

周陬; 邓添荣; 仇琛; 仇洪冰

doi:10.12263/DZXB.20251269

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基于智能体交互的无线电地图构建与非合作辐射源定位

更新时间：2026-06-15

- 基于智能体交互的无线电地图构建与非合作辐射源定位
- Radio Map Construction and Non-cooperative Radiation Source Localization Based on Agents Interaction
- 电子学报 2026年页码：1-14
- 作者机构：
  
  1.桂林电子科技大学认知无线电与信息处理省部共建教育部重点实验室，广西桂林 541004
  2.广西人工智能学院，广西南宁 530201
  3.鹏城实验室宽带通信部，广东深圳 518055
- 作者简介：
  
  周陬男，1983年3月出生于湖北省松滋市。现为桂林电子科技大学教授、博士生导师。主要研究方向为多模态感知与语义认知通信、电磁频谱具身智能、智能体互联网、无人集群/低轨卫星通感一体化、物联网与大数据技术应用等。中国电子学会会员编号为E19006051M。E-mail: zhouzou@guet.edu.cn
  邓添荣男，2001年2月出生于广东省东莞市。现为桂林电子科技大学硕士研究生。主要研究方向为电磁频谱空间认知学习的无线电地图构建。E-mail: bitzhzzz@163.com
  仇琛男，1990年11月出生于江苏省如皋市。现任鹏城实验室助理研究员。主要研究方向为无人机辅助无线网络、通感一体化、语义通信技术。E-mail: qiuch@pcl.a.cn
  仇洪冰男，1963年2月出生于江苏省如皋市。现为桂林电子科技大学信息与通信学院教授、博士生导师。主要研究方向为宽带无线通信、通信信号处理、辐射源定位等。中国电子学会会员编号为E190013474M。E-mail: qiuhb@guet.edu.cn
- 基金信息：
  
  国家自然科学基金(62101293);广西自然科学基金(2023GXNSFAA026058);认知无线电与信息处理省部共建教育部重点实验室主任基金(CRKL220110)
- DOI：10.12263/DZXB.20251269
  中图分类号： TN92;TP18
- 收稿：2026-02-19，
  
  录用：2026-03-19，
  
  网络首发：2026-06-15，
- 稿件说明：
移动端阅览
周陬, 邓添荣, 仇琛, 等. 基于智能体交互的无线电地图构建与非合作辐射源定位[J/OL]. 电子学报, 2026,1-14.

ZHOU Zou, DENG Tianrong, QIU Chen, et al. Radio Map Construction and Non-cooperative Radiation Source Localization Based on Agents Interaction[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-14.
周陬, 邓添荣, 仇琛, 等. 基于智能体交互的无线电地图构建与非合作辐射源定位[J/OL]. 电子学报, 2026,1-14. DOI： 10.12263/DZXB.20251269.

ZHOU Zou, DENG Tianrong, QIU Chen, et al. Radio Map Construction and Non-cooperative Radiation Source Localization Based on Agents Interaction[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-14. DOI： 10.12263/DZXB.20251269.

摘要

无线电地图构建与非合作辐射源定位是电磁环境精确认知与动态管控的核心任务，二者高度耦合、相互制约。本文借鉴认知学习与具身智能理论，提出基于智能体协同交互、智能内生的无线电地图构建与辐射源定位双任务统一架构，实现感知-认知-行为的反馈闭环。首先利用众包概率共识思想，结合高斯回归过程与二维位置编码，将多智能体的离散稀疏采样数据转换为包含连续信号场的预测分布、几何拓扑及辐射源目标空间位置存在概率估计的空间注意力图，建立多智能体关于辐射源目标的共识感知先验；在此基础上，构建多智能体统一语义空间中的辐射源目标一致性联合表征，利用多头自注意力机制设计任务驱动和目标驱动的智能体交互策略，以同时完成无线电地图构建和非合作辐射源定位双任务；基于近端策略优化强化学习方法，设计双任务信息交互反馈策略，实现自组织与自学习，以形成感知、认知与交互决策的具身行为闭环。仿真结果验证了所提方案的可行性：在5%稀疏采样条件下，本系统对无线电地图构建与辐射源定位双任务进行协同优化，相较于独立执行单任务，无线电地图构建与辐射源定位的精度分别提升18.2%和43.5%。其中，无线电地图构建精度（RMSE）相比克里金算法和全卷积深度补全自动编码器分别提升了60.95%和32.55%；辐射源定位精度相较于传统的RSS定位基准算法，定位误差降低了65.7%，扩展到10 km × 10 km的典型定位场景时，辐射源定位任务仍能稳定维持在1.2%以内的相对精度。

Abstract

Radio map construction and non-cooperative emitter localization are pivotal tasks for accurate perception and dynamic control of the electromagnetic environment

characterized by high coupling and mutual constraints. Drawing on cognitive learning and embodied intelligence theories

this paper proposes a unified dual-task framework for radio map construction and emitter localization featuring multi-agent collaborative interaction and endogenous intelligence

thereby establishing a "perception-cognition-action" feedback loop. First

leveraging the concept of crowdsourced probabilistic consensus

we integrate Gaussian Process Regression (GPR) with 2D-positional encoding to transform discrete sparse sampling data from multiple agent into spatial attention maps. These maps encompass continuous signal field predictive distributions

geometric topologies

and probability estimates of emitter spatial presence

establishing a consensus perceptual prior regarding the target emitters. Building upon this

a consistent joint representation for emitters is constructed within a unified multi-agent semantic space. A multi-head self-attention mechanism is then utilized to design task-driven and target-driven agent interaction strategies

enabling the simultaneous execution of both tasks. Furthermore

a dual-task information interaction feedback strategy based on Proximal Policy Optimization (PPO) is designed to achieve self-organization and self-learning

forming an embodied behavioral loop of perception

cognition

and interactive decision-making. Simulation results validate the feasibility and superiority of the proposed scheme. Under a 5% sparse sampling condition

the synergistic optimization of both tasks achieves accuracy improvements of 18.2% and 43.5% for radio map construction and emitter localization

respectively

compared to independent task execution. Specifically

the RMSE of radio map construction improves by 60.95% and 32.55% over Kriging and Full Convolutional Auto-Encoder

respectively. Meanwhile

the localization error is reduced by 65.7% compared to the RSS-based baseline

maintaining a stable relative accuracy within 1.2% even when extended to a 10 km×10 km large-scale scenario.

关键词

Keywords

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